The rapid evolution of 6G networks and AI-generated content (AIGC) is reshaping service provisioning at the wireless edge, where low-latency and computation-intensive tasks must be efficiently supported. Integrating AIGC with 6G Internet of Things (IoT) ecosystems offers significant benefits, enabling real-time analytics, IoT data augmentation and synthesis, personalized services, and intelligent resource coordination across heterogeneous devices. However, provisioning AIGC services at the mobile edge poses formidable challenges: massive data heterogeneity, stringent delay requirements, and the inherent limitations of learning-based offloading methods, such as high training cost, unstable convergence, and weak transparency. Inspired by the Quantum Approximate Optimization Algorithm (QAOA), we propose a hybrid optimization framework that combines classical wireless bandwidth pre-allocation for IoT devices connected to mobile edge computing (MEC) servers with Quadratic Unconstrained Binary Optimization (QUBO)-based AIGC server selection. This hybrid classical-quantum design ensures stable results and enables efficient exploration of combinatorial allocation spaces. We further implement the quantum circuits and evaluate the performance of our proposed approach. Simulation results show that our method achieves lower processing latency and greater stability than conventional reinforcement learning and heuristic baselines in small- to medium-scale IoT deployments. While current quantum hardware scalability remains a constraint, the framework points toward a promising pathway for large-scale AIGC offloading as quantum technology matures.

With the widespread deployment of wireless sensor networks (WSNs), energy replenishment and data collection of wireless sensor nodes (SNs) have emerged as vital research challenges. In existing studies, on one hand, autonomous aerial vehicles (AAVs) are employed to facilitate wireless charging for SNs within clusters, yet the fairness of energy replenishment among these SNs has been overlooked. On the other hand, as a novel and flexible data collection method, AAVs equipped with active reconfigurable intelligent surfaces (RISs) can reduce hardware requirements and signal processing complexity on the AAV side; however, the additional energy consumption introduced by active RIS operation is generally not considered. Therefore, in this work, we focus on utilizing AAV-mounted active RIS in conjunction with a non-orthogonal multiple access (NOMA) scheme to enable green (renewable) energy far-field wireless charging and data relaying for WSNs. We define the AAV’s service as powering SNs to reach their target energy thresholds and collecting the SNs’ data via the active RIS. We develop models for active RIS-aided data relaying and for determining the optimal reflection coefficients. We then formulate an optimization problem to maximize the number of SNs that can be served by the AAV. Given the NP-hard nature of the problem, we further propose a two-step solution with corresponding heuristic algorithms to efficiently solve it. Finally, extensive simulation results demonstrate the superior performance of the proposed solution.

Sixth-generation (6G) communication emphasizes the deep integration of sensing, communication, and computing to support intelligent and rapid-response networks. Autonomous aerial vehicles (AAVs), known for their superior flexibility, terrain adaptability, and low deployment costs, are promising candidates for data collection in large-scale wireless sensor networks (WSNs). However, many existing AAVs-assisted data collection studies assume that AAVs operate at relatively low speeds and incorporate hovering time during data collection. In such scenarios, the AAVs inevitably require longer flying durations and consume much energy. Additionally, they often neglect the impact of the Doppler effect during the data collection. In most general and realistic scenarios, AAVs typically fly at high speeds without the need to hover, and the Doppler effect highly impacts the communication between sensor nodes (SNs) and AAVs. To address this, we propose a data collection framework that leverages orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access (NOMA) to mitigate the Doppler-induced interference in the up-link. We formulate an AAV-assisted data collection efficiency maximization problem by jointly considering AAV energy consumption, and the SNs’ uploading rates and bit error rates (BERs). Given the NP-hard nature of this problem, we design a three-step solution: the first two steps employ heuristic algorithms and the third step integrates a bi-directional long short-term memory (BiLSTM) for intelligent AAV symbol detection. Simulation results validate the superiority of our proposed solution.

This paper presents an innovative framework that synergistically integrates energy-based generative models with morphological attention networks for robust hyperspectral image classification. Despite recent advances in data-driven approaches, existing methods struggle to model complex data distributions while preserving geometric invariance and maintaining stable representations under limited supervision. To address these challenges, we propose a novel architecture that combines three key components: (1) a morphological attention module that fuses learned structural elements with multi-head attention through parallel dilation and erosion pathways, (2) an uncertainty-aware energy-based learning paradigm that employs adaptive margin constraints and Langevin dynamics sampling, and (3) a multi-scale feature extraction block with parallel convolutional branches for dynamic spectral-spatial feature integration. Extensive experimental validation on benchmark datasets demonstrates our framework’s superior ability over state-of-the-art methods.

The proliferation of immersive applications such as Virtual, Augmented, and Mixed Reality (VR/AR/MR) imposes stringent low-latency and reliability requirements that challenge conventional O-RAN slicing mechanisms. Existing frameworks often fail to anticipate rapid XR traffic fluctuations driven by user motion and gaze dynamics, leading to inefficient resource utilization and SLA violations. To overcome these limitations, we propose a cross-layer intelligent control framework that integrates traffic prediction and reinforcement learning-based slice orchestration across the Non-RT and Near-RT RIC. By coupling long-term foresight with short-term adaptability, the proposed design enables proactive, SLA-aware scheduling under highly dynamic conditions. We further develop a trace-driven network emulator to reproduce realistic 5G behaviors and validate system robustness. Extensive experiments demonstrate that our framework consistently achieves over 95% SLA compliance, below 2% latency violations, and up to 30% latency reduction compared with state-of-the-art baselines, confirming its effectiveness and scalability for next-generation immersive networks.

5G networks promise to transform our technology experience by delivering ultra-high speeds and low latency, enabling applications like Augmented Reality (AR) and Connected Autonomous Vehicles (CAVs). However, 5G’s higher frequencies reduce its range and lead to performance inconsistencies, especially for users on the move. Moreover, the energy consumption of 5G is significantly higher than its predecessor, 4G, raising sustainability concerns. In this paper, we explore a solution that combines the strengths of Integrated Sensing and Communication (ISAC) with the advanced analytics capabilities of the Network Data Analytics Function (NWDAF) in 5G networks. We leverage two new functions, Sensing Service Function (SSF) and Energy Efficiency Control Function (EECF), designed to work together to make smarter, more energy-efficient network decisions. By optimizing base station downlink transmit power, our approach not only reduces energy consumption but also carefully balances the trade-offs between latency and energy efficiency. Our findings suggest a promising path toward a greener and more reliable future for 5G and beyond networks.

Intelligent Transportation Systems (ITS) rely on high-precision road networks, which are particularly scarce in indoor parking. Existing methods depend on expensive hardware (e.g., LiDAR) or manual mapping, both of which are costly and inefficient. The rise of the Internet of Things (IoT) has enabled large-scale data collection and connectivity, offering new opportunities for automated road network extraction. OpenStreetMap (OSM), as a crowdsourced IoT-driven platform, provides multi-layer geospatial data, including the Road Network Layer (RNL), Lane Boundary Layer (LBL), and Turn Sign Layer (TSL). However, OSM data often suffers from incompleteness and noisy connectivity, affecting the continuity and accuracy of road networks. This paper introduces OSM2Net, a novel framework designed to extract road networks from individual layers and leverage multi-layer data to construct directed road networks. Specifically, OSM2Net rasterizes noisy OSM data into bitmaps for image processing and multi-layer fusion. By leveraging the topology relationship between lane boundaries and road networks, a Lane-Road Map Generator (LRMG) creates a simulated dataset for training. Then, utilizing the simulated dataset, a Lane2Net model is designed to extract road networks from sparse lane boundary images. The framework then vectorizes bitmaps into a lightweight, undirected road network and refines it into a directed network by extracting and matching turn sign information. Experimental results show that Lane2Net achieves Intersection over Union (IoU) of 93% and 92% using simulated and real-world datasets, respectively. Extensive experiments on real-world datasets confirm that OSM2Net delivers robust completeness and high-quality road network extraction.

As Internet of Things (IoT) networks continue to expand rapidly, remote IoT devices (IoTDs) face significant challenges related to energy supply and data collection. On the one hand, limited battery capacities hinder the long-term, intervention-free operation of IoTDs. On the other hand, the Age of Information (AoI) is a crucial metric for evaluating data freshness, and delays in data collection reduce its value. A promising solution to these challenges is the use of autonomous aerial vehicles (AAVs) to facilitate green energy far-field wireless charging and data collection. Although extensive research has been conducted on scenarios where AAVs operate at fixed altitudes, in many real-world applications, most AAVs operate in 3-D space. In this work, we focus on a 3-D scenario where AAVs first wirelessly charge IoTDs and then collect data. We investigate the 3-D trajectories of multiple AAVs, considering varying altitudes and velocities, and introduce models for AAV-based wireless charging and data collection. We then formulate a multi-AAV wireless charging efficiency maximization problem, taking into account the IoTDs’ average AoI. Given the NP-hard nature of this problem, we propose the joint charging and data collection (JCDC) algorithm, which aims to ensure data timeliness while replenishing as many IoTDs as possible. Finally, extensive simulations are conducted to validate the performance of the proposed JCDC algorithm.

Non-line-of-sight (NLOS) identification is the key technique to improve the accuracy of the channel impulse response (CIR) based ultrawideband (UWB) positioning system. However, most existing NLOS identification approaches are tailored to static environments and often encounter difficulties in dynamic settings with both temporal and spatial variations, particularly when dealing with limited and hard samples. This paper introduces a hard sample meta-learning (HSML) approach to address the issues of NLOS identification across different scenarios and domains. HSML includes two phases: a hard sample meta-training phase and a fine-grained meta-testing phase. During the meta-training phase, we train a two-loop learning network using CIR from multiple scenarios (tasks). The inner loop focuses on learning task-specific features, while the outer loop captures cross-task generalization properties using a cross-entropy loss. Hard samples are identified based on estimated residuals for each task, and a new dataset is created, consisting of both hard samples and samples with small residuals. To improve the robustness against hard samples, we implement a residual-corrected focal loss, which is used to retrain the network on this new dataset. In the fine-grained meta-testing phase, we apply a filtering mechanism based on the tendency of estimated residuals during fine-tuning. This mitigates the risk of poor performance caused by anomalous samples. We validate the effectiveness and robustness of the proposed HSML method using two datasets containing multiple real-world scenarios. Our experimental results demonstrate that HSML outperforms existing models in terms of identification accuracy, robustness and generalization performance.

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime.

Sixth generation (6G) communications empower billions of Internet of Things devices (IoTDs), bringing significant convenience to modern life. However, the challenge of powering a burgeoning number of IoTDs has become a critical concern. Radio frequency (RF) wireless charging technology is a promising solution to this issue; however, its adoption has been limited due to its relatively low charging efficiency. Beamforming facilitated by antenna arrays can enhance the efficiency of RF wireless charging. In addition, renewable (green) energy can act as the energy source for RF wireless chargers. In this work, we integrate beamforming technique with green energy empowered RF wireless charging to develop a three-dimensional charging model for a green charger (GC). Furthermore, when investigating the multi-GC to multi-IoTD wireless charging scenario, we derive an accurate model to quantify the accumulation of wireless energy in the charging area. Based on this, we formulate the multi-GC to multi-IoTD charging efficiency maximization problem to enhance the energy received by IoTDs within a given charging period. Since this optimization problem is proved to be NP-hard, we propose the Efficient multi-beAm coopeRative chargiNg (EARN) algorithm to solve it efficiently. EARN coordinates multiple charging beams by intelligently assigning proper compensating phases to each GC, effectively utilizing the energy transmitted by GCs in all directions to strengthen the wireless charging effect. Ultimately, extensive simulations validate the performance of the EARN algorithm in conspicuously improving the wireless charging efficiency.

Recently, the growing of deep space explorations has attracted notable interests on interplanetary network (IPN), which is the key infrastructure for communications across vast distances in the solar system. However, the unique characteristics of IPN pose numerous unexplored challenges for interplanetary data transfers (IP-DTs), i.e., the challenges that existing schemes developed for Earth-based networks are ill-equipped to handle. To address these challenges, we first propose a novel distributed algorithm that leverages the Lyapunov optimization to jointly optimize the routing, scheduling and rate control of IP-DTs at each node. Specifically, our proposal adaptively optimizes the data-rate and bundle scheduling at each output port of a node, significantly improving the end-to-end (E2E) latency and delivery ratio of IP-DTs under a long-term energy constraint. Then, we further explore the heterogeneity of IPN to introduce limited state information exchange among nodes, and devise mechanisms for generating and disseminating state messages to facilitate timely adjustments of routing and scheduling schemes in response to unexpected link disruptions and traffic surges. Simulations verify the advantages of our proposal over the state-of-the-arts.

Real-time transmission of large-scale data, high computational demands, and resource limitations on edge devices pose significant challenges for intelligent sports systems. The proliferation of Internet of Things (IoT) technologies has catalyzed the rise of Smart Sport, where wearable sensors, cameras, and intelligent algorithms are integrated to revolutionize athletic training. Despite this transformation, access to professional coaching remains constrained by factors such as time, cost, and scalability. A critical limitation of existing remote coaching approaches is their inability to perform effective spatio-temporal analysis, hindering comprehensive evaluation and refinement of athletic performance. This paper introduces MRCoach, a mixed reality-based, immersive, and interactive sports coaching system that enables data-driven training without requiring in-person supervision. MRCoach reconstructs 3D volumetric avatars of both learners and expert athletes, allowing users to visualize and compare their movements side-by-side in a mixed reality environment for intuitive skill refinement. To ensure responsive and efficient feedback, we propose an adaptive semantic transmission strategy that prioritizes sport-relevant joints, thereby reducing latency and bandwidth requirements without sacrificing accuracy. Furthermore, a 3D sports analysis framework is developed to evaluate motion based on normalized joint positions, velocities, and accelerations. This framework computes real-time similarity scores and delivers actionable guidance to learners. Experimental results across four sports—tennis, soccer, basketball, and baseball—demonstrate MRCoach’s effectiveness in providing personalized, real-time training experiences. Compared to state-of-the-art baselines such as MagicStream, ExPose, and PIXIE, MRCoach achieves significantly lower end-to-end latency (79.9 ms) and higher frame rates (≥ 56 FPS), while maintaining accurate pose tracking and high avatar fidelity.

Efficient trajectory planning in Autonomous Valet Parking (AVP) remains challenging due to multiple vehicle interactions and environmental complexities. Existing single-agent Reinforcement Learning (RL) approaches face challenges in balancing complexity, convergence, and knowledge efficiency, often resulting in increased collisions and longer travel times. To address these issues, this paper proposes a Bio-inspired Evolutionary Reinforcement Learning (BERL) framework for multi-vehicle collaborative trajectory planning, where each vehicle is modeled as a Fusion Architecture for Learning and Cognition Network (FALCON) agent based on Adaptive Resonance Theory (ART). The BERL framework comprises three core modules: 1)Meme Reinforcement Learning (MRL), which enables agents to learn independently and adapt to changing environments; 2)Expert-Guided Evolutionary Learning (EGEL), which facilitates knowledge transfer from expert agents to less experienced ones, enhancing coordination; and 3)Integrated Forgetting and Memory Optimization (IFMO), which optimizes memory use and reduces algorithm complexity. Additionally, the BERL framework supports model and sensor quality heterogeneity in the multi-vehicle trajectory planning scenario. Finally, we build an AVP Simulation (AVPS) platform to validate the performance of the proposed framework. Comprehensive simulation results demonstrate that the BERL framework improves success rate and parking efficiency by at least 15.7% and 16.7%, respectively, as compared to state-of-the-art algorithms. Additionally, the proposed IFMO module reduces the number of memes in the FALCON agent by 30.2% while maintaining stable performance.

By integrating the software-defined networking (SDN) architecture with the machine learning-based knowledge plane, knowledge-defined networking (KDN) is revolutionizing established traffic engineering (TE) methodologies. This paper investigates the challenging joint routing and caching problem in KDN-based networks, managing multiple traffic flows to improve long-term quality-of-service (QoS) performance. This challenge is formulated as a computationally expensive non-convex mixed-integer non-linear programming (MINLP) problem, which exceeds the capacity of heuristic methods to achieve near-optimal solutions. To address this issue, we present DRL-JRC, an actor-critic deep reinforcement learning (DRL) algorithm for adaptive joint routing and caching in KDN-based networks. DRL-JRC orchestrates the optimization of multiple QoS metrics, including end-to-end delay, packet loss rate, load balancing index, and hop count. During offline training, DRL-JRC employs proximal policy optimization (PPO) to smooth the policy optimization process. In addition, the learned policy can be seamlessly integrated with conventional caching solutions during online execution. Extensive experiments demonstrate the comprehensive superiority of DRL-JRC over baseline methods in various scenarios. Meanwhile, DRL-JRC consistently outperforms the heuristic baseline under partial policy deployment during execution. Compared to the average performance of the baseline methods, DRL-JRC reduces the end-to-end delay by 51.14% and the packet loss rate by 40.78%.

Advanced Air Mobility (AAM) seeks to establish a next-generation air transportation system by leveraging autonomous unpiloted aerial vehicles (UAVs) to transport passengers and cargo between locations previously underserved or unserved by traditional aviation. Achieving AAM at scale requires overcoming significant challenges in airspace management, classification, and traffic control to safely accommodate the increasing volume of UAV operations. This paper presents a comprehensive design for air corridors to facilitate efficient aerial transport and formulates a multi-UAV coordination problem within these corridors. The objective is to enable each UAV to autonomously make control decisions based on local observations gathered from onboard sensors. This decentralized control approach is modeled as a multi-agent partially observable Markov decision process (POMDP), aiming at minimizing UAV travel time while ensuring adherence to corridor boundaries and collision avoidance. To address the complexities posed by varying state dimensions and types, we propose a novel Hybrid Transformer-based Multi-agent Reinforcement Learning (HTransRL) architecture. HTransRL integrates a customized transformer model into an actor-critic network, effectively processing both sequential and non-sequential observed states of varying sizes while capturing their correlations. This enables safe and efficient UAV navigation. Simulation results show that in test environments similar to or simpler than training scenarios, HTransRL achieves a successful arrival rate exceeding 90% in worst-case test scenarios. In test environments more complex than training scenarios, HTransRL demonstrates superior scalability compared to two baseline methods, achieving higher arrival rates and comparable travel times.

With the rapid development of Internet of Things (IoT), wireless rechargeable sensor networks (WRSNs) have found widespread applications in modern society. However, due to the dynamic energy consumption of sensor nodes (SNs) and the challenges associated with battery replacement, ensuring a timely energy supply to SNs is a pressing concern. Currently, employing unmanned aerial vehicles (UAVs) for far-field wireless charging emerges as a promising solution to charge SNs. However, with the expansion of WRSNs, existing solutions face challenges in meeting the energy demands of large-scale WRSNs. Therefore, we propose to leverage multiple green energy powered UAVs to facilitate far-field wireless charging for large-scale WRSNs. We first segment the SNs into clusters and propose the dynamic energy consumption models for UAVs and SNs. Then, we formulate the UAVs' charging service efficiency maximization problem, enabling more SNs to receive sufficient energy replenishment. As this problem is proved to be an NP-hard problem, we further propose the dyNamic wIreless chaRging (NIR) algorithm to efficiently determine the charging sequences of the SNs and reduce the UAVs' energy consumption. Extensive simulations have validated that NIR notably improves the charging service efficiency of the UAVs, thereby prolonging the WRSN's lifespan.

Due to the proliferation of devices and the availability of computing servers, mobile edge computing (MEC) has gained popularity in executing various computational tasks. MEC offers computing services at the network’s edge, providing user equipment (UE) with reduced latency for their applications. However, determining a suitable offloading policy for UEs in MEC, considering wireless resource allocation and power management, is computationally demanding. Additionally, the problem is NP-hard, making it challenging to find an optimal solution within a reasonable time frame. In this work, we propose a meta-reinforcement learning (MRL) based computational task offloading and power control mechanism for UEs in a resource-constrained environment of a MEC network to tackle the NP-hardness of the problem. We first develop an optimization problem to maximize UE computation efficiency by minimizing their power consumption for local computing and uplink transmission of UEs in the MEC network. We propose to use both binary offloading (full offloading or full local computing) and partial offloading schemes in the system. Our proposed MRL algorithm can figure out a suitable offloading policy for UEs within a short time. Unlike the traditional deep-reinforcement learning algorithms, our approach can resolve the issue of obtaining proper solutions in a new environment. Extensive simulation results prove the feasibility of our proposed work.

In the realm of modern wireless networks, the integration of wireless sensing and communication systems is pivotal, especially in the context of the forthcoming 6G Internet of Things (IoT) paradigm. The popularity of integrated sensing and communication (ISAC) stems from its potential to amplify the utilization of existing network infrastructures. This study introduces an innovative fusion of joint communication and sensing (JCAS), a framework of ISAC, combined with end-to-end (E2E) network slicing (NS) techniques. The aim is to meet user Quality-of-Service (QoS) expectations within the ambit of 6G IoT applications. The prime objective is to optimize resource allocation for communication and sensing services within a bespoke network slice tailored for 6G IoT scenarios. This is achieved by minimizing E2E system latency, essential for real-time decision making in 6G IoT environments. The optimization challenge is tackled by using deep reinforcement learning (DRL) in the form of a deep$Q$network (DQN) algorithm, which is adept at addressing nonlinear integer programming (NLIP) issues intrinsic to 6G IoT settings. Comprehensive simulations validate the approach, demonstrating its effectiveness in the context of 6G IoT networks. The amalgamation of ISAC with E2E NS emerges as a potent strategy for furnishing enhanced services customized for 6G IoT applications, successfully fulfilling consumers’ QoS requisites. This integrated approach holds substantial promise as a robust solution for addressing the exacting demands of forthcoming wireless networks, particularly those underpinned by the strides in 6G IoT technologies.

Sixth-generation (6G) wireless communications greatly emphasizes the integration of sensing, communicating, and computing. Unmanned aerial vehicles (UAVs), by leveraging their feasibility and mobility, can naturally facilitate flexible far-field wireless charging and data backhauling for widely implemented wireless rechargeable sensor networks (WRSNs) across diverse domains, such as intelligent agriculture, smart cities, and modern factories. However, the energy constraints inherent to UAVs, coupled with the absence of joint optimization in clustering and trajectory design, present formidable challenges in efficiently leveraging UAVs for large-scale WRSN wireless charging and data backhauling. Therefore, in this work, we empower the green energy-powered base station (GBS) to power a UAV by laser charger to prolong the UAV’s uptime. This enables the UAV to effectively perform wireless charging and data backhauling for a WRSN. By considering the GBS’s green energy budget, we formulate an optimization problem focused on determining the optimal 3-D hovering points for UAV to maximize the number of sensor nodes (SNs) capable of receiving sufficient energy and uploading data. Given the NP-hard nature of this problem, we propose a two-step solution featuring corresponding heuristic algorithms designed to efficiently address it. Extensive simulations have been conducted to validate the efficacy of our proposed algorithms.

The heavy burden of fingerprint collection and annotation has become one of the biggest bottlenecks in wireless indoor positioning, particularly in the context of the Internet of Things (IoT). Fortunately, crowdsourcing can be leveraged to alleviate the fingerprint collection burden by harnessing the collective intelligence of crowdsourcing users. However, it is rather difficult to acquire an accurate positioning model based solely on training unlabeled crowdsourcing data. To overcome this problem, we propose a novel positioning model called ENvironment Aware Positioning (ENAP), utilizing unlabeled crowdsourcing trace data. The proposed ENAP mainly consists of three steps, i.e., transforming the unlabeled crowdsourcing trace data into a cluster space, mapping the cluster space into the positioning space, and continuously updates the positioning model in an unsupervised manner. To enhance the performance and robustness against device heterogeneity of crowdsourcing users, we propose a novel clustering scheme for space transformation by adaptively fusing multiple signal features. Then, to ensure long-term positioning stability and continual environmental aware capability, we incorporate a dynamic replay memory into ENAP that enables the unsupervised online updating of positioning models, distinguishing our proposal from most existing positioning models. Simulation and experimental results demonstrate the effectiveness and superiority of the proposed ENAP approach as a practical and efficient solution for wireless indoor positioning in the IoT era.

Metaverse, a hypothetical digital environment linking the cyber world and the physical world, is expected to revolutionize the way people interact. In the metaverse, people interact with objects, the environment, and each other through digital representations of themselves or avatars across time and space. For example, in the metaverse, people can have meetings with colleagues hundreds of miles away. They can also walk through the aisles of a store, find the best fit and have it delivered to their doorstep. It is also possible to simulate the optimal process manufacturing line to adjust for product variation and minimize bottlenecks, or test an innovative aircraft wing design without building expensive prototypes.

Metaverse, a hypothetical digital environment linking the cyber world and the physical world, is expected to revolutionize the way people interact. In the metaverse, people interact with objects, the environment, and each other through digital representations of themselves or avatars across time and space. For example, in the metaverse, people can have meetings with colleagues hundreds of miles away. They can also walk through the aisles of a store, find the best fit, and have it delivered to their doorstep. It is also possible to simulate the optimal process manufacturing line to adjust for product variation and minimize bottlenecks, or test an innovative aircraft wing design without building expensive prototypes.

The recent advances of deep learning in various mobile and Internet-of-Things applications, coupled with the emergence of edge computing, have led to a strong trend of performing deep learning inference on the edge servers located physically close to the end devices. This trend presents the challenge of how to meet the quality-of-service requirements of inference tasks at the resource-constrained network edge, especially under variable or even bursty inference workloads. Solutions to this challenge have not yet been reported in the related literature. In the present paper, we tackle this challenge by means of workload-adaptive inference request scheduling: in different workload states, via adaptive inference request scheduling policies, different models with diverse model sizes can play different roles to maintain high-quality inference services. To implement this idea, we propose a request scheduling framework for general-purpose edge inference serving systems. Theoretically, we prove that, in our framework, the problem of optimizing the inference request scheduling policies can be formulated as a Markov decision process (MDP). To tackle such an MDP, we use reinforcement learning and propose a policy optimization approach. Through extensive experiments, we empirically demonstrate the effectiveness of our framework in the challenging practical case where the MDP is partially observable.

An end-to-end (E2E) network slicing (NS) scheme for heterogeneous network (HetNet) is proposed in which the number of slices and instances of various network functions (NFs) are optimized contingent on the number of users (UEs) and their quality of service (QoS) requirements. The objective of the scheme is to empower future generation networks by considering control signaling in the control plane as well as the UE traffic in the user plane of the core network (CN). We formulate a joinT UE assOciation, wiReless bandwidth allocation, sliCe formation, slice assignment, virtual network function (VNF) placement, computing resource allocation, link assignment, and link bandwidtH allocation (TORCH) problem to minimize the E2E task completion time of all UEs while considering both control signaling and UEs’ traffic. Since TORCH is a mixed-integer nonlinear problem, to tackle the problem, we decompose it into two sub-problems: the link assignment problem and the UE Association, resource allocation, Slice formation, Slice AssIgnment, and VNF pLacement (ASSAIL) problem. The ASSAIL problem comprises both the core network (CN) and radio access network (RAN), and they do not compete for resources, so we decompose it into two sub-problems: the RAN problem and the CN problem. We use Dijkstra’s algorithm and a deep Q-learning network (DQN) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the TORCH problem.

The worldwide rapid development of the Internet of Things (IoT) has led to an exaggerated scale and explosive increase of IoT devices (IoTDs). However, owing to embedded capacity limited batteries, most IoTDs have relatively short lifespans, which is a crucial factor of enervating their IoT applications. Far-field wireless charging is a means to extend IoTDs' lifespans. However, many far-field wireless charging scheduling strategies overlook the work load imbalances among different charging base stations, thus leading to low charging throughput (CTP) and prolonged waiting times for IoTDs. This adversely affects the IoTDs wireless charging network performance. Based on the M/G/1 queuing model, in this work, we first analyze the wireless charging workload of a charging base station and then aim to maximize CTP in the network by jointly considering the charging base stations' wireless charging workloads and the IoTDs' non-preemptive charging priorities. As the CTP maximization problem is NP-hard, we further propose the woRkload bAlancing and non-Preemptive TempOral pRiority (RAPTOR) algorithm to efficiently solve this problem. Finally, extensive simulations have validated the performance of RAPTOR in improving the CTP of the whole network.

Machine learning models, especially neural network (NN) classifiers, have shown tremendous potential of being used in complex tasks such as image classification, object detection and video analytics. However, to be adopted in the real-world applications, there are still problems to be answered. One of these problems is that training machine learning models, especially NN models, requires a certain level of computation and data processing. Other problems are the limited bandwidth of the network and the possibility of exposing the privacy of the users to attacks if the training data (specially video) is going to be transferred through the network. To mitigate these problems, researchers recently proposed the concept of federated learning.In this paper, we build a video analytic application for traffic management and train it using federated learning. More specifically, each traffic surveillance camera combined with its co-located small PC are seen as the worker node in federated learning. In this way, the NN model in each node can be trained on data collected from all nodes without transmitting and sharing with a central server, which resolves all of the above mentioned problems. The performance of the trained NN model is evaluated via experiments under different open sourced datasets to demonstrate that the proposed work has the potential to enhance the detection accuracy (mAP) over 40%.

In time-varying software-defined wireless sensor networks (SDWSNs) for Internet of Things (IoT) applications, the topology may change due to the interference or abnormal events, thus leading to network performance degradation. In this article, an energy-efficient topology control (TC) mechanism applied for IoT-oriented SDWSNs is proposed to maximize the network energy efficiency (EE) during the dynamic topology maintenance. First, a hierarchical SDWSN architecture consisting of the cluster-based sensing network and the programmable relay network is presented. Second, two TC algorithms based on the link EE are proposed to apply in the cluster and relay subnetworks of SDWSN, respectively. In the cluster subnetwork, the proposed distributed TC algorithm enables the link interference mitigation by employing power control and rate allocation in each cluster. In the relay subnetwork, the proposed centralized TC algorithm first utilizes a specified model to construct the original topology. During the dynamic topology maintenance, the proposed centralized TC algorithm is realized by the value-iteration learning method based on a Markov decision process (MDP) model, upon which the state-transition probability (STP) of the relay subnetwork is obtained, where the relay-network state is composed of the link, the queue, and the residual energy ratio states for all nodes in the relay subnetwork. Finally, simulation results show that both two TC algorithms can improve the corresponding subnetwork EE of time-varying SDWSN.

Powering a huge number of Internet of Things Devices (IoTDs), necessitated in many Internet of Things (IoT) applications, is a dreadful problem in many circumstances, in terms of the cost of labor, time, and so on. Far-field green energy wireless charging is a promising technique to remotely power IoTDs in a large-scale network. In order to improve the end-to-end energy efficiency, for the scenario with multiple green base stations (GBSs) wirelessly charging multiple IoTDs, our previous research work proposed a wireless charging scheme to aggregate the wireless power received by a static IoTD from multiple GBSs. In reality, in various emerging IoT applications, a large proportion of IoTDs are mobile. Hence, in this work, we focus on intelligently assigning multiple GBSs to wirelessly charge mobile IoTDs such that the IoTDs can be efficiently powered. We first propose the moving model and the charging model of IoTDs. Based on the nonlinear wireless charging model and nonlinear wireless energy conversion model, we then propose the Greedy dynamic joint charging (GAIN) algorithm to efficiently power the mobile IoTDs. Through extensive simulations, we validate the performance of the proposed algorithm.

Internet of Drones (IoD), where drones act as the Internet of Things (IoT) devices, provides multiple services, such as object recognition, traffic monitoring, and disaster rescue. The service response time is limited by drones’ onboard computing power, hence greatly affecting the Quality of Service (QoS). Machine learning [(ML), e.g., image processing] task offloading to the fog node attached to the ground base station can help reduce workload from drones and hence decrease service time. The communication latency between drones and the fog node during task offloading also affects the service time and hence the communication efficiency needs to be considered. Therefore, in this work, we consider the joint optimization of ML task offloading and power control in IoD to determine each drone’s number of offloaded images and wireless transmission power. We analyze the energy model of the commonly used convolutional neural network (CNN) for object recognition, and formulate the joint optimization problem as a mixed-integer nonlinear programming (MINLP) problem to minimize drones’ average service time constrained by drones’ energy budgets. An approximation algorithm with low computational complexity is then designed to address the problem and its performances are compared with the lower bounds and demonstrated via extensive simulations.

The growth of Internet of Things (IoT) has led to the convergence of heterogeneous networking systems powered by various protocols, and consequently the emergence of protocol-independent packet processing based on programmable data plane (PDP) for IoT. In this work, we study how to leverage the hardware acceleration enabled by offloading flow tables to SmartNIC to improve the performance of software PDP switches based on protocol-oblivious forwarding (POF). We design our SmartNIC offloading system (namely, OVS-POF-TC) based on the Linux kernel traffic classification (TC) system and open vSwitch (OVS), extend OVS to enable the installation of POF-based flow tables (POF-FTs) in a SmartNIC, and design a selective offloading mechanism for purposely offloading heavy-load POF-FTs. Our experimental results indicate that OVS-POF-TC offloads and updates POF-FTs timely, supports runtime programmability, and has improved packet processing throughput by$1.52\times $and$3.82\times $, when applying POF-FTs with SetField and AddField to packets, respectively. Moreover, to ensure that OVS-POF-TC can offload and replace POF-FTs adaptively, we formulate two mixed-integer linear programming (MILP) models to, respectively, solve the problems of flow placement and flow replacement, and also design time-efficient heuristics for them.

The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

Meeting huge traffic demand with resource constraints imposes a significant challenge for future generation wireless networks. In this work, we propose to utilize limited resources in a dense mobile edge computing (MEC) network to compute user equipment (UE) tasks through a novel hybrid multiple access (HYMA) scheme that employs both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). The main purpose of using HYMA is to reduce the co-channel interference incurred in NOMA by selectively deploying OMA while maintaining the required signal-to-interference ratio. We adopt partial offloading of computing tasks in the MEC network. We also employ network slicing to efficiently utilize the resources of the MEC network to meet different types of application requirements. We prioritize NOMA to increase spectral efficiency as well as energy efficiency. We first formulate a mixed integer nonlinear programming (MINLP) optimization problem to minimize the total energy consumption for both local computing and wireless transmission of the MEC network and propose an algorithm consisting of three parts (UE association, computing resource allocation, and wireless resource and uplink transmission power allocation) to solve the MINLP problem with less computational complexity. We demonstrate the viability of our solution via extensive simulations.

We propose a novel multi-tier (top, intermediate, and bottom tiers) architecture at the edge of a heterogeneous network (HetNet) where non-orthogonal multiple access (NOMA) provides access to user equipment (UE) to participate in federated edge learning (FEL). The HetNet consists of a macro base station (MBS) and several small base stations (SBSs) where each BS is equipped with an edge server (ES). SBSs use the same system bandwidth to increase the system capacity. The top tier consists of the MBS-ES which works as the global model aggregator while ESs of SBSs and UEs connected with MBS reside in the intermediate tier. Similarly, UEs connected with an SBS-ES of the intermediate tier occupy the bottom tier. ESs of SBSs work as the intermediate model aggregators between the ES of the top tier and the UEs of the bottom tier. To minimize the total energy consumption (EC) for local computing (LC) and uplink transmission (UT) of UEs, we formulate a non-linear programming (NLP) optimization problem, present our solution by decomposing the problem into sub-problems, and propose two sequential algorithms to estimate EC for both LC and UT with less complexity. Our extensively simulated results demonstrate the viability of our proposed work.

This paper extensively examines the impact of the numerology schemes on a sliced TDD radio access network. The slices are multiplexed over both the sub-6 GHz and mmWave bands. The incorporation of numerology schemes into network slicing, the impact of which has yet to be explored, will be highly instrumental in realizing the future 5G and 6G networks. Thus, this work demonstrates the enhanced capabilities of sliced networks utilizing numerology. To that end, two optimization problems are formulated, one to maximize the downlink average spectral efficiency of a throughput-sensitive slice, and the other to minimize the uplink transmission power of the users of the latency-sensitive slice. This work optimizes key parameters such as duplex ratio, numerology scheme and optimally allocates power and bandwidth under numerology-enabled slicing. The comparative analyses highlight that for high-throughput applications, lower numerology schemes are significantly more spectrally efficient and can often achieve throughputs characteristic of higher schemes. On the other hand, the higher schemes are the best choice for the minimization of user transmission power and latency, which are crucial in energy-efficient communications and edge computing applications, respectively.

Mobile edge computing (MEC), extending computing services from cloud to edge, is recognized as one of key pillars to facilitate real-time services and tackle backhaul bottleneck. However, it is not economically efficient to attach intensive security appliances to every MEC node to defend application-level DDoS attacks and ensure the availability of services. Thus, we explore the elasticity of security defense among MEC nodes by proposing a COoperative DEfense (CODE) framework for MEC, referred to asCODE4MEC. CODE4MEC aims to adapt to traffic changes by coordinating container-carried defensive resources among cooperative MEC nodes in an automatic way. Towards this aim, we propose four control plane functions to enable a life-cycle management for CODE4MEC, namely, CODE triggering, scheduling, coordination and releasing. However, an effective CODE4MEC requires non-trivial algorithmic schemes, in particular for CODE scheduling and coordination functions. We thus design an online combinatorial auction mechanism for real-time CODE scheduling, and prove a tighter performance bound relative to prior arts. As for CODE coordination, a flow-based traffic and context information coordination scheme is proposed to enable classical defense schemes to work properly and efficiently. Finally, using a combination of real testbed and simulation evaluations, we validate the effectiveness of CODE4MEC.

Providing quality communications under adversarial electronic attacks, e.g., broadband jamming attacks, is a challenging task. Unlike state-of-the-art approaches which treat jamming signals as destructive interference, this paper presents a novel active anti-jamming (AAJ) scheme for a jammed channel to enhance the communication quality between a transmitter node (TN) and receiver node (RN), where the TN actively exploits the jamming signal as a carrier to send messages. Specifically, the TN is equipped with a programmable-gain amplifier, which is capable of re-modulating the jamming signals for jamming modulation. Considering four typical jamming types, we derive both the bit error rates (BER) and the corresponding optimal detection thresholds of the AAJ scheme. The asymptotic performances of the AAJ scheme are discussed under the high jamming-to-noise ratio (JNR) and sampling rate cases. Our analysis shows that there exists a BER floor for sufficiently large JNR. Simulation results indicate that the proposed AAJ scheme allows the TN to communicate with the RN reliably even under extremely strong and/or broadband jamming. Additionally, we investigate the channel capacity of the proposed AAJ scheme and show that the channel capacity of the AAJ scheme outperforms that of the direct transmission when the JNR is relatively high.

With the development of Internet of Things (IoT), wireless rechargeable sensor networks (WRSNs) have been widely applied in modern society. There has a greater demand for green and efficient remote wireless charging to power the wireless sensor nodes (SNs) in WRSNs. However, few has tackled the wireless charging efficiency problem that can achieve the low dead SNs percentage (DSP) in far-field wireless charging. Existing far-field wireless charging schemes cannot meet the energy supply requirement of large-scale SNs. Therefore, this work proposes the multiple mobile chargers (MCs)-assisted efficient green energy wireless charging for WRSNs. Firstly, according to the existing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the area not covered by the wireless charging base stations (BSs) is divided into multiple irregular-shape sub-regions. Then, we leverage multiple MCs to wirelessly charge the SNs located in these sub-regions. Finally, we propose the Minimal chArging grouPings (MAP) algorithm to minimize the number of the MCs’ anchor points (APs) and efficiently charge the SNs. The simulation results validate that our proposed algorithm can effectively improve the wireless charging energy efficiency and reduce the DSP.

Nowadays, Far-field Wireless Power Transfer (FWPT) has attracted many research efforts to conveniently power the Internet of Things (IoT) devices. Electromagnetic Radiation (EMR) safety in FWPT has brought much attention from the public. Existing works on FWPT mainly focus on improving the remote charging efficiency but overlooking the effects of EMR. A few works consider EMR safety but do not present accurate EMR quantization analysis because there lacks an accurate EMR computing model in IoT wireless charging scenario. In this paper, in order to evaluate and avoid the EMR's harmful impact, we first propose an accurate theoretical calculation equation for EMR and the concept of Charging Restricted Area (CRA). In the wireless charging area on a 2-dimensional plane, according to the EMR computing model, we further maximize the overall charging power by adjusting the power of chargers and ensure that the EMR in this area is lower than the EMR safety threshold. The wireless charging EMR safety problem is formulated as a linear programming problem with infinite constraints. To re-express the wireless charging EMR safety problem as a typical linear programming problem with finite constraints, the Sampling Safety Charging (SSC) algorithm is proposed. We have conducted extensive experiments to validate our proposed algorithm; the simulation results show that the performance achieved by our algorithm outperforms that achieved by the distributed RObustlySafE (ROSE) algorithm.

A wireless sensor network's (WSN's) uptime is highly depended on the lifespans of its wireless sensor nodes (SNs). However, the battery of an SN sometimes may not be easy to be replaced owing to circumstantial constraints. Nowadays, the far-field wireless power transmission (WPT) has attracted more attention from industry and academy. Many researchers have proposed to utilize the WPT technology to remotely charge the SNs. As unmanned aerial vehicle (UAV) can fly close to the SNs located in some hard-to-reach areas, in this work, a UAV is adopted to assist the green base station (GBS) to efficiently power the SNs which are far away from the GBS. Considering the limited energy carried by the UAV and the UAV's energy consumption, we propose a novel UAV comprehensive charging strategy to efficiently power the WSN and to enable all the SNs to be charged with considerable energy replenishment. The simulation results validate our proposed strategy can effectively extend the WSN's uptime.

The high agility and maneuverability of the unmanned aerial vehicles (UAVs) provide a unique opportunity to carry communications and edge-computing facilities on board to serve mobile users in the cellular networks. An important problem would be to maximize the average aggregate quality-of-experience of all users over time slots. However, this is a non-convex, nonlinear and mixed discrete optimization problem, which is difficult to solve and obtain the optimal solution. We thus propose a deep reinforcement learning algorithm to solve this problem by considering UAV path planning, user assignment, bandwidth and computing resource assignment. The UAVs and base stations are to serve mobile users in multiple continuous time slots, and machine learning is leveraged to facilitate joint resource allocation and path planning in provisioning UAV-assisted edge computing. We compare the performance of our proposal with two baseline cases through simulations 1) with fixed UAV locations and 2) without UAVs. We demonstrate that the deep reinforcement learning algorithm performs better than these two baseline cases.

Unmanned aerial vehicles (UAVs) are playing a critical role in provisioning instant connectivity and computational needs of Internet of Things Devices (IoTDs), especially in crisis and disaster management. In this work, we focus on optimizing trajectories of UAVs along which IoTDs are served with communication and computing resources in multiple time slots. The Quality of Experience (QoE) of an IoTD depends on its latency performance; we thus aim to maximize the average aggregate QoE of all IoTDs overall time slots. However, this is a nonconvex, nonlinear, and mixed discrete optimization problem, which is difficult to solve and obtain the optimal solution. We thus propose two deep reinforcement learning algorithms to solve this problem by considering UAV path planning, user assignment, bandwidth, and computing resource assignment. We compare the performance of our proposed algorithms through simulations with three baseline cases: 1) with fixed UAV locations; 2) without UAVs; and 3) the fixed UAV trajectories. We demonstrate that the deep reinforcement learning algorithms perform better than all baseline cases.

This work proposes a dynamic numerology scheme assignment framework to provision mobile-edge computing (MEC) for massive Internet of Things (IoT) networks via unmanned aerial vehicles (UAVs). IoT devices (IoTDs) usually lack computational power; thus, they offload their computational tasks to MEC servers. To enhance their battery lives, an optimal assignment of a numerology scheme to each IoTD is imperative; it also enhances the system spectral efficiency. In this work, we bring MEC services closer to a massive IoT network by deploying a UAV-MEC and allocate communication resources of the sub-6-GHz band dependent on the numerology schemes to each IoTD. We formulate a multiobjective optimization problem (MOOP) with two countering objectives to maximize the uplink spectral efficiency while minimizing the IoTDs’ energy consumption. We solve a series of sequential subproblems, which are convex approximations of the MOOP and propose a novel algorithm to allocate computational resources, assign numerology schemes, and communication resources for each of the IoTDs. Our extensive simulation results validate the proposed aims herein.

With the worldwide ubiquitous implementation of Internet of Things (IoT), IoT devices (IoTDs) and their emerging applications are commendably enriching people’s daily life with intelligence and convenience. However, a tremendous number of IoTDs all over the world, incurring a huge amount of energy consumption, are exacerbating the global electric grid load and natural environment changes while the electronic equipment traditional charging approaches are unable to efficiently power the IoTDs. Leveraging green energy to remotely charge the IoTDs (i.e., green energy far-filed wireless charging) is the essential solution to revolutionarily resolve these problems. In this work, we propose a two-step green energy wireless charging (TREE) algorithm to efficiently power the IoTDs for the multiple-green base stations (GBSs)-to-multiple IoTDs charging scenarios. First, we propose the recharging threshold model for IoTDs and schedule the IoTDs to be efficiently charged by their associated GBSs within the green wireless charging time period. Second, for those IoTDs that will not be fully charged, we propose the multi-GBS joint accumulative charging scheme to fulfill most of the IoTDs’ charging requirements within the charging period. Finally, we validate the performance of the proposed algorithm through extensive simulations.

Federated learning (FL) has been proposed to efficiently and privacy-preserving distributed machine learning architecture for the Internet of Things (IoT). In a wireless FL system, clients in IoT devices train their local models over the local data sets. The derived local models are uploaded to an FL server to generate a global model, broadcasted to the clients in the next global iteration for further training. Owing to the heterogeneous feature of the clients, client selection is critical to determine the overall training time. Traditionally, the objective of client selection is to select the maximum number of clients who can derive and upload their local models before the deadline in each global iteration. However, selecting more clients increases the energy consumption of the clients. Moreover, selecting the maximum number of clients is unnecessary as having fewer clients in early global iterations and more clients in later global iterations have been proved to achieve higher model accuracy. Hence, this article proposes to dynamically adjust and optimize the tradeoff between maximizing the number of selected clients and minimizing the total energy consumption of the clients by selecting suitable clients and allocating appropriate resources in terms of CPU frequency and transmission power. We formulate the joint client selection and resource management problem and design the energy and latency-aware resource management and client selection (ELASTIC) algorithm to efficiently solve the problem. Extensive simulations are conducted to demonstrate the performance of ELASTIC.

Internet of Things (IoT) devices usually lack perpetual power supply since they are generally deployed at remote areas with limited battery capacities. Moreover, the computing power of the IoT devices is usually limited and the generated data need to be offloaded to a more powerful computing server for further processing. In this article, we study a novel unmanned aerial vehicle (UAV)-enabled IoT network, where the UAV delivers energy to the ground IoT devices by employing wireless power transfer (WPT) in the downlink and collects data in the uplink. In our work, we try to minimize the total energy consumption of the UAV by determining the trajectory and charging power of the UAV, the resource allocation, and the transmission scheme subject to task collection and resource budget requirements. To make the formulated problem more tractable, we decompose the primal problem into three subproblems (i.e., the transmission association problem, the trajectory design problem, and the resource allocation problem) and utilize the block coordinate descent (BCD) method to solve them alternately. Since the trajectory design problem is still highly nonconvex, we further transform it into a convex one by leveraging the successive convex approximation (SCA) technique. In the simulation, we provide extensive numerical results to corroborate the effectiveness of our proposed algorithm.

Mobile edge computing can provision hierarchical cloud resources at the edge, and efficiently reduce the distance that the remote data need to travel towards the end-users. To empower content delivery at the edge and ultimately transform “shorter distance” to “less time”, instead of focusing only on the storage aspects (e.g., allocation of limited media caches) of mobile edge, the radio aspects (e.g., interference models caused by sharing limited RBs) should be jointly considered. We hence propose a novel comprehensive analytical content caching and delivery framework for the cloud enhanced mobile edge with hierarchical radio access points. To investigate the impacts of limited radio resources (e.g., power, spectrum, and time) and storage resources, we propose a joint user scheduling and caching (JSC) scheme to optimize the end to end performance in terms of throughput (i.e., the number of successful content requests). By leveraging the backhaul links among multi-tier access points, the proposed algorithm can tap on the potential of the coded multi-casting scheme, which is designed to reduce the traffic load among the network. Simulation results demonstrate that the JSC algorithm can improve the resource utilization efficiency and provide a significant sum throughput gain.

Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally forwarded to the traffic management center (TMC) for in-depth analysis and may thus exacerbate the network paths to the TMC. To alleviate such bottlenecks, we propose to utilize edge computing by equipping edge nodes that are close to cameras with computing resources (e.g., cloudlets). A cloudlet, with limited computing resources as compared to TMC, provides limited video processing capabilities. In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC. Our solution utilizes two algorithms for each task, one implemented at the edge and the other one at the TMC, which are designed with the consideration of different computing resources. While the TMC provides strong computation power, the video quality it receives depends on the underlying network conditions. On the other hand, the edge processes very high-quality video but with limited computing resources. Our model captures this trade-off. We evaluate the performance of the proposed two-tier model as well as the traffic monitoring algorithms via test-bed experiments under different weather as well as network conditions and show that our proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

Traditional fingerprint-based positioning approaches work well on static data; they cannot handle scenarios where the data distribution, the feature space and even the signal source evolve over time, which are ubiquitous in real-world applications. One straightforward approach for circumventing these difficulties is to repeat labeled data calibration for maintaining an up-to-date fingerprint database, which is usually infeasible or expensive in large-scale indoor environments. In this paper, we propose a Long Short-Term indoor Positioning (LSTP) framework that enables adaptation at different time scales with low human-effort, and thus extends the effectiveness of existing fingerprinting techniques for a more generalized environment. Specifically, LSTP mainly considers the distribution discrepancy caused by continuous environmental dynamics in short-term positioning and the feature space heterogeneity in long-term positioning. To address the first challenge, we design an incremental ensemble localization model which leverages multiple source classifiers to resolve distribution differences in an online manner. To address the second challenge, we seek to borrow knowledge learned from an earlier time period with plenty of labeled samples for the current time period, thus reducing the required number of new calibration samples. By fully capturing the transferable spatial information across different time periods with multi-level constraints (sample, feature, and model levels), we can study a discriminative domain-invariant space from which we can make better predictions. The experiments on three real-world datasets demonstrate the superiority of the proposed framework, which outperforms the state-of-the-art systems by 18% in mean accuracy.

Using green energy to charge Internet of Things (IoT) devices is becoming a technically viable option. Green far-field Wireless Power Transmission can help powering IoT terminal devices. Keeping the maximum number of IoT devices (IoTDs) in their normal working modes is to furthest prolong the lifetime of the IoT network. In this work, for a given charging area, we first determine association between IoTDs and the green energy base station (GEBS), and then efficiently power IoTDs wirelessly. In green far-field wireless charging, we propose a dual-threshold model for IoTDs to facilitate wireless charging. In the dual-threshold model, we define two thresholds, i.e., Alarm Threshold and Working Threshold. Based on the dual-threshold model, we then propose the Dual-Threshold Orderly Charging (DTOC) algorithm to efficiently charge their surrounding IoTDs in a specific order to improve the charging efficiency. Finally, we validate the performance of the proposed algorithm through extensive simulations.

Owing to the development of Internet of Things (IoT) and Artificial Intelligence (AI) technology, powering IoT devices has become a dire problem that mobile IoT devices need a more portable way to be charged. Based on our previous research on green IoT, the far-field Wireless Power Transfer (WPT) powered by green energy can alleviate this problem. Although many existing works on Multi-Base Station Joint Charging Schemes have gained remarkable results, the aggregation of multiple power waves cannot be explicitly described by the traditional 1-dimensional model suggested by Friis Formula. The 2-dimensional model called vector model can solve this problem by clearly indicating how the multiple power waves aggregate at an IoT device in the form of a 2-dimensional vector. In this work, an Adjusting Phase (AP) method based on the vector model is designed to enhance the value of aggregated power waves. In addition, we propose the Greedy chArging Grouping Algorithm (GAGA) to ensure that the charging mission will be completed on time and the risk of running out of power can be reduced. Finally, we validate the performance of the proposed algorithm in comparison with the state-of-the-art solutions through extensive simulations.

The Industrial Internet of Things (IIoT), allowing direct wireless communications between industrial machines and people, is the key component of Industry 4.0. By grouping several IIoT devices (IIoT-Ds) into IIoT groups (IIoT-Gs), the IIoT-Ds within one group can share information and deter malicious users from accessing the network by modifying the access control list (ACL) in IIoT-Ds. In an IIoT-G, several IIoT-Ds jointly manage the access control of IIoT sensors. The security of ACL is protected by blockchains deployed in IIoT-Ds. Even secured by blockchains, a blockchain-based IIoT-G (B-IIoT-G) still cannot secure correct access control in a system with more than half of the blockchain-based IIoT-Ds (B-IIoT-Ds) from spreading wrong authorization information. In this article, we propose a B-IIoT-G with trust evaluation for each B-IIoT-D to evaluate the weight in voting for final decisions of authorization. By designing a new voting mechanism with trust evaluation for the access control in B-IIoT-G, the system has a higher probability of making correct authorization even with malicious B-IIoT-Ds. Simulation results have demonstrated that the mechanism with trust evaluation can still make, with a high probability, correct authorization even with more than half of the B-IIoT-Ds in B-IIoT-G being malicious.

The sensing service of Internet-of-Things (IoT) networks enables IoT sensors to sense the environment information (e.g., temperature and traffic conditions) and send them through the IoT gateway to the users who request those information. The explosive growth of IoT users and sensors injects massive traffic into IoT networks and easily depletes the battery of IoT sensors. Caching at the IoT gateway is hence a promising solution to mitigate this problem by storing popular IoT data at the IoT gateway and sending them directly to the users instead of activating IoT sensors to transmit the data. In our work, we investigate the content placement problem, which determines data to be cached at each time epoch in dynamic IoT networks with the objective to minimize the average data transmission delay constrained by the cache storage capacity and IoT data freshness. We formulate our problem as an integer linear programming (ILP) problem and then model it as a Markov decision process (MDP). A deep reinforcement learning algorithm is proposed to solve this problem and its performances are demonstrated via extensive simulations.

Machine learning models have been built in fog nodes in fog-aided Internet-of-Things (IoT) networks to provision future events prediction and image classification by training data collected from IoT devices. However, sending massive data from all devices to a fog node incurs huge network traffic in wireless links in between. Federated learning is proposed to address the challenge by training models locally in IoT devices and only sharing model parameters in the fog node. In this article, we investigate both the CPU frequency control and wireless transmission power control of all IoT devices to balance the tradeoff between the device energy consumption and federated learning time (consisting of both the computation and communication latencies) in fog-aided IoT networks. We formulate the joint optimization of CPU and power control as a nonlinear programming (NLP) problem with the objective to minimize the energy consumption of all IoT devices constrained by the federated learning time requirement. An alternative direction algorithm, which alternatively optimizes the CPU frequency and wireless transmission power until convergence, is hence designed to solve this problem and its performance is demonstrated via extensive simulations.

With convenient Internet access and ubiquitous high-quality sensors in end-user devices, a growing number of content consumers are engaging in the content creation process. Meanwhile, mobile edge computing (MEC) can provision distributed computing resources for local data processing. The MEC-enhanced video crowdsourcing application will gather user-generated video contents and collectively distribute them to the viewers of interest. To empower the crowdsourced video streaming at the edge, we investigate how to efficiently transmit data from content generators to the viewers. In particular, for a group of collaborative mobile users willing to share their data with the viewers, the content generation and delivery scheme is designed by considering the cost incurred by the crowdsourcing application. By leveraging the cloud resources available at the wireless base stations, the uploading or downloading server site is chosen for each user. To minimize the system makespan, i.e., the overall data transmission time among the generators and viewers, the user association scheme is also designed to efficiently utilize the diverse wireless radio resources. As compared with traditional centralized/distributed content delivery schemes, the proposed algorithm can improve the cost-effectiveness of distributed radio/cloud resources deployed at the mobile edge.

Transfer learning algorithms (TLAs) are often used to solve the distribution discrepancy issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to real time changes in the environmental dynamics of the target space due to three remarkable shortcomings: a) redundant knowledge in source domain may lead to “negative transfer”; b) the required target domain samples to calculate the distributions are unrealistically feasible for real-time positioning; c) they cannot transfer knowledge efficiently across domains with heterogeneous feature spaces. In this paper, we propose TransLoc, a heterogeneous knowledge transfer framework for fingerprint-based indoor localization, which can perform knowledge transfer efficiently even with only one sample in the target domain. Specifically, we first refine the source domain according to the target domain by removing redundant knowledge in the source domain. Then, we derive a cross-domain mapping, which transfers the specific knowledge of one domain to another domain, to construct a homogeneous feature space. In this new feature space, the transfer weights are computed for training a classifier for target location prediction. To further train the framework efficiently, we combine the mapping and weights learning into a joint objective function and solve it by a three-step iterative optimization algorithm. Extensive simulation and real-world experimental results verify that TransLoc not only significantly outperforms state-of-the-art methods but is also very robust to changing environment.

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the load-modulated multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of transmitting antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes.

Internet of Drones (IoD) employs drones as the internet of things (IoT) devices to provision applications such as traffic surveillance and object tracking. Data collection service is a typical application where multiple drones are deployed to collect information from the ground and send them to the IoT gateway for further processing. The performance of IoD networks is constrained by drones' battery capacities, and hence we utilize both energy harvesting technologies and power control to address this limitation. Specifically, we optimize drones' wireless transmission power at each time epoch in energy harvesting aided time-varying IoD networks for the data collection service with the objective to minimize the average system energy cost. We then formulate a Markov Decision Process (MDP) model to characterize the power control process in dynamic IoD networks, which is then solved by our proposed model-free deep actor-critic reinforcement learning algorithm. The performance of our algorithm is demonstrated via extensive simulations.

Advances in wireless communications are empowering the emerging Internet-of-Things (IoT) applications and services with billions of connected devices. Mobile-edge computing (MEC) has been proposed to reduce the round-trip delay of these applications as IoT devices may have limited computing resources and the resource-rich mobile cloud may be far away. On the other aspect, unmanned aerial vehicles (UAVs) may potentially be employed to improve the quality of service and the channel conditions of users. We thus propose to utilize the UAV as a computing node as well as a relay node to improve the average user latency in the UAV-aided MEC (UAV-MEC) network and formulate the UAV-MEC problem with the objective to minimize the average latency of all UEs. As the UAV-MEC problem is NP-hard, we decompose it into three subproblems. We propose an approximation algorithm with low complexity to solve the first subproblem and then we obtain the optimal solutions of the remaining two subproblems, upon which another proposed approximation algorithm employs these solutions to finally solve the UAV-MEC problem. The evaluation results demonstrate that the proposed algorithm is superior to three baseline algorithms.

In this article, a dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications. First, the time-varying features of SDWSNs are investigated from which the state-transition probability (STP) of the node is calculated based on a Markov chain. Second, a dynamic link weight is designed for DRA-EERS by incorporating both the link reward and the link cost, where the link reward is related to the link energy efficiency (EE) and the node STP, while the link cost is affected by the locations of nodes. Moreover, one adjustable coefficient is used to balance the link reward and the link cost. Finally, the energy-efficient routing problem can be formulated as an optimization problem, and DRA-EERS is performed to find the best relay according to the energy-efficient RS criteria derived from the designed link weight. The simulation results demonstrate that the path EE obtained by DRA-EERS through an available coefficient adjustment outperforms that by Dijkstra's shortest path algorithm. Again, a tradeoff between the EE and the throughput can be achieved by adjusting the coefficient of the link weight, i.e., increasing the impact of the link reward to improve the EE, and otherwise, to improve the throughput.

In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths, and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. The simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.

Fog computing is an emerging paradigm in provisioning computing and storage resources for the Internet-of-Things (IoT) devices. In a fog computing system, all devices can offload their data or computationally intensive tasks to nearby fog nodes, instead of to the distant cloud. As compared with cloud computing, fog computing can significantly reduce the transmission delay between IoT devices and computing servers. However, the current fog system is rather susceptible to malicious attacks. To increase the security level, we propose to partition the fog system into fog node clusters (FNCs), with fog nodes (FNs) in one cluster sharing the same access control list (ACL) which is protected by a blockchain. Generating blockchains requires tremendous computing power and can rapidly drain the computing capacities of FNs. In this article, we first customize the blockchain for FNC to reduce the required computing power consumption and storage spaces. Second, a new scheme is designed for the blockchain-based FNC (BFNC) to recover ACL automatically. In addition, we propose a heuristic algorithm to reduce the time to acquire hash values of blocks by computing cooperatively with all available devices. The simulation results have demonstrated that using the cooperative computing strategy can reduce the time of computing a block hash than noncooperative strategies.

In the Green Cloudlet Network (GCN) architecture, each User Equipment (UE) is associated with an Avatar (a private virtual machine for executing its UE's offloaded tasks) in a cloudlet located at the network edge. In order to reduce the operational expenditure for maintaining the distributed cloudlets, each cloudlet is powered by green energy and uses on-grid power as a backup. Owing to the spatial dynamics of energy demands and green energy generations, the energy gap (i.e., energy demand minus green energy generation) among different cloudlets in the network is unbalanced, i.e., some cloudlets' energy demands can be fully provisioned by their green energy generations but others need to utilize on-grid power to meet their energy demands. The unbalanced energy gap increases the on-grid power consumption of the cloudlets. In this paper, we propose the Green-energy aware Avatar Placement (GAP) strategy to minimize the total on-grid power consumption of the cloudlets by migrating Avatars among the cloudlets according to the cloudlets' residual green energy, while guaranteeing the service level agreement (the End-to-End (E2E) delay requirement between a UE and its Avatar). Simulation results show that GAP can save 57.1 and 57.6 percent of on-grid power consumption as compared to the two other Avatar placement strategies, i.e., Static Avatar Placement and Follow me AvataR, respectively.

It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.

Multiple drone-mounted base stations (DBSs) are used to be deployed over a disaster struck area to help mobile users (MUs) communicate with working BSs, which are located beyond the disaster-struck area. DBSs are considered as relay nodes between MUs and working BSs. In order to relax the bottleneck in wireless backhaul links, we propose a cooperative drone assisted mobile access network architecture by enabling DBSs (whose backhaul links are congested) to offload their traffic to other DBSs (whose backhaul links are not congested) via DBS-to-DBS communications. We formulate the DBS placement and channel allocation problem in the context of the cooperative drone assisted mobile access network architecture, and design a COoperative DBS plAcement and CHannel allocation (COACH) algorithm to solve the problem. The performance of COACH is demonstrated via extensive simulations.

Internet of drones (IoD), employing drones as the internet of things (IoT) devices, brings flexibility to IoT networks and has been used to provision several applications (e.g., object tracking and traffic surveillance). The explosive growth of users and IoD applications injects massive traffic into IoD networks, hence causing congestions and reducing the quality of service (QoS). In order to improve the QoS, caching at IoD gateways is a promising solution which stores popular IoD data and sends them directly to the users instead of activating drones to transmit the data; this reduces the traffic in IoD networks. In order to fully utilize the storage-limited caches, appropriate content placement decisions should be made to determine which data should be cached. On the other hand, appropriate drone association strategies, which determine the serving IoD gateway for each drone, help distribute the network traffic properly and hence improve the QoS. In our work, we consider a joint optimization of drone association and content placement problem aimed at maximizing the average data transfer rate. This problem is formulated as an integer linear programming (ILP) problem. We then design the Drone Association and Content Placement (DACP) algorithm to solve this problem with low computational complexity. Extensive simulations demonstrate the performance of DACP.

Drone-mounted base stations (DBSs) are promising solutions to provide ubiquitous connections to users and support many applications in the fifth generation of mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we have investigated the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network, and this problem is decomposed into two sub-problems: the DBS Placement problem (including the vertical position and horizontal position) and the joint UE association, power and bandwidth assignment (Joint-UPB) problem. Since the BUD problem is NP- hard, we propose approximation algorithms to solve the sub-problems and another, named the AA-BUD algorithm, to solve the BUD problem with guaranteed performance. The performance of the AA- BUD algorithm has been demonstrated via extensive simulations, and results show that the AA-BUD algorithm is superior to two benchmark algorithms.

Unmanned aerial vehicles (UAVs) have attracted attention from both the academic and industry because of its highly controllable mobility. The UAV has hence become a potential alternative for a large amount of geographically distributed sensors in provisioning sensing service where the information of different locations (e.g., temperature, humidity, pollutant level and traffic condition) are sensed and sent to the ground station (GS). However, the UAV on-board battery is usually limited due to the size and weight constraints, and greatly affects the UAV performance. Practically, the UAV usually needs to return to the GS for recharging before the battery exhaustion. The trajectory routes, therefore, should be well designed to meet the battery capacity constraint and improve the quality of service (QoS). In this paper, we investigate the trajectory optimization of rechargeable UAV for sensing service to minimize the task completion latency. We formulate this problem as a mixed integer linear programming (MILP) model. A Clone Searching Algorithm (CSA), which clones the rechargeable UAV into several non-rechargeable virtual UAVs and simultaneously search trajectory routes for each virtual UAV, is then designed to reduce the computational complexity of MILP. Numerical results demonstrate the performance of our proposed algorithm.

Fog-aided Internet of Things (IoT) networks provide low latency IoT services by offloading computational intensive and delay sensitive tasks to the fog nodes, which are deployed close to the IoT devices. Mobile IoT relies on battery limited mobile IoT devices (e.g., wearable devices and smartphones) to provision networks with enhanced flexibility. Mobile IoT faces the challenges of varying wireless channel conditions and hence may degrade the quality of service (QoS). We investigate the task allocation, which intelligently distributes tasks to different fog nodes and adapts to IoT varying mobile environment, such that the average task completion latency, constrained by QoS requirements and mobile IoT device battery capacity, is minimized. An integer linear programming (ILP) problem is then formulated to solve this problem. However, it is difficult to obtain the user mobility patterns (i.e., future locations where tasks are offloaded) and user side information (i.e., task length and computing intensity). Therefore, we propose an online learning algorithm to engineer task allocation decisions and then demonstrate its performances by extensive simulations.

Edge computing enables data collected by Internet of Things (IoT) devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users [i.e., user equipments (UEs)], drone base station (DBS), which can be flexibly deployed for hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) the DBS should be deployed in suitable areas with heavy traffic demands to serve more UEs and 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, we propose a traffic load balancing scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme, we divide the problem into two subproblems and design two algorithms to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme.

With the growth of WLAN infrastructure, received signal strength (RSS) fingerprint-based WiFi indoor positioning systems have received considerable attention recently. Some existing RSS fingerprint-based localization methods estimate locations by directly matching an online testing sample with an offline database, and thus show low accuracy because RSS is known to be vulnerable to variations caused by changing environment and heterogeneous hardware. To overcome the above drawbacks, we propose an expectation maximization indoor localization approach by leveraging supporting set (EMSS). In the offline phase, we first divide a positioning area into ${G}$ grid points and index each grid by a label. All the indices of grid points form a label set $ {\Psi =\{1,2,\ldots, G\}}$ . Then, we collect the RSS fingerprints to construct an offline database for all labeled grid points. In the online phase, given an online RSS testing sample, we first construct a supporting set (SS), which is a subset of $ {\Psi }$ , selected by the similarity between the online RSS sample and offline database. So, SS is a latent space that likely includes the true label (location) of the user. Based on the SS, we then derive an expectation maximization (EM) algorithm by incorporating the fingerprint quality into the estimation of the true label. EM can intelligently estimate the location of the user by evaluating the fingerprint quality of SS. Furthermore, we propose an optimal size selection algorithm based on Bayesian information criterion to adaptively determine the size of SS. Our method can effectively mitigate the impacts of changing environment and heterogeneous hardware without fingerprint and hardware calibrations, and can thus be practically applied. Experimental results verify that EMSS performs significantly better than some existing fingerprint-based methods.

Fusing the predictions of multiple received signal strength (RSS)-based classifiers is an efficient strategy to mitigate the impact of the fluctuation of the RSS. However, most of the existing fusion methods exhibit two remarkable shortcomings: 1) they need to train and store offline weights by the supervised learning and 2) they directly fuse the so-called candidate location set (CLS), which is collected from the most likely location estimate of each classifier (location with the largest probability of being the true location predicted by the classifier), and thus do not fully leverage the knowledge of classifiers. In general, the fluctuation of RSS does not guarantee the location predicted by each classifier with the highest probability to be the true location, thus leading to severe performance degeneration of the existing fusion methods. To overcome the above shortcomings, we propose an accurate WiFi localization framework by unsupervised fusion of an extended CLS (ECLS). First, we train multiple classifiers by only using RSS fingerprints in the offline phase. In the online phase, instead of collecting the CLS from the trained classifiers, we construct an ECLS by augmenting CLS with other location estimates (locations with predication probability greater than a certain threshold) from each classifier. As compared with the CLS, ECLS provides a bigger fusion space that likely includes the true location of the user. Furthermore, an unsupervised fusion localization algorithm based on the ECLS is derived from the joint optimization of weights and the location of the user. Furthermore, a point of inflection searching algorithm is also proposed to intelligently construct the ECLS. Real experimental results show that our proposed algorithm is more robust to changing environments and model errors, and can significantly improve the localization accuracy without any fingerprint and hardware calibrations.

The main drawback for large-scale applications of WiFi-based localization is the varying characteristics of received signal strength (RSS), which degenerates the localization performance seriously. To mitigate the variation problem, we propose a hybrid fingerprint quality evaluation model (HFQuM) for accurate WiFi localization. HFQuM can intelligently determine the location of a user by evaluating the hybrid fingerprint quality in different subareas, that is a high fingerprint quality indicates that the frequently occurred location label is more likely to be true. To achieve this, in the offline phase, instead of only collecting RSS fingerprints, we construct a WiFi-based group of fingerprints (GOOFs) consisting of RSS, signal strength difference (SSD), and hyperbolic location fingerprint (HLF). Given an RSS testing sample of a user at an unknown location in the online phase, we first construct the multiple supporting sets (MSSs), including a sample space and a label space, selected by the similarity between the online sample and the GOOF. Based on the MSS, HFQuM is able to estimate the user's location as well as subareas and their hybrid fingerprint quality simultaneously by jointly modeling the process of generating the sample space and label space. To further reduce the computational complexity, HFQuM employs an access point (AP) selection algorithm to exclude redundancy APs. Experimental results in a typical library environment verify the superiority of HFQuM in terms of localization accuracy as compared with other existing fingerprint-based methods.

To support machine type communications (MTCs) in next generation mobile networks, Narrowband-Internet of Things (NB-IoT) has been released by the third generation partnership project (3GPP) as a promising solution to provide extended coverage and low energy consumption for low cost MTC devices. However, the existing orthogonal multiple access (OMA) scheme in NB-IoT cannot provide connectivity for a massive number of MTC devices. In parallel with the development of NB-IoT and non-OMA (NOMA), introduced for the fifth generation wireless networks, is deemed to significantly improve the network capacity by providing massive connectivity through sharing the same spectral resources. To leverage NOMA in the context of NB-IoT, we propose a power domain NOMA scheme with user clustering for an NB-IoT system. In particular, the MTC devices are assigned to different ranks within the NOMA clusters where they transmit over the same frequency resources. Then, we formulate an optimization problem to maximize the total throughput of the network by optimizing the resource allocation of MTC devices and NOMA clustering while satisfying the transmission power and quality of service requirements. We prove the NP-hardness of the proposed optimization problem. We further design an efficient heuristic algorithm to solve the proposed optimization problem by jointly optimizing NOMA clustering and resource allocation of MTC devices. Furthermore, we prove that the reduced optimization problem of power control is a convex optimization task. Simulation results are presented to demonstrate the efficiency of the proposed scheme.

Cognitive radio (CR) can be leveraged to mitigate the spectrum scarcity problem of Internet of Things (IoT) applications while wireless energy harvesting (WEH) can help reduce recharging/replacing batteries for IoT and CR networks. To this end, we propose to utilize WEH for CR networks in which the CR devices are not only capable of sensing the available radio frequencies in a collaborative manner but also harvesting the wireless energy transferred by an access point. More importantly, we design an optimization framework that captures a fundamental tradeoff between energy efficiency (EE) and spectral efficiency of the network. In particular, we formulate a mixed integer nonlinear programming problem that maximizes EE while taking into consideration of user buffer occupancy, data rate fairness, energy causality constraints, and interference constraints. We further prove that the proposed optimization framework is an NP-hard problem. Thus, we propose a low complexity heuristic algorithm, to solve the resource allocation and energy harvesting optimization problem. The proposed algorithm is shown to be capable of achieving near optimal solution with high accuracy while having polynomial complexity. The efficiency of our proposal is validated through well designed simulations.

Accurate localization is critical to facilitate location services for Internet of Things (IoT). It is particular challenging to provision localization based on nonline-of-sight (NLOS) signals. Thus, we actualize source localization based on time difference of arrival (TDOA) derived from NLOS signal propagations. The existing robust least squares (RLS) method exhibits two shortcomings: 1) it is formulated using too large upper bounds on the NLOS errors, and 2) it suffers from the possible inexact triangle inequality problem. Aiming at circumventing the shortcomings of the existing RLS method, we propose two new RLS formulations. On one hand, to reduce the upper bounds on the NLOS errors, we propose to jointly estimate the source position and the NLOS error in the reference path. On the other hand, to avoid using the triangle inequality, we introduce a “balancing parameter” in the first formulation and develop the second formulation by transforming the measurement model. Both formulations are transformed via the S-lemma into optimization problems that are amendable to semidefinite relaxation. The proposed methods achieve superior performance over the existing methods, as validated by using both simulated and experimental data.

The Internet of Things (IoT) sensing service allows systems and users to monitor environment states by transmitting the content sensed by a variety of sensors. Owing to billions of sensors and devices deployed in the IoT system, a huge amount of data (big data) are generated, thus injecting tremendous traffic into the network. Cloud radio access network (C-RAN) is a promising wireless network architecture to accommodate the fast growing IoT traffic and improve the performance of IoT services. Caching in C-RAN, which brings content to the edges, not only alleviates the network traffic, thus improving the end-to-end user quality of service (QoS), but also avoids activating the sensors too frequently, thus reducing their energy consumption. The content placement problem determines what and where to cache in C-RAN. However, the caching performance is highly related to the caching storages. The storage allocation problem determines the storage capacities of network entities. In this paper, we jointly optimize the storage allocation problem and content placement problem in a hierarchical cache-enabled C-RAN architecture for IoT sensing service. We formulate the joint problem as an integer linear programming (ILP) model with the objective to minimize the total network traffic cost. The storage allocation problem and content placement problem are constrained by caching storage budgets and cache capacities, respectively. Two heuristic algorithms are proposed in order to reduce the computational complexity of ILP. Extensive simulations have been conducted to demonstrate that the performances of our proposed algorithms approximate the optimal solutions.

The Internet of Things (IoT) sensing service enables users to monitor the ambient environment by fetching data from IoT sensors. The explosive growth of mobile users and IoT applications injects massive traffic to the IoT network and also speeds up the drainage of sensor batteries. Caching at the IoT gateway (GW), which stores the IoT data and directly send them to the users, can avoid activating sensors too frequently, hence reducing the traffic in the IoT network as well as the energy consumption of sensors. To overcome the limited energy capacity of sensors, energy transmitters (ETs) are deployed to charge them. Practically, the GW and ETs may be owned by different operators, and the GW operator needs to incentivize ETs to provision the charging service. In this paper, we formulate a Stackelberg game in the cache-enabled energy harvesting aided IoT framework to improve the user quality of service. Caching strategies, incentive strategies, and ET transmission power strategies are jointly optimized to find the Stackelberg equilibrium by our proposed alternative direction approach. Simulation results elicit the benefits of our framework and demonstrate the performances of our proposed algorithm.

To provide a better quality of service (QoS), cloud computing paradigm in Internet of Things (IoT) networks has shifted toward the edge. Fog-aided IoT networks deploy fog nodes, which are equipped with computing and storage resources, at the network edge to take over the deadline-driven computing tasks from IoT devices. In the fog node, where multiple virtual machines (VMs) can be rented to process the tasks, fog provisioning is to determine which VM should be rented and how to distribute different tasks to VMs in order to minimize the system cost (i.e., VM rentals). On the other hand, VMs may fail and lead to QoS degradation. Hence, reliability of VMs should also be considered when addressing the fog resource provisioning problem. To improve reliability, more VMs should be rented to satisfy the QoS requirement; this leads to higher system cost. Therefore, there is a tradeoff between reliability and the system cost. In this paper, we investigate the tradeoff of maximizing the reliability and minimizing the system cost for fog resource provisioning in IoT networks. An integer linear programming (ILP) problem is formulated but suffers from a high computational complexity. We then design an alternative algorithm to achieve suboptimal solutions with better time efficiency. The simulation results demonstrate the performances of our proposed algorithm.

In a traditional big data network, data streams generated by User Equipments (UEs) are uploaded to the remote cloud (for further processing) via the Internet. However, moving a huge amount of data via the Internet may lead to a long End-to-End (E2E) delay between a UE and its computing resources (in the remote cloud) as well as severe traffic jams in the Internet. To overcome this drawback, we propose a cloudlet network to bring the computing and storage resources from the cloud to the mobile edge. Each base station is attached to one cloudlet and each UE is associated with its Avatar in the cloudlet to process its data locally. Thus, the E2E delay between a UE and its computing resources in its Avatars is reduced as compared to that in the traditional big data network. However, in order to maintain the low E2E delay when UEs roam away, it is necessary to hand off Avatars accordingly-it is not practical to hand off the Avatars' virtual disks during roaming as this will incur unbearable migration time and network congestion. We propose the LatEncy Aware Replica placemeNt (LEARN) algorithm to place a number of replicas of each Avatar's virtual disk into suitable cloudlets. Thus, the Avatar can be handed off among its cloudlets (which contain one of its replicas) without migrating its virtual disk. Simulations demonstrate that LEARN reduces the average E2E delay. Meanwhile, by considering the capacity limitation of each cloudlet, we propose the LatEncy aware Avatar hanDoff (LEAD) algorithm to place UEs' Avatars among the cloudlets such that the average E2E delay is minimized. Simulations demonstrate that LEAD maintains the low average E2E delay.

Fog-aided Internet of Things (IoT) addresses the resource limitations of IoT devices in terms of computing and energy capacities, and enables computational intensive and delay-sensitive tasks to be offloaded to the fog nodes attached to the IoT gateways. A fog node, utilizing the cloud technologies, can lease and release virtual machines (VMs) in an on-demand fashion. For the power-limited mobile IoT devices (e.g., wearable devices and smart phones), their quality of service may be degraded owing to the varying wireless channel conditions. Power control helps maintain the wireless transmission rate and hence the quality of service (QoS). The QoS (i.e., task completion time) is affected by both the fog processing and wireless transmission; it is thus important to jointly optimize fog resource provisioning (i.e., decisions on the number of VMs to rent) and power control. This paper addresses this joint optimization problem to minimize the system cost (VM rentals) while guaranteeing QoS requirements, formulated as a mixed integer nonlinear programming problem. An approximation algorithm is then proposed to solve the problem. Simulation results demonstrate the performance of our proposed algorithm.

The concept of fog computing is centered around providing computation resources at the edge of the network, thereby reducing the latency and improving the quality of service. However, it is still desirable to investigate how and where at the edge of the network the computation capacity should be provisioned. To this end, we propose a hierarchical capacity provisioning scheme. In particular, we consider a two-tier network architecture consisting of shallow and deep cloudlets and explore the benefits of hierarchical capacity provisioning based on queuing analysis. Moreover, we explore two different network scenarios in which the network delay between the two tiers is negligible and the case that the deep cloudlet is located somewhere deeper in the network and thus the delay is significant. More importantly, we model the first network delay scenario with bufferless shallow cloudlets and the second scenario with finite-size buffer shallow cloudlets, and formulate an optimization problem for each model. We also use stochastic ordering to solve the optimization problem formulated for the first model and an upper bound-based technique is proposed for the second model. The performance of the proposed scheme is evaluated via simulations in which we show the accuracy of the proposed upper bound technique and the queue length estimation approach for both randomly generated input and real trace data.

Mobile edge computing (MEC) is a new networking paradigm that enables low-latency computation offloading for compute-intensive mobile applications. The dynamic wireless channel, non-uniform spatiotemporal traffic, and limited computation resources impair the service latency of mobile edge computing. Therefore, jointly managing radio and computation resources is needed to achieve low latency MEC. In this paper, we propose a joint radio and computation resource management (iRAR) algorithm which minimizes users' service latency by optimizing the uplink transmission power, receive beamforming, computation task assignment, and computation resource allocation. We compare the performance of the proposed algorithm with three different algorithms and demonstrate that the iRAR algorithm reduces up to 52% average service latency as compared to the other algorithms.

By leveraging the elasticity of cloud computing, cloud radio access network (C-RAN) facilitates on-demand radio and computing resource provisioning. In this paper, we propose an energy-efficient on-demand C-RAN virtualization model which dynamically provisions virtual C-RAN according to service demand. The energy consumption of the virtual C-RAN is minimized by jointly optimizing the remote radio head (RRH) selection and computing resource provisioning. The network energy consumption minimization problem is challenging because of the interdependence between the RRH selection and the computing resource provisioning. We propose the energy-efficient on-demand C-RAN virtualization (REACT) algorithm to solve the problem in two steps. First, we cluster RRHs into groups using the hierarchical clustering analysis (HCA) algorithm and assign a BBU to each RRH group for the baseband signal processing. Second, we determine the RRH selection by optimizing the cooperative beamforming. The performance of the proposed algorithm is evaluated through extensive simulations, which shows the proposed algorithm reduces up to 62% of the network energy consumption as compared to a baseline algorithm.

Rapid growth of Internet of Things (IoT) services with different service level agreement (SLA) requirements has called for offloading computing tasks from the resource constrained IoT devices to the remote cloud. The growing number of IoT devices is exacerbating the links between IoT devices and the cloud. Furthermore, many services (e.g., health and disaster response) require fast service response and immediate data analytics, thus posing a great challenge to the remote cloud. Fog computing provides the solution by bringing computing resources to the edge of the network to assume substantial computing tasks. However, the resource failures during service processing should not be overlooked because they can greatly degrade service performance. In our work, we investigate the fog resource provisioning problem for the deadline-driven IoT services to minimize the system cost considering the probability of resource failures. We formulate the problem as an integer linear programming (ILP) model, and then design a Weighted Best Fit Decreasing (WBFD) algorithm with low computational complexity. Simulation results validate that our proposed algorithm performs close to the optimal solutions of ILP.

Applying Drone-mounted Base Station (DBS) to assist Macro Base Station (MBS) can potentially increase the throughput of the mobile access network. In this paper, we first derive the spectral efficiency of delivering traffic from the MBS to a Ground User (GU) via the DBS, which is operated in the half-duplex in-band mode, upon which we formulate the problem by jointly optimizing the DBS placement (i.e., the altitude of the DBS) and user association in order to maximize the spectral efficiency of the hotspot area. We design the Spectral efficienT Aware DBS pLacement and usEr association (STABLE) algorithm to solve the proposed problem and demonstrate the performance of STABLE via simulations.

Internet of Things (IoT) promotes a heightened level of awareness about our world and makes our life more intelligent and convenient. In IoT, machine-to-machine (M2M) communications enables direct connectivities among machines and devices to automatically exchange information and perform actions. Low Power Wide Area (LPWA) plays a crucial role in provisioning wide area coverage and low energy consumption network for M2M communications in IoT. Moreover, green energy harvesting is essential for mobile machine type-devices (MTDs) to achieve their self-sustainability and independence. Therefore, we propose dual-battery architecture to empower MTDs with concurrent green energy harvesting and IoT functionalities. Rather than routing through an LPWA base station (BS), direct and dual-hop transmissions are proposed for proximal M2M communications. According to the residual green energy in the MTDs' batteries, we provision a relay incentive policy and relay selection schemes to facilitate direct and dual-hop M2M communications. For dual-hop M2M communications, some heuristics are proposed to maximize the overall data rate with low computational complexity. Finally, we validate the performances of the proposed architecture and schemes through extensive simulations.

Device-to-Device (D2D) communications is regarded as an option to extricate cellular Base Stations (BSs) from heavy traffic load. With more devices using D2D communications, there is an urgent demand for more spectrum bands for D2D communications. To provide more licensed bands for D2D communications, downlink spectrum reuse is a promising way to solve this problem. In an FDD-OFDMA LTE network, discontinuous resource blocks (RBs) of downlink spectrum are allocated to the same cellular user, thus resulting in complex interference between D2D users and cellular users. In order to increase the number of D2D users in the system and to increase the capacity of D2D users, allowing RBs reuse by more D2D pairs is an effective method, which, however, may cause more complicated interference. Multiple D2D users communicate with the same RBs poses a trade-off between transmission power and D2D users data rates.To provide a high capacity and to serve more users, we propose the RICE algorithm to allocate continuous RBs to D2D pairs, by facilitating different D2D pairs to share the same RBs. Simulations show that the RICE algorithm provides the highest capacity as compared to the exclusive spectrum allocation (ESA) strategy and adaptive subcarrier allocation (ASA) algorithm.

Empowered by computing resources at the network edge, data sensed from Internet of Things (IoT) devices can be processed and stored in their nearby cloudlets to reduce the traffic load in the core network, while various IoT applications can be run in cloudlets to reduce the response time between IoT users (e.g., user equipment in mobile networks) and cloudlets. Considering the spatial and temporal dynamics of each application's workloads among cloudlets, the workload allocation among cloudlets for each IoT application affects the response time of the application's requests. While assigning IoT users' requests to their nearby cloudlets can minimize the network delay, the computing delay of a type of requests may be unbearable if the corresponding virtual machine of the application in a cloudlet is overloaded. To solve this problem, we design an application aware workload allocation scheme for edge computing-based IoT to minimize the response time of IoT application requests by deciding the destination cloudlets for each IoT user's different types of requests and the amount of computing resources allocated for each application in each cloudlet. In this scheme, both the network delay and computing delay are taken into account, i.e., IoT users' requests are more likely assigned to closer and lightly loaded cloudlets. Meanwhile, the scheme will dynamically adjust computing resources of different applications in each cloudlet based on their workloads, thus reducing the computing delay of all requests in the cloudlet. The performance of the proposed scheme has been validated by extensive simulations.

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, geolocation, and smart cities. Wi-Fi-based indoor localization using received signal strength (RSS) has drawn much attention over the past decade because it does not require extra infrastructure and specialized hardware. It is well known that the localization accuracy using RSS is rather susceptible to the changing environment. Localization by fusing multiple fingerprint functions of RSS is a promising strategy to overcome the above drawback. However, the existing fusion techniques cannot make full use of the intrinsic complementarity among multiple fingerprint functions. It also fails to exploit the knowledge obtained in the offline phase and thus shows low accuracy in the complex environment. This paper proposes a knowledge aided adaptive localization (KAAL) approach by using a global fusion profile (GFP) to mitigate the above shortcomings. First, we propose a GFP construction algorithm by minimizing position errors over all fingerprint functions with weight constraints in the offline phase. Based on the knowledge from GFP and the trained multiple fingerprint models, we then derive two KAAL algorithms, namely, multiple function averaging and optimal function selection, to achieve highly accurate localization results. Experimental results demonstrate that our proposed localization approach is superior to the existing methods both in simulated and real environments.

With the fast development of Internet of Things (IoT), the fifth generation (5G) wireless networks need to provide massive connectivity of IoT devices and meet the demand for low latency. To satisfy these requirements, nonorthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. In parallel with the development of NOMA techniques, mobile edge computing (MEC) is becoming one of the key emerging technologies to reduce the latency and improve the quality of service (QoS) for 5G networks. In order to capture the potential gains of NOMA in the context of MEC, this paper proposes an edge computing aware NOMA technique which can enjoy the benefits of uplink NOMA in reducing MEC users' uplink energy consumption. To this end, we formulate an NOMA-based optimization framework which minimizes the energy consumption of MEC users via optimizing the user clustering, computing and communication resource allocation, and transmit powers. In particular, similar to frequency resource blocks (RBs), we divide the computing capacity available at the cloudlet to computing RBs. Accordingly, we explore the joint allocation of the frequency and computing RBs to the users that are assigned to different order indices within the NOMA clusters. We also design an efficient heuristic algorithm for user clustering and RBs allocation, and formulate a convex optimization problem for the power control to be solved independently per NOMA cluster. The performance of the proposed NOMA scheme is evaluated via simulations.

We propose to apply constrained application protocol publish/subscribe to cache popular Internet of Things (IoT) resources in a broker to reduce the energy consumption of servers (which host these popular resources) and the average delay for delivering the IoT resources' contents to the clients. We provide the smart parking application in smart cities as an example to demonstrate the benefit for conducting popular IoT resource caching. However, caching popular IoT resources in the broker may not always be the optimal choice, i.e., the broker may be congested by caching too many IoT resources, and so the average delay for enabling the broker to deliver contents of the IoT resources may be unbearable. Thus, we propose a novel energy aware and latency guaranteed dynamic resource caching (EASE) strategy to enable the broker to cache suitable popular resources such that the energy savings from the servers are maximized, while the average delay for publishing the contents of the resources to the corresponding clients is minimized. We demonstrate the performances of EASE via simulations as compared to other two baseline IoT resource caching strategies.

In this paper, we propose two different free-space-optics (FSO) coverage models for next-generation high-speed-train communications. To the best of our knowledge, these are the first coverage models proposed for FSO seamless handover. The models provide different coverage areas for performing seamless signal handover and uninterrupted ground-to-train communication. The first model uses two different wavelengths in adjacent covered areas and the second one uses a single wavelength. We find the optimal distance from the train track to a ground base station and the distance between base stations to provide seamless connectivity and handover while minimizing the number of base stations along the track. We base our estimations on a realistic model of an FSO system and provide numerical evaluations demonstrating the performance of the proposed coverage models. We show the different amounts of received power on ground-to-train communications as a function of the location of ground base stations. We also consider the effect of fog on the FSO link as the most attenuating condition for FSO communications. Our results show that communication rates of 1 Gpbs and higher may be achieved with the proposed station positioning and coverage models.

Software-defined networking (SDN) has been considered as a break-through technology for the next-generation Internet. It enables fine-grained flow control that can make networks more flexible and programmable. However, this might lead to scalability issues due to the possible flow state explosion in SDN switches. SDN-based source routing can reduce the volume of flow-tables significantly by encoding the path information into packet headers. In this paper, we leverage the protocol-oblivious forwarding instruction set to design protocol-oblivious source routing (POSR), which is a protocol-independent, bandwidth-efficient, and flow-table-saving packet forwarding technique. We lay out the packet format for POSR, come up with the packet processing pipelines for realizing unicast, multicast, and link failure recovery, and implement POSR in a protocol-oblivious forwarding-enabled SDN network system. Experiments are then performed in a network testbed, which consists of 14 stand-alone SDN switches, to validate the advantages of POSR. Specifically, we compare POSR with several OpenFlow-based benchmarks for unicast, multicast, and link failure recovery, and confirm that POSR can reduce flow-table utilization effectively, shorten path setup latency and expedite link failure recovery.

At the Internet of Things (IoT) application layer, a physical phenomenon, which is sensed by a server (i.e., an IoT device), is defined as an IoT resource. In this paper, we propose to cache popular IoT resources in brokers, which are considered as the application layer middleware nodes. Caching popular resources in the brokers is to move the traffic loads (for delivering the up-to-date contents of the resources) from the servers (which host these popular resources) to the brokers, thus reducing the energy consumption of the servers. However, many brokers may be geographically distributed in the network and caching popular resources in nearby brokers may result in unbalanced traffic loads among the brokers, and may thus dramatically increase the average delay of the brokers in delivering the contents of their cached popular resources to clients. To reduce the average delay among the brokers, we propose to re-cache/re-allocate the popular resources from heavily loaded brokers into lightly loaded brokers in order to balance the traffic loads among brokers. We formulate the popular resource re-caching problem as an optimization problem, which is proven to be NP-hard. We design the latency aware popular resource re-caching (LEARN) algorithm to efficiently solve the problem, and demonstrate the performance of LEARN via simulations.

Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the most lethal cyber-attacks that can target an UAV network, namely, false information dissemination, GPS spoofing, jamming, and black hole and gray hole attacks. Extensive simulations confirm that the proposed scheme performs well in terms of attack detection even with a large number of UAVs and attackers since it exhibits a high detection rate, a low number of false positives, and prompt detection with a low communication overhead.

Different from traditional static small cells, Drone Base Stations (DBSs) exhibit their own advantages, i.e., faster and cheaper to deploy, more flexibly reconfigured, and likely to have better communications channels owing to the presence of short-range line-of-sight links. Thus, applying DBSs into the cellular network has great potential to increase the throughput of the network and improve Quality of Service (QoS) of Mobile Users (MUs). In this paper, we focus on how to place the DBS (i.e., jointly determining the location and the association coverage of a DBS) in order to improve the QoS in terms of minimizing the total average latency ratio of MUs by considering the energy capacity limitation of the DBS. We formulate the DBS placement problem as an optimization problem and design a Latency aware dronE bAse station Placement (LEAP) algorithm to solve it efficiently. The performance of LEAP is demonstrated via simulations as compared to other two baseline methods.

The complexity of networking mechanisms will increase significantly because of the dense deployment of radio base stations in ultra-dense mobile networks. As a result, the existing networking mechanisms may be unable to efficiently manage ultra-dense mobile networks. To solve this problem, we propose a data driven network optimization framework which integrates the big data analysis methods with networking mechanisms. In the proposed framework, we adopt big data analysis methods to divide densely deployed base stations into groups. Then, each group of base stations are managed with networking mechanisms independently. In this way, the complexity of the networking mechanisms is reduced. The key challenge in designing the framework is to optimally group base stations into clusters in real time. Addressing this challenge, the proposed framework consists of an offline machine learning module and an online base station clustering and network optimization module. The offline machine learning module predicts the optimal number of base station groups in the next time interval based on the historical data. The online base station clustering and network optimization module clusters base stations and optimize the network in real time. The performance of the proposed data-driven network management framework is validated through network simulations with real network data traces.

Owing to the impact of greenhouse gases on the environment and climate change, the on-grid energy consumption of Information and Communications Technology (ICT) has received much attention in recent years. Cellular networks are among the major energy guzzlers in ICT, and their contributions to the global energy consumption increase rapidly. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Utilizing green energy to power BSs is essential to save the on-grid energy and reduce the electricity expenditure of the network providers. However, in order to furthest save on-gird energy, most existing works only focus on maximizing the green energy utilization, while compromising the services received by the mobile users. In fact, dissatisfaction of services eventually leads to loss of market shares and profits of the network providers. Therefore, we propose a novel profit driven user association scheme for green heterogeneous cellular networks by jointly considering the green energy utilization and traffic delivery latency to maximize the profit of the network providers. Since this profit driven user association problem is NP-hard, we further propose some heuristics to maximize the profit with low computational complexity. Finally, we validate the performance of the proposed algorithm through extensive simulations.

Caching popular contents closer to users has been proposed to alleviate wireless network traffic and improve user quality of experience (QoE). Decisions on where and what to cache is of great importance. In this paper, we propose a hierarchical cache-enabled cloud radio access network (C-RAN) architecture where joint caching is considered in both remote radio heads (RRHs) and baseband units (BBUs) with the constraints of backhaul and fronthaul links. We formulate the content placement problem as an integer linear programming (ILP) model with the objective of minimizing the average content download time. A heuristic algorithm is proposed in order to reduce the time complexity. Simulation results of the average download delay are analyzed from different aspects including caching locations, total file lengths, cache sizes and file popularities, and they demonstrate that the performance of the proposed popularity-based algorithm approximates ILP solutions closely but with high time efficiency.

As accessing computing resources from the remote cloud for big data processing inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of networks, can potentially mitigate this problem. Although load offloading in cloudlet networks has been proposed, placing the cloudlets to minimize the deployment cost of cloudlet providers and E2E delay of user requests has not been addressed so far. The locations and number of cloudlets and their servers have a crucial impact on both the deployment cost and E2E delay of user requests. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing strategy (CAPABLE) to optimize the tradeoff between the deployment cost and E2E delay. When cloudlets are already placed in the network, we also design a load allocation scheme to minimize the E2E delay of user requests by assigning the workload of each region to the suitable cloudlets. The performance of CAPABLE is demonstrated by extensive simulation results.

Greening information and communications technology is becoming an environmental and economic sine qua non, and has attracted much research attention. For a cellular network, base stations (BSs) incur more than 50% of the energy consumption of the whole network. Therefore, BSs can be powered by green energy to reduce its on-grid power consumption. Meanwhile, the throughput has always been a critical issue in cellular networks. Since the throughput and energy consumption mutually affect each other, saving on-grid power is at the cost of sacrificing a certain amount of throughput. In this paper, we propose a Throughput Aware and Green Energy aware user association (TAGE) scheme in heterogeneous cellular networks (HCNs) to optimize the trade-off between the throughput and on-grid power consumption. Meanwhile, we employ an energy-throughput coefficient α to control the energy-throughput tradeoff. The simulation results verify that TAGE improves the effective throughput and saves a significant amount of on-grid power for HCNs.

In concert with advances of wireless technologies in facilitating internet connectivity of Internet of Things (IoT) devices, mobile edge computing can provision and distribute computing resources at the cloudlets to efficiently process a high volume of IoT data. Among the IoT applications, multi-party data sharing among IoT devices, wireless access nodes and cloudlets is becoming increasingly critical, not only because the data collected by each single IoT device will often stay unmined, but also because of the security concern. As IoT applications' dependence on the cloud environment grows, the rich resources at cloudlets often become the attack targets, and the IoT data that are stored or processed using the cloud resources will be jeopardized. For the internet of important things, we have investigated how to efficiently and securely share the data among multi-party. In particular, for a group of cooperative IoT devices, by leveraging the cloud resources available at the wireless access points, a secure cache site with fast data uploading rate is chosen for each user. To minimize the overall data downloading time, the multi-party multi-path data delivery scheme is also designed such that each user can efficiently retrieve the data belonging to other parties.

The increased density of base stations (BSs) may significantly add complexity to network management mechanisms and hamper them from efficiently managing the network. In this paper, we propose a big-data-driven network partitioning and optimization framework to reduce the complexity of the networking mechanisms. The proposed framework divides the entire radio access network (RAN) into multiple sub-RANs and each sub-RAN can be managed independently. Therefore, the complexity of the network management can be reduced. Quantifying the relationships among BSs is challenging in the network partitioning. We propose to extract three networking features from mobile traffic data to discover the relationships. Based on these features, we engineer the network partitioning solution in three steps. First, we design a hierarchical clustering analysis (HCA) algorithm to divide the entire RAN into sub-RANs. Second, we implement a traffic load balancing algorithm to characterize the performance of the network partitioning. Third, we adapt the weights of networking features in the HCA algorithm to optimize the network partitioning. We validate the proposed solution through simulations designed based on real mobile network traffic data. The simulation results reveal the impacts of the RAN partitioning on the networking performance and the computational complexity of the networking mechanism.

We consider a multi-carrier and densely deployed small cell network, where small cells are powered by renewable energy source and operate in a full-duplex mode. We formulate an energy and traffic aware resource allocation optimization problem, where a joint design of the beamformers, power and sub-carrier allocation, and users scheduling is proposed. The problem minimizes the sum data buffer lengths of each user in the network by using the harvested energy. A practical uplink user rate-dependent decoding energy consumption is included in the total energy consumption at the small cell base stations. Hence, harvested energy is shared with both downlink and uplink users. Owing to the non-convexity of the problem, a faster convergence sub-optimal algorithm based on successive parametric convex approximation framework is proposed. The algorithm is implemented in a distributed fashion, by using the alternating direction method of multipliers, which offers not only the limited information exchange between the base stations, but also fast convergence. Numerical results advocate the redesigning of the resource allocation strategy when the energy at the base station is shared among the downlink and uplink transmissions.

Next Generation Passive Optical Network Stage 2 (NG-PON2) features multiple wavelength channels. Channel bonding combines multiple NG-PON2 wavelength connections in parallel to increase the access network throughput beyond the capacity of a single connection. It enhances the access network peak rate provisioning. Channel bonding is actively studied in the ITU Telecommunication Standardization Sector (ITU-T) as a key enhancement to NG-PON2 recommendations. In this paper, we propose a channel bonding scheme by reusing the ITU-T PON data units of XG-PON encapsulation method (XGEM). The bonding system structure is investigated, and the bonding problem is formulated by using integer linear programming (ILP) formulation. A heuristic algorithm is proposed to control data transmission in the bonded channels. Performance is evaluated via network simulations. Simulation results are further analyzed to provide guidance on packet delay control and algorithm key parameter configuration.

Cloud computing enables service sharing in a massive scale via network access to a pool of configurable computing resources. It has to allocate resources adaptively for tasks and applications to be executed effectively and reliably in a large scale, highly heterogeneous environment. Resource allocation in cloud computing is an NP‐hard problem. In this study, the authors conduct a reliability analysis of cloud services by applying a Markov‐based method. They formulate the cloud scheduling problem as a multi‐objective optimisation problem with constraints in terms of reliability, makespan, and flowtime. Furthermore, they propose a genetic algorithm‐based chaotic ant swarm (GA‐CAS) algorithm, in which four operators and natural selection are applied, to solve this constrained multi‐objective optimisation problem. Simulation results have demonstrated that GA‐CAS generally speeds up convergence and outperforms other meta‐heuristic approaches.

To facilitate wireless transmission of multimedia content to mobile users, we propose a content caching and distribution framework for smart grid enabled OFDM networks, where each popular multimedia file is coded and distributively stored in multiple energy harvesting enabled serving nodes (SNs), and the green energy distributively harvested by SNs can be shared with each other through the smart grid. The distributive caching, green energy sharing, and the on-grid energy backup have improved the reliability and performance of the wireless multimedia downloading process. To minimize the total on-grid power consumption of the whole network, while guaranteeing that each user can retrieve the whole content, the user association scheme is jointly designed with consideration of resource allocation, including subchannel assignment, power allocation, and power flow among nodes. Simulation results demonstrate that bringing content, green energy, and SN closer to the end user can notably reduce the on-grid energy consumption.

The multitiered concept of Internet of Things (IoT) devices, cloudlets, and clouds is facilitating a user-centric IoT. However, in such three tier network, it is still desirable to investigate efficient strategies to offer the computing, storage, and communications resources to the users. To this end, this paper proposes a new hierarchical model by introducing the concept of field, shallow, and deep cloudlets where the cloudlet tier itself is designed in three hierarchical levels based on the principle of LTE-advanced backhaul network. Accordingly, we explore a two time scale approach in which the computing resources are offered in an auction-based profit maximization manner and then the communications resources are allocated to satisfy the users' quality of service.

The Internet of Things (IoT) heralds a vision of future Internet where all physical things/devices are connected via a network to promote a heightened level of awareness about our world and dramatically improve our daily lives. Nonetheless, most wireless technologies in unlicensed band cannot provision ubiquitous and quality IoT services. In contrast, cellular networks support large-scale, quality of service guaranteed, and secured communications. However, tremendous proximal communications via local base stations (BSs) will lead to severe traffic congestion and huge energy consumption in conventional cellular networks. Device-to-device (D2D) communications can potentially offload traffic from and reduce energy consumption of BSs. In order to realize the vision of a truly global IoT, we propose a novel architecture, i.e., overlay-based green relay assisted D2D communications with dual batteries in heterogeneous cellular networks. By optimally allocating the network resource, our proposed resource allocation method provisions the IoT services and minimizes the overall energy consumption of the pico relay BSs. By balancing the residual green energy among the pico relay BSs, the green energy utilization has been maximized; this furthest saves the on-grid energy. Finally, we validate the performance of the proposed architecture through extensive simulations.

Advances in wireless communications and microelectronics have spearheaded the development of unmanned aerial vehicles (UAVs), which can be used to augment a ground network composed of sensors and/or vehicles in order to increase coverage, enhance the end-to-end delay, and improve data processing. While UAV-aided networks can potentially find applications in many areas, a number of issues, particularly security, have not been readily addressed. The intrusion detection system is the most commonly used technique to detect attackers. In this paper, we focus on addressing two main issues within the context of intrusion detection and attacker ejection in UAV-aided networks, namely, activation of the intrusion monitoring process and attacker ejection. In fact, when a large number of nodes activate their monitoring processes, the incurred overhead can be substantial and, as a consequence, degrades the network performance. Therefore, a tradeoff between the intrusion detection rate and overhead is considered in this work. It is not always the best strategy to eject a node immediately when it exhibits a bad sign of malicious activities since this sign could be provisional (the node may switch to a normal behavior in the future) or be simply due to noise or unreliable communications. Thus, a dilemma between detection and false positive rates is taken into account in this paper. We propose to address these two security issues by a Bayesian game model in order to accurately detect attacks (i.e., high detection and low false positive rates) with a low overhead. Simulation results have demonstrated that our proposed security game framework does achieve reliable detection.

Explosive data traffic growth leads to a continuous surge in capacity demands across mobile networks. In order to provision high network capacity, small cell base stations (SCBSs) are widely deployed. Owing to the close proximity to mobile users, SCBSs can effectively enhance the network capacity and offloading traffic load from macro BSs (MBSs). However, the cost-effective backhaul may not be readily available for SCBSs, thus leading to backhaul constraints in small cell networks (SCNs). Enabling cache in BSs may mitigate the backhaul constraints in SCNs. Moreover, the dense deployment of SCBSs may incur excessive energy consumption. To alleviate brown power consumption, renewable energy will be explored to power BSs. In such a network, it is challenging to dynamically balance traffic load among BSs to optimize the network utilities. In this paper, we investigate the traffic load balancing in backhaul-constrained cache-enabled small cell networks powered by hybrid energy sources. We have proposed a network utility aware (NUA) traffic load balancing scheme that optimizes user association to strike a tradeoff between the green power utilization and the traffic delivery latency. On balancing the traffic load, the proposed NUA traffic load balancing scheme considers the green power utilization, the traffic delivery latency in both BSs and their backhaul, and the cache hit ratio. The NUA traffic load balancing scheme allows dynamically adjusting the tradeoff between the green power utilization and the traffic delivery latency. We have proved the convergence and the optimality of the proposed NUA traffic load balancing scheme. Through extensive simulations, we have compared performance of the NUA traffic load balancing scheme with other schemes and showed its advantages in backhaul-constrained cache-enabled small cell networks with hybrid power supplies.

With the development of green energy technologies, base stations (BSs) can be readily powered by green energy in order to reduce the on-grid power consumption, and subsequently reduce the carbon footprints. As smart grid advances, power trading among distributed power generators and energy consumers will be enabled. In this paper, we investigate the optimization of smart grid-enabled mobile networks, in which green energy is generated in individual BSs and can be shared among the BSs. In order to minimize the on-grid power consumption of this network, we propose to jointly optimize the BS operation and the power distribution. The joint BS operation and power distribution optimization (BPO) problem is challenging due to the complex coupling of the optimization of mobile networks and that of the power grid. We propose an approximate solution that decomposes the BPO problem into two subproblems and solves the BPO by addressing these subproblems. The simulation results show that by jointly optimizing the BS operation and the power distribution, the network achieves about 18% on-grid power savings.

It is known that multicast provisioning is important for supporting cloud-based applications, and as the traffics from these applications are increasing quickly, we may rely on optical networks to realize high-throughput multicast. Meanwhile, the flexible-grid elastic optical networks (EONs) achieve agile access to the massive bandwidth in optical fibers, and hence can provision variable bandwidths to adapt to the dynamic demands from the cloud-based applications. In this paper, we consider all-optical multicast in EONs in a practical manner and focus on designing impairment- and splitting-aware multicast provisioning schemes. We first study the procedure of adaptive modulation selection for a light-tree, and point out that the multicast scheme in EONs is fundamentally different from that in the fixed-grid wavelength-division multiplexing networks. Then, we formulate the problem of impairment- and splitting-aware routing, modulation and spectrum assignment (ISa-RMSA) for all-optical multicast in EONs and analyze its hardness. Next, we analyze the advantages brought by the flexibility of routing structures and discuss the ISa-RMSA schemes based on light-trees and light-forests. This paper suggests that for ISa-RMSA, the light-forest-based approach can use less bandwidth than the light-tree-based one, while still satisfying the quality of transmission requirement. Therefore, we establish the minimum light-forest problem for optimizing a light-forest in ISa-RMSA. Finally, we design several time-efficient ISa-RMSA algorithms, and prove that one of them can solve the minimum light-forest problem with a fixed approximation ratio.

To facilitate content delivery to mobile users, we propose a content caching and distribution framework for the heterogeneous OFDM networks, where a library of files available at the macro base station (MBS) can be distributively cached in multiple serving nodes (SNs). SNs are capable of both receiving unstored files from MBS and transmitting files to the associated users. For a given group of file downloading requests, the user scheduling scheme is jointly designed with the content caching scheme so that the number of served users is maximized for a given amount of spectrum and time. First, the corresponding downlink throughput maximization problem is shown to be NP-hard. Then, for the system with one MBS and one SN, the design of the joint user scheduling and caching scheme is transformed into a binary linear programming problem. For the system with one MBS and two SNs, the joint scheduling and caching (JSC) algorithm has been proposed to tap on the potential of the coded multicasting scheme between MBS and SNs. The proposed algorithm can be extended to networks with arbitrary number of SNs. Simulation results demonstrate that the JSC algorithm provides a significant sum throughput gain.

As the era of Internet of Things (IoT) approaches, we are facing a new level of awareness about our world. It has been predicted that almost 50 billion devices will be connected by 2020 to realize the Internet of Things. A large number of devices will communicate with each other to gather, share and forward information to connect people in a more intelligent, convenient and efficient way. Therefore, Device-to-Device (D2D) communications is expected to be the intrinsic part of IoT. However, most existing researches in D2D communications are based on the D2D and cellular communications coexisted architecture. Provisioning D2D and cellular communications in the same cellular network quickly exhaust the limited resources, thus leading to performance degradation. We envision a novel architecture of green relay assisted D2D communications with dual battery for IoT. We adopt low power small base stations (BSs) as the relay BSs in the network. By optimally allocating the network resource, our proposed architecture enables the source- destination device pairs to reach their required transmission data rates to satisfy their different application services. The relay BSs are powered by both green energy and on-grid energy, and equipped with dual battery. By balancing the residual green energy among the relay BSs, we maximize the utilization of green energy in the network and achieve the goal of furthest saving the on-grid energy. Finally, we validate the performance of the proposed architecture through extensive simulations.

In this paper, we present a stochastic geometry based framework to analyze the coverage probability and ergodic rate with different channel allocations for device-to-device (D2D) communications. Different from existing works, we assume there are two different kinds of users, cellular users and D2D users, in the muti-channel uplink cellular network. Specifically, cellular users can upload data to the nearest base station (BS) directly through cellular channels. However, D2D users must upload data to their own D2D relays through D2D channels and then the D2D relays communicate with the nearest BS through cellular channels. There is no overlapping between cellular channels and D2D channels. Each cellular user and D2D relay adopt the channel inversion power control with maximum transmit power limit. Our results indicate that the framework can help to find the optimal channel allocation to achieve the optimal system performance in terms of coverage probability and average rate.

The big data era is presenting unprecedented opportunities for generating new revenues in various sectors ranging from health care, economics, life science, to manufacturing. Datacenters (DCs) are widely deployed to provision various application services as well as to process big data. Since more and more servers are installed in DCs, the cost of electricity incurs a financial burden for the DC operators. Many DCs are equipped with renewable energy to reduce the electricity bill. However, the locations of the energy demands do not match the locations of the renewable energy generation. This mismatch may be addressed by virtual machine (VM) migration. The DCs, which lack renewable energy, can migrate their workloads to other DCs, which have abundant renewable energy. In addition, DC operators always want to maximize revenue and minimize operation cost. In this paper, the problem of maximizing revenue and minimizing operation cost of a green DC network enabled with and without VM migration is formulated by integer linear programming. Simulation results show that optimal results can be reached for small size problems. Two heuristic algorithms are proposed to efficiently solve large size problems. To our best knowledge, this is the first study of the revenue driven VM management problem in green DC networks towards big data with VM migration.

To advance green communications, we propose an orthogonal frequency division multiplexing (OFDM) based cooperative relay system, where the relay node not only can forward the data to the destination node, but is also capable of transferring energy to the source node. In particular, to maximize the overall system capacity in multiple subchannels and multiple time slots while meeting the power constraints, a power allocation optimization problem is formulated and solved in three steps. First, at each data transmission and data forwarding cycle, we split the total transmission power of relay into two parts, one for data forwarding and the other as power supplement for the source node. Then, our analysis indicates that at each cycle, once all of the subchannels are sorted in a certain order, the relay node will only provide forwarding power to the subchannels with index greater than a certain value. Meanwhile, the incentive for the relay node to provide power supplement should be strong enough such that relay chooses not to simultaneously transmit data and energy. Then, an equivalent convex constrained optimization problem is formulated and the solution is derived by solving the Lagrange function. The solution takes the form of water-filling in combination with a cooperative feature. Numerical results demonstrate that energy cooperation notably improves the system capacity.

Green communications has received much attention in recent years. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Reducing energy consumption of BSs is essential to realizing green cellular networks. Utilizing green energy to power BSs is a promising way to reduce the on-grid energy consumption. Maximizing the utilization of green energy has thus been proposed to furthest save the on-grid energy. In this paper, we propose a green energy driven user-BS association with dual battery system to maximize the utilization of green energy at BSs. The BSs of cellular networks are powered by both on-grid energy and green energy. The optimal usage of green energy is achieved by balancing the mobile users among BSs according to the amount of residual green energy in their batteries. This green energy driven user association optimization problem is NP-hard. Hence, we propose some heuristics to maximize the green energy utilization and approximate the optimal user association with low computational complexity. Finally, we validate the performance of the proposed algorithm through extensive simulations.

We propose a cloudlet network architecture to bring the computing resources from the centralized cloud to the edge. Thus, each User Equipment (UE) can communicate with its Avatar, a software clone located in a cloudlet, and can thus lower the end-to-end (E2E) delay. However, UEs are moving over time, and so the low E2E delay may not be maintained if UEs' Avatars stay in their original cloudlets. Thus, live Avatar migration (i.e., migrating a UE's Avatar to a suitable cloudlet based on the UE's location) is enabled to maintain the low E2E delay between each UE and its Avatar. On the other hand, the migration itself incurs extra overheads in terms of resources of the Avatar, which compromise the performance of applications running in the Avatar. By considering the gain (i.e., the E2E delay reduction) and the cost (i.e., the migration overheads) of the live Avatar migration, we propose a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations. Simulation results demonstrate that as compared to the other two strategies (i.e., Follow Me Avatar and Static), PRIMAL maximizes the profit in terms of maintaining the low average E2E delay between UEs and their Avatars and minimizing the migration cost simultaneously.

Greening information and communications technology is becoming an environmental and economic sine qua non, and has attracted much research attention. For a cellular network, the base stations (BSs) cost more than 50% of the energy consumption of the whole network. Therefore, BSs can be powered by green energy to reduce its on-grid power consumption. In this paper, we propose a greeN Energy Aware user associaTion (NEAT) scheme in the two-tier green heterogeneous network, that enables a BS depleting of green energy to offload its traffic load to other BSs with excessive green energy. Since the Macro BS (MBS) and Pico BS (PBS) employ different partitions of the licensed spectrum, we also consider the bandwidth allocation and adjust the two spectrum partitions dynamically. However, in the NEAT scheme, achieving the optimal user association in terms of minimizing the on-grid power consumption of BSs, is NP-hard. Therefore, we propose a heuristic NEAT algorithm to approximate the optimal solution with low computational complexity. Finally, the performance and viability of the algorithm are substantiated by simulation results.

Green communications has received much attention in recent years. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Reducing energy consumption of BSs is essential to realize green cellular networks. Utilizing green energy to power BSs is a promising way to reduce the on-grid energy consumption. Owing to the dynamics of both mobile traffic loads and green energy, the mismatch between the energy demands and green energy generation in a BS results in inefficient green energy utilization. Managing the battery in BSs can control the green energy usage in individual time slots, thus alleviating the inefficiency caused by the mismatch. In this paper, we propose an intelligent battery management mechanism to optimize the green energy utilization in BSs based on the Markov Decision Process (MDP). A large number of states in the Markov chain are required to model the dynamics of solar radiation and BS workload demands. Thus, the original MDP optimal policy iteration method incurs a high computational complexity. Therefore, we propose some heuristics to approximate the optimal energy dispatching strategy with low computational complexity, and validate the performance of the proposed algorithm through extensive simulations.

In the Web of Things (WoT) environment, Web traffic logs contain valuable information of how people interact with smart devices and Web servers. Mining the wealth of information available in the Web access logs has theoretical and practical significance for many important applications like network optimization and security management. The first critical step of the mining task is modeling the relationships among HyperText Transfer Protocol (HTTP) requests for accessing Web objects to investigate the behavior of Web clients. In this paper, we introduce the request dependency graph (RDG), a graph representation of the relationships among HTTP requests. Conceptually, a directed link from A to B in the graph means that the accessing of Web object B is caused by the accessing of A, i.e., B depends on A. We propose a methodology to establish such a graph by mining the temporal and causal information among aggregated HTTP requests. To demonstrate the value and effectiveness of the proposed model, we design and implement an algorithm for primary requests identification, which is a critical task of Web usage mining, based on the RDG. Evaluation results from a large-scale real-world Web access log shows that the RDG is a useful tool for Web usage mining.

Dramatic mobile data traffic growth has spurred a dense deployment of small cell base stations (SCBSs). Small cells enhance the spectrum efficiency and thus enlarge the capacity of mobile networks. Although SCBSs consume much less power than macro BSs (MBSs) do, the overall power consumption of a large number of SCBSs is phenomenal. As the energy harvesting technology advances, base stations (BSs) can be powered by green energy to alleviate the on-grid power consumption. For mobile networks with high BS density, traffic load balancing is critical in order to exploit the capacity of SCBSs. To fully utilize harvested energy, it is desirable to incorporate the green energy utilization as a performance metric in traffic load balancing strategies. In this paper, we have proposed a traffic load balancing framework that strives a balance between network utilities, e.g., the average traffic delivery latency, and the green energy utilization. Various properties of the proposed framework have been derived. Leveraging the software-defined radio access network architecture, the proposed scheme is implemented as a virtually distributed algorithm, which significantly reduces the communication overheads between users and BSs. The simulation results show that the proposed traffic load balancing framework enables an adjustable trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a considerable amount of on-grid power, e.g., 30%, at a cost of only a small increase, e.g., 8%, of the average traffic delivery latency.

We propose a Green Cloudlet Network (GCN) architecture to provide seamless Mobile Cloud Computing (MCC) services to User Equipments (UEs) with low latency in which each cloudlet is powered by both green and brown energy. Fully utilizing green energy can significantly reduce the operational cost of cloudlet providers. However, owing to the spatial dynamics of energy demand and green energy generation, the energy gap among different cloudlets in the network is unbalanced, i.e., some cloudlets' energy demands can be fully provided by green energy but others need to utilize on-grid energy (i.e., brown energy) to satisfy their energy demands. We propose a Green-energy awarE Avatar migRation (GEAR) strategy to minimize the on-grid energy consumption in GCN by redistributing the energy demands via Avatar migration among cloudlets according to cloudlets' green energy generation. Furthermore, GEAR ensures the Service Level Agreement (SLA) in terms of the maximum Avatar propagation delay by avoiding Avatars hosted in the remote cloudlets. We formulate the GEAR strategy as a mixed integer linear programming problem, which is NP-hard, and thus apply the Branch and Bound search to find its sub-optimal solution. Simulation results demonstrate that GEAR can save on-grid energy consumption significantly as compared to the Follow me AvataR (FAR) migration strategy, which aims to minimize the propagation delay between an UE and its Avatar.

Datacenters (DCs) are deployed in a large scale to support the ever increasing demand for data processing to support various applications. The energy consumption of DCs becomes a critical issue. Powering DCs with renewable energy can effectively reduce the brown energy consumption and thus alleviates the energy consumption problem. Owing to geographical deployments of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. Since migrating VMs incurs additional traffic in the network, the VM migration is constrained by the network capacity. The inter-datacenter (inter-DC) VM migration with network capacity constraints is an NP-hard problem. In this paper, we propose two heuristic algorithms that approximate the optimal VM migration solution. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 31% of brown energy consumption.

Accurate and real-time network measurements are becoming increasingly critical for a large variety of management tasks like accounting, bandwidth provisioning and security analysis. However, existing network measurement techniques have major limitations in supporting scalable and real-time traffic data monitoring and analyzing on high-speed (10Gbps and beyond) network links. Therefore, we propose a novel real-time network measurement system, named SONAR, which facilitates the convergence of real-time network monitoring and traffic analysis. We illustrate how the proposed system is designed and implemented based on streaming computing technologies, and demonstrate its capabilities with a built-in abnormal traffic detection application. The proposed system, based on real-world actualization and evaluation, has been demonstrated to be a high-performance and scalable solution for real-time network traffic measurements.

In this paper, we propose an E-voting procedure which, by utilizing the multi-part ballot mode, provides voters an enhanced way to cast and audit their own votes with great anonymity. The proposed scheme, the Multi-part ballot based Name and vOte separaTed E-voting system (M-NOTE), is partially based on our previous works, which have achieved the goal of keeping candidates confidential and voters anonymous, as well as reducing the risk of leaking both candidates' and voters' identities during the ballot distribution phase. In addition, M-NOTE can prevent the possible clash attack in which either malicious authority or hackers could partially manipulate voting results by tampering voters' original ballots. Meanwhile, our performance analysis shows that M-NOTE provides an outstanding secured level, as the possibility of ballot reconstruction and voting result manipulation has been reduced to closer to zero. This E-voting system also illustrates to readers an applicable framework of designing a fair election in the future.

Explosive data traffic growth has led to a continuous surge in capacity demands in mobile networks. In order to provision high network capacity, small cell base stations (SCBSs) are widely deployed. Owing to the close proximity to mobile users, SCBSs can effectively enhance the network capacity and offloading traffic load from macro BSs (MBSs). However, the cost-effective backhaul may not be readily available for SCBSs that leads to backhaul constraints in small cell networks. In this paper, we investigate the traffic offloading in backhaul constrained small cell networks. We have proposed a network latency aware user association scheme that balances traffic loads among base stations (BSs) to minimize the average traffic delivery latency of the mobile network. The proposed network latency aware user association scheme considers the traffic delivery latency in both BSs and their backhaul during the process of establishing user associations. We have proved that the proposed user association scheme converges to the optimal solution that minimizes the average traffic delivery latency of the network. The simulation results show that the proposed scheme reduces the average traffic delivery latency by 63% and 34% as compared to the user association scheme that considers only the traffic delivery latency in BSs and a two-tier data rate bias user association scheme, respectively.

This paper presents a mobile cloud computing application model and addresses how to minimize the energy consumption for uploading L size of data load within the T delay constraint. We propose a bandwidth allocation strategy for a LTE network with homogeneous sub-channel condition. Our objective is to allocate more bandwidth to each UE when its relative channel condition becomes better. We formulate the UE's objective function as the sum of two penalty functions: channel condition penalty function which incentivizes base stations to minimize the energy consumption for every UE and Service Level Agreement (SLA) demand penalty function which guarantees L size of data load that can be uploaded in time. In the network scenario, we formulate the EnerGy Optimized (EGO) bandwidth allocation strategy as a linear programming model and solve it by the Simplex Method. Simulation results show that EGO can save energy of up to 60% for each UE and decrease the SLA violation rate in the network of up to 30% in comparison with the existing bandwidth allocation strategy in the uplink of the LTE network.

Energy efficiency is a growing concern for wireless networks, not only due to the emerging traffic demand from smart devices, but also because of the dependence on the traditional unsustainable energy and the overall environmental concerns. The urgent call for reducing power consumption while meeting system requirements has motivated increasing research efforts on green radio. In this paper, we investigate a new joint spectrum and power allocation scheme for a cooperative downlink multi-user system using the frequency division multiple access scheme, in which arbitrary M base stations (BSs) coordinately allocate their resources to each user equipment (UE). With the assumption that multi-BS UE (user being served by multi-BS) would require the same amount of spectrum from these BSs, we conclude that when the number of multi-BS UEs is limited by M-1, the resource allocation scheme can always guarantee the minimum overall transmit power consumption while meeting the throughput requirement of each UE and also each BS's power constraint. Then, to decide the clusters of multi-BS UEs and the clusters of individual-BS UEs (users being served by individual BSs), we propose a UE-BS association scheme and a complexity reduction scheme. Finally, a novel joint spectrum and power allocation algorithm is proposed to minimize the total power consumption. Simulation results are presented to verify the optimality of the derived schemes.

In this paper, we present a novel capacity-aware spectrum allocation model for cognitive radio networks. First, we model interference constraints based on the interference temperature model, and let the secondary users (SUs) increase their transmission power until the interference temperature on one of their neighbors exceeds its interference temperature threshold. Then, knowing the SINR and bandwidth of potential links, we calculate the link capacity based on the Shannon formula, and model the co-channel interference between potential links on each channel by using an interference graph. Next, we formulate the spectrum assignment problem in the form of a binary integer linear programming (BILP) to find the optimal feasible set of simultaneously active links among all the potential links in the sense of maximizing the overall network capacity. We also propose a new radix tree based algorithm that, by removing the sparse areas in the search space, leads to a considerable decrease in time complexity of solving the spectrum allocation problem as compared to the BILP algorithm. The simulation results have shown that this proposed model leads to a considerable improvement in overall network capacity as compared to genetic algorithm, and leads to a considerable decrease in time duration needed to find the optimal solution as compared to the BILP algorithm.

Abstract Automatic modulation classification (AMC) is applied as the intermediate step between signal detection and demodulation to identify modulation schemes. AMC is a challenging task, especially in a non‐cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver. The proposed modulation classification scheme based on multi‐sensor signal fusion makes the premise that the combined signal from multiple sensors provides a more accurate description than any one of the individual signals alone. Multi‐sensor signal fusion offers increased reliability and huge processing gains in overall performance as compared with the single sensor, thus making AMC of weak signals in non‐cooperative communication environment more reliable and successful. Signal‐to‐noise ratio improvement through multi‐sensor signal fusion is studied by using second‐order and fourth‐order moments method. The classification performance based on multi‐sensor signal fusion is investigated in the additive white Gaussian noise channel as well as the flat fading channel and is evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset, and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi‐sensor signal fusion‐based AMC algorithm can greatly outperform other existing AMC methods.Copyright © 2013 John Wiley & Sons, Ltd.

Mobile base stations (BSs) can be powered by green energy in order to reduce the on-grid energy consumption, and subsequently reduce the carbon footprints. However, equipping a BS with a green energy system incurs additional capital expenditure (CAPEX) which is determined by the size of the green energy generator, the battery capacity, and other installation expense. In this paper, we introduce and investigate the green energy provisioning (GEP) problem which aims to minimize the CAPEX on deploying green energy powered BSs while achieving the capacity expansion and traffic offloading target. The GEP problem is challenging because it involves optimization over multiple time slots and across multiple BSs. We propose a heuristic green energy provisioning solution which decomposes the GEP problem into three sub-problems: the traffic load optimization problem, the active BS selection problem, and the green energy system sizing problem. We propose algorithms to solve the sub-problems and subsequently solve the GEP problem.

The integration of information and communication technologies with renewable energy sources will upgrade the conventional power distribution network to a Smart Grid, a smart and economic network, which can reduce power loss and carbon footprints. The power distribution network in the Smart Grid can be regarded as composing of multiple microgrids that include the customers, micro-sources and controllers. We propose the Adaptive-Economic Power Distribution Algorithm (AEPDA), which adaptively changes the topology of the distribution network in order to reduce the power transmission distance and maximize the utilization of renewable energy sources. The algorithm works in two operation states: the "online" mode when the microgrids are connected to the main power transmission grids and the "off-line" mode when the microgrids are disconnected from the main grid and are islanded. In the "on-line" mode, the On-Line Economic Microgrids Coalition Algorithm (OLEMCA) can form optimal coalitions among the mirco-grids; in the "off-line" mode, the Off-Line Micro-sources Coalition Algorithm (OLMCA) optimizes the coalitions among the micro-generators in each microgrid. The simulation results demonstrate that the proposed algorithms can reduce the power loss in the "on-line" mode and reduce costs for the customers in the "off-line" mode.

With the development of green energy technologies, base stations (BSs) can be powered by green energy in order to reduce the on-grid power consumption, and subsequently reduce the carbon footprints. As smart grid advances, power trading among distributed power generators and energy consumers will be enabled. In this paper, we have investigated the optimization of smart grid enabled mobile networks in which green energy is generated in individual BSs and can be shared among the BSs. In order to minimize the on-grid power consumption of this network, we have proposed to jointly optimize the BS operation and the power distribution. The joint BS operation and Power distribution Optimization (BPO) problem is challenging due to the complex coupling of the optimization of mobile networks and that of power grid. We have proposed an approximation solution that decomposes the BPO problem into two subproblems and solves the BPO by address these subproblems. The simulation results show that by jointly optimizing the BS operation and the power distribution, the network achieves about 18% on-grid power savings.

Recent emergence of smartphone applications have led to explosive traffic growth in cellular networks. Understanding the traffic characteristics and user behaviors in cellular data networks becomes critical in the rapidly evolving market. Statistics show that Android and iOS are two leading smartphone operating systems and Windows Phone operating system is catching up fast in the global smartphone market share. This paper characterizes mobile Internet traffic generated by Android, iOS, and Windows Phone platforms devices. We also explore and compare user behaviors of these three platforms from two aspects: traffic dynamics and user applications. This study was conducted based on the traffic data collected from a major cellular operator's network covering more than two million users. The platform of a mobile device is identified based on HTTP signatures. Our analysis of this big data set is crucial for improving user experience and future smartphone application design.

Dynamic wavelength bandwidth assignment (DWBA) in hybrid TDM/WDM EPON is a challenging and important issue. Normally, the ONUs located at different distances away from the OLT can affect the network performance caused by the heterogeneous round trip delays in report-grant based bandwidth allocation. This paper addresses this problem by mapping the DWBA problem in WDM/TDM EPON into parallel machine scheduling with release dates. This scheduling problem is NP-hard. In order to achieve high network utilization, we try to minimize the cycle length for given traffic loads. We propose hybrid shortest propagation delay (SPD) first and longest processing time (LPT) first (SPD/LPT) scheduling algorithms and analyze their network performances. The results show that our proposed algorithms can achieve the shortest cycle length as compared with traditional LPT and SPD rules when fewer upstream wavelengths are deployed with a give number of ONUs.

A continuous surge of mobile data traffic not only congests mobile networks but also results in a dramatic increase in the energy consumption of mobile networks. Device-to-device (D2D) communications is a promising technique for offloading mobile data traffic and enhancing energy efficiency of mobile networks. By enabling D2D communications, the base stations (BSs) may reduce their energy consumption through traffic offloading, and mobile users may increase their quality of services by retrieving contents from their neighboring peers instead of the remote BSs. By leveraging D2D communications, we propose a novel mobile traffic offloading scheme-the content brokerage. In the content brokerage scheme, a new network node called the green content broker (GCB) is introduced to arrange the content delivery between the content requester and the content owner. The GCB is powered by green energy, e.g., solar energy, to reduce the CO2footprints of mobile networks. In the scheme, maximizing traffic offloading with the constraints of the amount of green energy and bandwidth is an nondeterministic polynomial time (NP) problem. We propose a heuristic traffic offloading (HTO) algorithm to approximate the optimal solution with low computational complexity. Our simulation results valid the performance of the content brokerage scheme and the HTO algorithm.

ABSTRACT In this paper, we propose an improved electronic voting (E‐voting) system based on our previous work Name and vOte separaTed E‐voting system. The proposed E‐voting system, referred to as Enhanced Name and vOte separaTed E‐voting system, is improved with a new protocol design and a watchdog hardware device added to ensure voter confidentiality and voting accuracy. In our improved scheme, rather than Election Committee and Vote Counting Committee, an impartial third party, Ballot Distribution Center, is proposed to take the responsibility of distributing ballots. The votes and the candidates' names are separated into two parts while voters cast their votes. The watchdog device records all voting transactions during the election to prevent voting disputes and other malicious behaviors from voter frauds. Our proposed procedure addresses issues related to voter confidentiality and candidate privacy, voting frauds and voting accuracy, and will thus help to create a fair election. Copyright © 2014 John Wiley & Sons, Ltd.

Green communications has received much attention recently. For mobile networks, the base stations (BSs) account for more than 50% of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to greening mobile networks. In this paper, we propose a novel energy spectrum trading (EST) scheme which enables the macro BSs to offload their mobile traffic to Internet service providers' (ISPs') wireless access points by leveraging cognitive radio techniques. Since the ISP's wireless access points are usually closer to the mobile users, the energy and spectral efficiency of mobile networks are enhanced. However, in the EST scheme, achieving optimal mobile traffic offloading in terms of minimizing the energy consumption of the macro BSs is NP-hard. We thus propose a heuristic algorithm to approximate the optimal solution with low computation complexity. We have proved that the energy savings achieved by the proposed heuristic algorithm is at least 50% of that achieved by the brute-force search. Simulation results demonstrate the performance and viability of the proposed EST scheme and the heuristic algorithm.

In this paper, we address the time-of-arrival (TOA) based localization problem in an adverse environment, where line-of-sight (LOS) signal propagation between the source and the sensor is not readily available, in which case we have to resort to non-line-of-sight (NLOS) signals. Two convex relaxation methods, i.e., the semidefinite relaxation (SDR) and the second-order cone relaxation (SOCR) methods, are proposed to mitigate the effect of NLOS errors on the localization performance. We consider two separate cases in which the information of the NLOS status is totally unknown and perfectly known, respectively. The proposed methods can be applied without knowing the distribution of NLOS errors. Moreover, we propose a NLOS error mitigation method that is robust to detection errors, which are generated in the process of detecting NLOS paths. Simulation results show that the proposed convex relaxation methods outperform some existing state-of-the-art methods.

Device to device (D2D) communications is a promising concept to improve data rates and the energy efficiency of mobile networks. User devices (UEs) participated in D2D communications may increase their own data rates by retrieving the content from their neighboring peers instead of from the base stations. Meanwhile, UEs may drain their batteries while performing as content providers and transmitting the content to their peers. Increasing the data rates of D2D communications is desirable to alleviate the UEs' power consumption. In this paper, we propose a novel green relay assisted D2D communication architecture, in which the relay nodes powered by green energy are deployed to increase the data rates of D2D communications. However, achieving the optimal relay assignment for green relay assisted D2D communications is challenging. We propose a heuristic green relay assignment algorithm which maximizes the minimum data rates of the D2D pairs while considering the green load capacity of the relay nodes. We show that the proposed algorithm approximates the optimal solution with low computational complexity, and validate its performance by using simulation results.

In this paper, we will discuss an E-voting procedure by utilizing ring signature. The proposed scheme, RE-NOTE, is based on our previous work E-NOTE and has the benefit of reducing the risk of leaking voters' identities during the ballot distribution procedure. In addition, we will consider the “Clash Attack” which is a simple attack on the verification procedure during an election. Either authority or hackers may use the receipt to manipulate the voting results. We will show how our proposed scheme can mitigate such an attack. Our proposed scheme addresses issues related to both voter's and candidate's confidentiality and verification, and thus provides a framework for fair elections.

Bandwidth is a key resource in cloud networks. Every tenant wants to be assigned the bandwidth which is proportional to the price they have paid. At the cloud vender side, the link bandwidth utilization could be enhanced to support more clients. In this paper, we show that the traditional PS-N (Proportional Sharing at Network level) bandwidth allocation algorithm cannot achieve the network proportionality fairness when the network is over-subscribed. PPSN (Persistence Proportional Sharing at Network level) is proposed to solve the unfairness issue. However, the bandwidth utilization of both algorithms is not good enough to meet venders' demands. BEPPS-N (Bandwidth Efficiency Persistence Proportional Sharing at Network level) is thus proposed to enhance the bandwidth utilization by assigning more bandwidth to the communication pairs that are not passing through bottleneck links, and at the same time, keep proportionality fairness per different tenant. Finally, simulations and performance analysis have been conducted to substantiate the viability of our proposed approach.

Power consumption is a critically important issue for data centers. Virtual machine (VM) consolidation is fundamentally employed to improve resource utilization and power optimization in modern data centers. In general, VM consolidation is formulated as a vector bin packing problem, which is a well-known NP-hard problem. Hence, heuristic algorithms, such as single dimensional heuristics (e.g., first fit decreasing (FFD)) and dimension-aware heuristics (e.g., DotProduct), are usually deployed in practice for VM consolidation. However, all of these previous heuristic algorithms did not sufficiently explore the heterogeneity of the VMs' resource requirements. In this paper, we propose several heterogeneity aware dominant resource assistant heuristic algorithms for VM consolidation. The performance evaluations validate the effects of the proposed heterogeneity aware heuristics on VM consolidation. The proposed heuristics can achieve quite similar consolidation performance as dimension-aware heuristics with almost the same computational cost as those of the single dimensional heuristics.

Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to enhance the energy efficiency of cellular networks. Meanwhile, the mobile data traffic is expected to increase exponentially. To accommodate the increasing data traffic with the limited radio frequency, enhancing the spectrum efficiency is critical for next generation cellular networks. In this paper, we propose an auction-based energy-spectrum trading scheme which exploits the cooperation between primary base stations (PBSs) and the secondary base stations (SBSs) to enhance the energy as well as spectrum efficiency of cellular networks. In the cooperation, by leveraging cognitive radio, PBSs share the licensed spectrum with SBSs, and the SBSs provide data service to the primary users under its coverage utilizing the shared bandwidth. The cooperation between PSBs and SBSs can significantly improve the energy and spectral efficiency of cellular networks. However, optimizing the bandwidth sharing between PBSs and SBSs is an NP-hard problem. Solving such problem using centralized algorithms is not computationally efficient, especially when considering a large number of PBSs and SBSs. Thus, we design an auction-based decentralized mechanism to enable the cooperation between PBSs and SBSs. Simulation results that demonstrated the performance and viability of the proposed decentralized mechanism.

Green communications has received much attention recently. Utilizing green energy in wireless cellular networks is promising to reduce the main grid electricity consumption, and thus to reduce the CO2footprint. However, owing to the fluctuating nature of green energy, it is challenging to use green energy in cellular networks. In this paper, we propose an energy agile packet scheduler which maximizes the utilization of green energy by optimizing the packet scheduling. The packet scheduling optimization problem is NP-hard in the strong sense. The energy agile scheduler approximates the optimal packet scheduling solution in two steps. First, the energy agile scheduler balances the BS's energy consumption in transmitting packets among time slots. Second, within each time slot, the energy agile scheduler optimizes the bandwidth allocations to minimize the BS's energy consumption. Simulation results demonstrate that the proposed energy agile scheduler achieves significant main grid energy savings.

Extensive studies have shown that the peer-to-peer (P2P) traffic has already become the dominant traffic in the current Internet. The current P2P streaming user base is still undergoing stunning growth in China although its user scale already reached 158 million in 2010, 68% of Chinese web users. Hence, a comprehensive understanding of the P2P streaming network traffic characterization is essential to Internet Service Providers (ISPs) in terms of network planning and resource allocation. In this paper, based on the massive data collected with a passive network monitoring equipment placed in the Internet backbone, we provide an in-depth view of the current P2P streaming traffic in the current Internet of China. In particular, we statistically study the P2P streaming traffic in both wired (ADSL in this paper) and wireless (CDMA) networks, and characterize the traffic from both flow-level and packet-level aspects. Our study uncovers the significant impact of the P2P streaming traffic on the underlying network due to its unique characteristics and the bandwidth intensive nature of the corresponding applications. In addition, the result reveals the significant difference between the characterizations of the P2P streaming traffic in wired and wireless networks due to their respective intrinsic environmental characteristics.

In elastic optical networks (EONs), bandwidth fragmentation refers to the existence of non-aligned, isolated and small-sized blocks of contiguous subcarrier slots in the optical spectrum. As they are neither contiguous in the spectrum domain nor aligned along the routing paths, the network operator will have difficulty to use these slots for future connections. In this work, we analyze the effect of bandwidth fragmentation on the blocking probability in EONs. Our theoretical analysis indicates that two factors related to bandwidth fragmentation have effects on the blocking probability: 1) the extent that the available slot-blocks (i.e., blocks of contiguous slots) on different links are aligned on spectrum locations, and 2) the sizes of the available slot-blocks in links' spectra for future requests. When an EON's spectrum becomes more fragmented, the first factor actually reduces the blocking probability, while the second one increases the blocking probability. Their mixed effect determines the overall trend of how the blocking probability will change with bandwidth fragmentation. Our theoretical model can forecast this trend and reveal the relation among the blocking probability, bandwidth fragmentation, request bandwidth distribution, and spectrum utilization. We have also conducted numerical simulations to verify the theoretical analysis, and the simulation results exhibit similar trends as predicted by the theoretical model.

Quantized Congestion Notification (QCN) has been developed for IEEE 802.1Qau to provide congestion control at the Ethernet Layer or Layer 2 in data center networks (DCNs) by the IEEE Data Center Bridging Task Group. One drawback of QCN is the rate unfairness of different flows when sharing one bottleneck link. In this paper, we propose an enhanced QCN congestion notification algorithm, called fair QCN (FQCN), to improve rate allocation fairness of multiple flows sharing one bottleneck link in DCNs. FQCN identifies congestion culprits through joint queue and per flow monitoring, feedbacks individual congestion information to each culprit through multi-casting, and ensures convergence to statistical fairness. We analyze the stability and fairness of FQCN via Lyapunov functions and evaluate the performance of FQCN through simulations in terms of the queue length stability, link throughput and rate allocations to traffic flows with different traffic dynamics under three network topologies. Simulation results confirm the rate allocation unfairness of QCN, and validate that FQCN maintains the queue length stability, successfully allocates the fair share rate to each traffic source sharing the link capacity, and enhances TCP throughput performance in the TCP Incast setting.

Mobile ad hoc networks (MANETs) have attracted much attention due to their mobility and ease of deployment. However, the wireless and dynamic natures render them more vulnerable to various types of security attacks than the wired networks. The major challenge is to guarantee secure network services. To meet this challenge, certificate revocation is an important integral component to secure network communications. In this paper, we focus on the issue of certificate revocation to isolate attackers from further participating in network activities. For quick and accurate certificate revocation, we propose the Cluster-based Certificate Revocation with Vindication Capability (CCRVC) scheme. In particular, to improve the reliability of the scheme, we recover the warned nodes to take part in the certificate revocation process; to enhance the accuracy, we propose the threshold-based mechanism to assess and vindicate warned nodes as legitimate nodes or not, before recovering them. The performances of our scheme are evaluated by both numerical and simulation analysis. Extensive results demonstrate that the proposed certificate revocation scheme is effective and efficient to guarantee secure communications in mobile ad hoc networks.

Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings.

In this paper, we design a new distributed angle estimation method for localization in wireless sensor networks (WSNs) under multipath propagation environment. We employ a two-antenna anchor that can emit two linear chirp waves simultaneously, and propose to estimate the angle of departure (AOD) of the emitted waves at each receiving node via frequency measurement of the local received signal strength indication (RSSI) signal. An improved estimation method is further proposed where multiple parallel arrays are adopted to provide the space diversity. The proposed methods rely only on radio transceivers and do not require frequency synchronization or precise time synchronization between the transceivers. More importantly, the angle is estimated at each sensor in a completely distributed manner. The performance analysis is derived and simulations are presented to corroborate the proposed studies.

Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. In this paper, we optimize the energy utilization in cellular networks whose BSs are powered with both regular energy from the grid and the renewable energy. We minimize the on-grid energy consumption of BSs by adapting their cell sizes. The cell size optimization problem is NP-hard. We divide the problem into two subproblems: the multi-stage energy allocation problem and energy consumption minimization problem. We propose an energy allocation policy and an approximation algorithm to solve these subproblems, respectively, and subsequently solve the cell size optimization problem. Simulation results demonstrate that the proposed solution achieves significant energy savings.

Data redundancy elimination (DRE), also known as data de-duplication, reduces the data amount to be transferred or stored by identifying and eliminating both intra-object and inter-object duplicated data elements. It is one of the key content delivery acceleration techniques over wide area networks (WANs) to reduce delivery latency and bandwidth consumptions by reducing the amount of data to be transferred. Deploying DRE at the end hosts maximizes the bandwidth savings and latency reductions, because the amount of content sent to the destination hosts is minimized. However, standard DRE used to identify redundant content chunks is very expensive in terms of memory and processing capability especially on resource constrained hosts. By analyzing the web application traffic traces, we find out that some types of contents have more redundant contents than others. Thus, it is possible to apply DRE selectively and opportunistically on those contents with more redundant data elements than other content types to save the memory and processing resources at the hosts. In this paper, we propose content-type based selective DRE (SDRE), which deploys DRE selectively on the contents which have the most opportunities for redundant content identification. We explore the benefits of deploying SDRE on smartphone traffic traces. The results show that SDRE can achieve almost the same bandwidth savings as that of standard DRE with less computation and smaller memory.

Owing to the imminent fixed mobile convergence, Internet applications are frequently accessed through mobile nodes. However, service delivery latency is too high to satisfy user expectations. In this paper, we design a new TCP algorithm, TCP-ME (Mobile Edge), to accelerate the service delivery in mobile networks. Considering the QoS (Quality of Service) mechanisms of mobile networks, TCP-ME is designed to differentiate the packet loss caused by wireless errors, traffic conditioning of mobile core networks, and Internet congestion, as well as to react to the packet loss accordingly. To detect wireless errors, we mark the ACK (Acknowledge) packets in the uplink direction at the base station, and the marking threshold is a function of the instantaneous downlink queue length and the number of consecutive HARQ retransmissions. We modify the ECN mechanism with deterministic marking to detect Internet congestion. The packet loss caused by traffic conditioners of mobile networks is detected by whether the incoming DUPACK is marked or not. TCP-ME adapts the inter-packet interval when the packet loss is caused by wireless errors or the admission control mechanism. If the packet loss is due to Internet congestion, TCP-ME applies the TCP-New Reno's congestion window adaptation algorithm. Simulation results show that TCP-ME can speed up web service response time in mobile networks by about 80%.

In this paper, we propose an E-voting system based on and improved from our previous work (Name and vOte separaTed E-voting system, NOTE). The proposed E-voting system, referred to as Enhanced NOTE (E-NOTE), is enhanced with a new protocol design and watchdog hardware device to ensure voter confidentiality and voting accuracy. In our improved scheme, other than the Election Committee (EC) and Vote Counting Committee (VCC), an impartial third party, Ballot Distribution Center (BDC), is proposed to take the responsibility of distributing ballots. The votes and the candidates' names are separated into two parts when the voters cast their votes. The watchdog device records all voting transactions to prevent voter frauds. Our proposed procedure addresses issues related to voter confidentiality, voter frauds, and voting accuracy, thus providing a framework for fair elections.

With the rapid growth of data center networks in providing a myriad of services and applications to store, access, and process data, the power consumption of data center networks has become critically important and must be efficiently managed. In this paper, we discuss the power optimization of data center networks in a hierarchical perspective. We establish a two-level power optimization model to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximum link utilization. After having shown that the addressed problem falls in the class of capacity constraint multi-commodity flow problems, we design several simple heuristic algorithms to solve the problem. Our simulations show that hierarchical energy optimization can effectively save power consumption in data center networks.

This paper focuses on reducing energy consumptions of optical network units (ONUs) in Ethernet passive optical network (EPON). In EPON, optical line terminal (OLT) located at the central office broadcasts the downstream traffic to all ONUs, each of which checks all arrival downstream packets so as to obtain the downstream packets destined to itself. Thus, receivers at ONUs have to always stay in the awake status and consume a large amount of energy. To address the downstream challenge, we propose a novel sleep control scheme which can efficiently put ONU receivers into sleep without extending the standardized EPON MAC protocol. We also theoretically analyze the impacts of different parameters in the sleep control scheme on the delay and energy saving performances by using semi-Markov chains. It is shown that, with proper settings of sleep control parameters, the proposed scheme can save as high as 70% of the ONU receiver energy.

Transmission Control Protocol (TCP) slow start degrades TCP performance under conditions of long-distance and high end-to-end latency, i.e., inherent characteristics of wide area networks (WANs). In this paper, we propose a new TCP slow start algorithm for WANs, called Adaptive Fast Start (AFStart), which incorporates an inline available bandwidth measurement over TCP technique into TCP slow start to set the slow start threshold adaptively and adjusts the congestion window intelligently. The performance of AFStart is evaluated through simulations using the dumb-bell topology and parking-lot topology by applying AFStart to Fast TCP. The simulation results show that AFStart can ramp up the congestion window from its initial value to the slow start threshold more quickly and smoothly than standard slow start, and AFStart achieves higher network link utilization and TCP throughput during the slow start than Fast TCP.

IEEE 802.17 is a standardized ring topology network architecture, called the Resilient Packet Ring (RPR), to be used mainly in metropolitan and wide area networks. The standard calls for establishing rings utilizing uniform links of the same capacity. The impact of using different capacity links on the same ring has not been investigated before from the ring utilization and fairness point of view. This paper investigates supporting non-uniform links in RPR.

In IEEE 802.16 e/m mobile broadband wireless systems, mobile subscriber stations (MSSs) are encouraged to enter the sleep mode for energy saving when end users do not have downstream or upstream traffic. To avoid service disruption of an MSS which stays in the sleep mode, IEEE 802.16 e/m specifies a two-way handshake protocol between the base station (BS) and an MSS. After performing the handshake process, BS is aware of the sleep status of an MSS, and can thus buffer the downstream traffic which arrives when an MSS is asleep. However, the whole handshaking process takes at least four WiMAX frame durations, and the overhead involved may significantly impair the energy saving efficiency. In this paper, we propose a simple sleep control scheme to put an MSS into the sleep mode without performing the handshake between BS and MSSs. Our main idea is to let MSSs monitor and infer its downstream queue status based on the common information owned by both BS and MSSs, and make sleep decisions according to the inferred downstream queue information. We also let BS be aware of the sleep control scheme implemented at MSSs such that BS can accurately infer the status of MSSs without being explicitly notified. Simulation results show that significant power saving can be achieved without degrading the delay performances of MSSs.

ABSTRACT The ever‐increasing malware variants pose serious challenges for traditional signature‐based anti‐virus (AV) scan engines. To effectively handle the scale and magnitude of new malware variants, AV functionality is being moved from the user desktop into the cloud. AV in‐the‐cloud service is becoming the next‐generation security infrastructure designed to defend against virus threats. It provides reliable protection service delivered through data centers worldwide, which are built on virtualization technologies. Nowadays, cloud‐based security services are gaining bullish projections in both consumer and enterprise markets. However, are we getting ready for the cloud evolution? Security vendors are facing various challenges regarding the architectural design, implementation, and validation. Owing to the lack of operation standards among vendors and very few research works conducted up to this point, researchers have no references of AV cloud testing to rely on. In this paper, the architecture of AV in‐the‐cloud service is described. The challenges and solutions are discussed and illustrated by examples taken from our cutting‐edge research on practical applications. Copyright © 2011 John Wiley & Sons, Ltd.

The operation and control of the existing power grid system, which is challenged with rising demands and peak loads, has been considered passive. Congestion is often discovered in high-demand regions, and at locations where abundant renewable energy is generated and injected into the grid; this is attributed to a lack of transmission lines, transfer capability, and transmission capacity. While developing distributed generation (DG) tends to alleviate the traditional congestion problem, employing information and communications technology (ICT) helps manage DG more effectively. ICT involves a vast amount of data to facilitate a broader knowledge of the network status. Data computation and communications are critical elements that can impact the system performance. In this paper, we consider congestion caused by power surpluses produced from households' solar units on rooftops or on ground. Disconnecting some solar units is required to maintain the reliability of the distribution grid. We propose a model for the disconnection process via smart metering communications between smart meters and the utility control center. By modeling the surplus congestion issue as a knapsack problem, we can solve it by proposed greedy solutions. Reduced computation time and data traffic in the network can be achieved.

Power-aware routing in Wireless Sensor Networks (WSNs) is designed to adequately prolong the lifetime of severely resource-constrained ad hoc wireless sensor nodes}. Recent research has identified the energy hole problem in single sink-based WSNs, a characteristic of the many-to-one (convergecast) traffic patterns. In this paper, we propose HYbrid Multi-hop routiNg (HYMN) algorithm, which is a hybrid of the two contemporary multi-hop routing algorithm architectures, namely, flat multi-hop routing that utilizes efficient transmission distances, and hierarchical multi-hop routing algorithms that capitalizes on data aggregation. We provide rigorous mathematical analysis for HYMN-optimize it and model its power consumption. In addition, through extensive simulations, we demonstrate the effective performance of HYMN in terms of superior connectivity.

Although topology control has received much attention in stationary sensor networks by effectively minimizing energy consumption, reducing interference, and shortening end-to-end delay, the transience of mobile nodes in Mobile Ad hoc Networks (MANETs) renders topology control a great challenge. To circumvent the transitory nature of mobile nodes, k-edge connected topology control algorithms have been proposed to construct robust topologies for mobile networks. However, uniformly using the value of k for localized topology control algorithms in any local graph is not effective because nodes move at different speeds. Moreover, the existing k-edge connected topology control algorithms need to determine the value of k a priori, but moving speeds of nodes are unpredictable, and therefore, these algorithms are not practical in MANETs. A dynamic method is proposed in this paper to effectively employ k-edge connected topology control algorithms in MANETs. The proposed method automatically determines the appropriate value of k for each local graph based on local information while ensuring the required connectivity ratio of the whole network. The results show that the dynamic method can enhance the practicality and scalability of existing k-edge connected topology control algorithms while guaranteeing the network connectivity.

Automatic modulation classification (AMC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver; the problem will be more challenging in the multipath fading channel. The proposed AMC method based on optimal decision fusion by using wireless sensor networks provides a more accurate classification result than any one of the individual signal alone. Wireless sensor networks offer increased reliability and optimal decision fusion provides huge gains in overall classification performance as compared to that of the single sensor. Thus, optimal decision fusion based AMC by using wireless sensor networks greatly enhances classification performance of weak signals in non-cooperative communication environment. Classification performances of optimal decision fusion based AMC by using wireless sensor networks in the multipath fading channel are investigated and evaluated in terms of correct classification probability. Through Monte Carlo simulations, we demonstrate that the proposed AMC algorithm can greatly outperform that of single sensor in multipath fading channel.

The Resilient Packet Ring (RPR), defined under IEEE 802.17, has been proposed as a high-speed backbone technology for metropolitan area networks. RPR is introduced to mitigate the underutilization and unfairness problems associated with the current technologies such as SONET and Ethernet. The key performance objectives of RPR are to achieve high bandwidth utilization, optimum spatial reuse on the dual rings, and fairness. The challenge is to design an algorithm that can react dynamically to the traffics in achieving these objectives. The RPR fairness algorithm is comparatively simple, but it poses some critical limitations that require further investigation and remedy. One of the major problems is that the amount of bandwidth allocated by the algorithm oscillates severely under unbalanced traffic scenarios. These oscillations are barrier to achieving spatial reuse and high bandwidth utilization. Moreover, the performance of the RPR fairness algorithm is very sensitive to the algorithm parameters. In this paper, we apply Control Theory to solve the fairness issue, and construct a Proportional controller to dynamically adjust the fair rate in order to eliminate the state of congestion and to converge to the optimal fair rate.

Network security protocols depend mainly on developing cryptographic schemes and using biometric methods. These have led to several security protocols that are unbreakable based on difficulty of solving untractable mathematical problems such as factoring large integers. In this paper, Securing Networks by Employing Encoding and Decoding (SNEED) is developed to mitigate single and multiple link attacks. Network coding and shared capacity among the working paths are used to provide data protection and data integrity against network attackers and eavesdroppers. It is shown that SNEED can be implemented easily where there are k link disjoint paths between two core nodes (routers or switches) in an enterprise network. Finally, SNEED can be incorporated into various applications such as on-demand TV, satellite communications, and multimedia security.

Applicability of the concept of Flow-Aware Networking (FAN) for QoS assurance in wireless networks is studied in the paper. The link utilization efficiency for six versions of the TCP protocol in the FAN environment is investigated and compared. The second contribution of the paper is a new congestion control mechanism for FAN, referred to as RAMAF (Remove and Accept Most Active Flows), that fulfills the wireless link requirements. While in basic FAN, new flows cannot begin transmission in congestion, the new solution ensures short acceptance times of new streaming flows in a router regardless if the outgoing link is congested or not. As compared to other congestion control mechanisms proposed for FAN, RAMAF does not need to tune any parameters, and is thus automatic. Moreover, the proposed solution allows for more effective transmission of elastic flows in comparison to other approaches, and may be used in both wired and wireless FAN. The results of the carefully selected simulation experiments show that TCP NewJersey incorporated with the RAMAF mechanism meets the requirements of the wireless transmission in FAN.

Certificate revocation is an important security component in mobile ad hoc networks (MANETs). Owing to their wireless and dynamic nature, MANETs are vulnerable to security attacks from malicious nodes. Certificate revocation mechanisms play an important role in securing a network. When the certificate of a malicious node is revoked, it is denied from all activities and isolated from the network. The main challenge for certificate revocation is to revoke the certificates of malicious nodes promptly and accurately. In this paper, we build upon our previously proposed scheme, a clustering-based certificate revocation scheme, which outperforms other techniques in terms of being able to quickly revoke attackers' certificates and recover falsely accused certificates. However, owing to a limitation in the scheme's certificate accusation and recovery mechanism, the number of nodes capable of accusing malicious nodes decreases over time. This can eventually lead to the case where malicious nodes can no longer be revoked in a timely manner. To solve this problem, we propose a new method to enhance the effectiveness and efficiency of the scheme by employing a threshold based approach to restore a node's accusation ability and to ensure sufficient normal nodes to accuse malicious nodes in MANETs. Extensive simulations show that the new method can effectively improve the performance of certificate revocation.

Automatic blind modulation classification (MC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver. The proposed MC scheme based on multi-sensor signal fusion makes the premise that the combined signal from multiple sensors provides a more accurate description than any one of the individual signal alone. Multi-sensor signal fusion offers increased reliability and huge gains in overall performance as compared to the single sensor one, thus making automatic modulation classification (AMC) of weak signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-sensor signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-sensor signal fusion, Q-PSK signal SNR estimations using the M2M4 method after multi-sensor signal fusion with 10 sensors versus SNR are also presented. Finally, classification performances based on multi-sensor signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-sensor signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.

TCP Incast, also known as TCP throughput collapse, is a term used to describe a link capacity under-utilization phenomenon in certain many-to-one communication patterns, typically in many datacenter applications. The main root cause of TCP Incast analyzed by prior works is attributed to packet drops at the congestion switch that result in TCP timeout. Congestion control algorithms have been developed to reduce or eliminate packet drops at the congestion switch. In this paper, the performance of Quantized Congestion Notification (QCN) with respect to the TCP incast problem during data access from clustered servers in datacenters are investigated. QCN can effectively control link rates very rapidly in a datacenter environment. However, it performs poorly when TCP Incast is observed. To explain this low link utilization, we examine the rate fluctuation of different flows within one synchronous reading request, and find that the poor performance of TCP throughput with QCN is due to the rate unfairness of different flows. Therefore, an enhanced QCN congestion control algorithm, called fair Quantized Congestion Notification (FQCN), is proposed to improve fairness of multiple flows sharing one bottleneck link. We evaluate the performance of FQCN as compared to that of QCN in terms of fairness and convergence with four simultaneous and eight staggered source flows. As compared to QCN, fairness is improved greatly and the queue length at the bottleneck link converges to the equilibrium queue length very fast. The effects of FQCN to TCP throughput collapse are also investigated. Simulation results show that FQCN significantly enhances TCP throughput performance in a TCP Incast setup.

This paper addresses the orthogonal frequency-division multiple access (OFDMA) resource allocation and wavelength assignment problems in wavelength-division multiplexing (WDM) radio-over-fiber picocellular networks with OFDMA as the wireless modulation and access scheme. We consider the case that the number of WDM wavelengths is limited in which one wavelength is shared among multiple picocells. The set of picocells sharing the same wavelength is referred to as a nanocell in this paper. Since picocells in the same nanocell cannot be allocated with OFDMA resource block (RB) of the same frequency at a time, the intra-nanocell interference is eliminated. However, picocells in different nanocells may be allocated with OFDMA RBs of the same frequency at a time, thus posing interference to each other. To minimize the inter-nanocell interference, OFDMA RBs of the same frequency are assigned to picocells which pose the least interference to each other. However, this may result in some picocells being allocated with a large number of OFDMA RBs, leading to the limited power share received by each OFDMA RB because of the power constraint of the picocell. To minimize the inter-nanocell interference with consideration of the power constraints of picocells, we recast the OFDMA resource allocation and wavelength assignment problems into graph problems, and then propose corresponding solutions.

To lower costs, WDM PONs may share the usage of wavelengths, transmitters, and receivers among ONUs, instead of dedicating the transmission between OLT and each ONU with one individual wavelength and one individual pair of transmitter and receiver. The specific sharing strategy depends on the network architecture and the wavelength supports of transmitters and receivers. Different sharing strategies may yield different achievable data rates of ONUs. In this paper, we make three main contributions in investigating the impact of the sharing strategy on the capacity region of a WDM PON. First, we abstract the data transmission processes in WDM PONs into directed graphs, where arcs represent wavelengths, transmitters, and receivers, as well as relations between them. Second, we apply Ford and Fulkerson's Max-flow, Min-cut Theorem to derive the upper bound of the capacity region of a WDM PON, and prove that the upper bound is achievable. Third, in light of these analytical results, we discuss and compare capacities of some WDM PON architectures.

Owing to the high bandwidth provisioning, hybrid wavelength division multiplexing/time division multiplexing (WDM/TDM) passive optical network (PON) is becoming an attractive future-proof access network solution. In hybrid WDM/TDM PON, tunable lasers are potential candidate light sources attributed to their multiwavelength provisioning capability and color-free property. Currently, the laser tuning time ranges from a few tens of nanoseconds to seconds, or even minutes, depending on the adopted technology. Different laser tuning time may introduce different network performance. To achieve small packet delay and ensure fairness, the schedule length for given optical network unit (ONU) requests is desired to be as short as possible. This paper illustrates contributions in four main aspects. First, we show that both preemptive and nonpreemptive scheduling problems with the objective of minimizing the schedule length are NP-hard when the laser tuning time is nonzero. Second, we present a heuristic preemptive scheduling algorithm with an approximation factor of at most 2 and a heuristic nonpreemptive scheduling algorithm with an approximation factor of at most 2-1/m, wheremis the number of wavelengths. Third, extensive simulations have been conducted, and simulation results show that our proposed algorithms, which consider laser tuning time, achieve significantly better performances as compared to algorithms that are directly derived from existing algorithms without considering laser tuning time. Fourth, since the scheduling in one cycle is related to that in the last cycle, we provide some discussions on the scheduling in multiple cycles.

Owing to the heterogeneity and high degree of connectivity of various networks, there likely exist multiple available paths between a source and a destination. An effective model of delay-controlled load distribution becomes essential to efficiently utilize such parallel paths for multimedia data transmission and real-time applications, which are commonly known to be sensitive to packet delay, packet delay variation, and packet reordering. Recent research on load distribution has focused on load balancing efficiency, bandwidth utilization, and packet order preservation; however, a majority of the solutions do not address delay-related issues. This paper proposes a new load distribution model aiming to minimize the difference among end-to-end delays, thereby reducing packet delay variation and risk of packet reordering without additional network overhead. In general, the lower the risk of packet reordering, the smaller the delay induced by the packet reordering recovery process, i.e., extra delay induced by the packet reordering recovery process is expected to decrease. Therefore, our model can reduce not only the end-to-end delay but also the packet reordering recovery time. Finally, our proposed model is shown to outperform other existing models, via analysis and simulations.

Power aware routing in Wireless Sensor Networks (WSNs) focuses on the crucial issue of extending the network lifetime of WSNs, which are limited by low capacity batteries. However, most of the previously proposed power aware routing algorithms have an inherent problem, which is the isolation of the sink node due to the quick power exhaustion of nodes that are close to the sink. In this paper, we propose a solution, referred to as HYbrid Multi-hop routiNg (HYMN), which addresses this problem by combining two routing strategies, namely flat multi-hop routing and hierarchical multi-hop routing. The former method aims at minimizing the total power consumption in the network while the latter attempts to decrease the amount of transferred data traffic by utilizing data compression. We present a mathematical analysis on the effect of the hybrid location on the performance of HYMN, and also demonstrate the effectiveness of HYMN through extensive simulations.

There have been several approaches to provisioning traffic between core network nodes in Internet Service Provider networks. Such approaches aim to minimize network delay, increase capacity, and enhance security services. MATE (Multipath Adaptive Traffic Engineering) has been proposed for multipath adaptive traffic engineering between an ingress node (source) and an egress node (destination). Its novel idea is to avoid network congestion and attacks that might exist in edge and node disjoint paths between two core network nodes. This paper builds an adaptive, robust, and reliable traffic engineering scheme for better performance and operation of communication networks. This will also provision quality of service (QoS) and protection of traffic engineering to maximize network efficiency. Specifically, we present a new approach, S-MATE (secure MATE) is developed to protect the network traffic between two core nodes (routers, switches, etc.) in a cloud network. S-MATE secures against a single link attack/failure by adding redundancy in one of the operational paths between the sender and receiver. The proposed scheme can be built to secure core networks such as optical and IP networks.

Reliable mesh communications in dense wireless ad hoc networks require the creation of both self organizing mesh structures and mesh routing protocols to accomplish efficient and reliable communications with the added infrastructure redundancy. To date, much of the research in the area has focused on communication protocol design. The investigations often are based on a mesh network structure already fully formed and some times fixed to the underlying physical node topology. Therefore, there is a need for a platform to build mesh networks with structural flexibility and to provide management functions to network- and application-level protocols. In this paper, we propose the distributed diffusion-based mesh (DDM) algorithm for distributed mesh construction that instructs distributed nodes on how to make the desired connections with their neighbors. We accomplish this by introducing the concept of connection rule, which defines allowed connections at each mesh node, combined with a token signal that initiates and controls the structure and boundaries of the resulting mesh. We argue that slight changes in mesh network structure greatly affect network performance and show how the combined use of rule and token signal offers control over the resulting mesh structure. This methodology can be used for cross-layer optimization to achieve a network topology suitable for different network applications. As compared with existing protocols, our algorithm also provides a large reduction in communication overhead.

Recently, much effort aiming at achieving ubiquitous networks has been made. A ubiquitous network refers to a network environment, which enables anytime and anywhere access, by possibly any given device or by any user. In a ubiquitous network, applications require many types of information such as temperature and so forth. A great deal of attention has been paid to aggregate this information in Wireless Sensor Networks (WSNs). A WSN consists of tiny nodes comprising sensing and communication devices. The information sensed by each node is relayed via other nodes in the WSN to the destination node called the ``sink''. One of the most significant challenges pertaining to any WSN is to reduce the energy consumption of its nodes, which run on scarce battery resources. An effective scheme to reduce this energy consumption is to exploit the sink node's mobility, which however presents new challenges to the sink node's routing and information aggregation. In this paper, we propose a new routing and data aggregation scheme based on clustering. Simulation results demonstrate that our scheme can provide better energy efficient data aggregation as compared to the KAT (K-means And Traveling salesman path) mobility.

Driven by emerging bandwidth-hungry applications, next generation passive optical networks (NG-PONs) provide higher bandwidth to users by using more wavelengths and increasing data rates of optical network units (ONUs). On the other hand, for smooth upgrading, NG-PON is desired to be backward compatible with the current TDM PONs where data rates of ONUs remain unchanged. Thus, both high-rate ONUs and low-rate ONUs may coexist in NG-PON. The key parameters of bandwidth allocation in this multi-rate multi-wavelength network include achieving fairness among all ONUs, encouraging low-rate ONUs to increase their data rates, and utilizing wavelength resources efficiently. This paper illustrates contributions in three main aspects. First, we define rate-dependent utilities for ONUs, which serve as the basis for bandwidth arbitration among low-rate and high-rate ONUs. Second, to achieve fairness among ONUs, we employ water-filling idea and formulate a utility max-min fair bandwidth allocation scheme. Third, to efficiently utilize the wavelengths, we map the resource allocation problem in multi-wavelength PON into a multi-processor scheduling problem and employ a heuristic algorithm to address the NP-hard wavelength assignment problem.

In Mobile Ad hoc NETworks (MANETs), certification systems play an important role in maintaining network security because attackers can freely move and repeatedly launch attacks against different nodes. By adopting certification systems, it becomes possible to exclude identified attackers from the network permanently by revoking the certifications of the attackers. A simple way to identify attackers is to collect information on attackers from nodes in the network. However, in this approach, it is difficult to differentiate valid accusations made by legitimate nodes from false accusations made by malicious nodes. In addition, the amount of traffic in order to exchange information on attackers and the necessary time to gather the information increases as the network size becomes larger. In this paper, we propose a certificate revocation scheme which can revoke the certification of attackers in a short time with a small amount of operating traffic. By clustering nodes and introducing multi-level node reliability, the proposed scheme can mitigate the improper certificate revocation due to false accusations by malicious users.

Energy consumption and network connectivity are two of the important research issues that are yet to be resolved in mobile ad hoc networks (MANETs). As taken advantage of in static networks, reliable topology control algorithms are also considered to be a good approach for mobile networks. However, a more adequate evaluation of these algorithms regarding mobility is still needed. In this paper, we evaluate the performance of some well-known topology control algorithms with various scenarios and measurements. The results show that Local Tree-based Reliable Topology (LTRT), a recently proposed algorithm, is the most scalable method and provides the most benefit in terms of redundant connectivity.

With increasing implementation of broadband wireless networks and extensive deployment of multimedia services such as Video on Demand (VoD) or IPTV, the demand for video streaming applications will increase and more people will use their wireless devices to reach numerous video contents available in the Internet. Streaming real-time video in wireless networks is a challenging problem due to the stringent service requirements of video traffic and impairments of wireless channels. Providing the required Quality of Service (QoS) through efficient resource allocation is a complicated problem that service providers are confronting. In this paper, we propose a traffic-aware, cross-layer solution for enhancing the perceived video quality at the end user in wireless networks. Our solution incorporates the characteristics of the MPEG traffic to give more priority to the more important frames and to protect them against dropping when available resources of the network are not sufficient for providing the desired QoS to the traffic flow. It is shown that the proposed solution will improve the perceived video quality over the broadband wireless networks.

While Transmission Control Protocol (TCP) is the most popular transport protocol used in terrestrial networks, its performance is not adequate in wireless networks with long delay, e.g., satellite networks. Though some improvements of TCP and new transport protocols have been proposed, we focus on Variable-structure congestion Control Protocol (VCP) designed for high Bandwidth Delay Product (BDP) networks. In VCP, Explicit Congestion Notification (ECN) is used to generate the feedback signal from routers to sources in order to notify the utilization ratio of a bottleneck link. By adjusting its congestion window according to the network traffic conditions, VCP is able to achieve high link utilization even in high BDP networks. However, VCP requires a long time to fill the link capacity due to its more conservative window control mechanism than the slow start phase in TCP. In addition, throughput is unnecessarily degraded in VCP due to packet losses in wireless environments. In this paper, to address these issues, we propose two extensions of VCP, namely Bandwidth-Independent Start-up Extension (BISE) and Wireless Loss Tolerant Extension (WLTE). BISE can quickly increase its congestion window in the start-up phase. WLTE can maintain high throughput in wireless environments. The performance of the proposed schemes is evaluated through computer simulations. The results demonstrate that the proposed schemes dramatically improve the performance of VCP in the initial phase and also in wireless environments.

The authors study the design of heterogeneous two-tier wireless sensor networks (WSNs), where one tier of nodes is more robust and computationally intensive than the other tier. The authors find the ratios of densities of nodes in each tier to maximise coverage and network lifetime. By employing coverage processes and optimisation theory, the authors show that any topology of WSN derived from random deployments can result in maximum coverage for the given node density and power constraints by satisfying a set of conditions. The authors show that network design in heterogeneous WSNs plays a key role in determining key network performance parameters such as network lifetime. The authors discover a functional relationship between the redundancy, density of nodes in each tier for active coverage and the network lifetime. This relationship is much less pronounced in the absence of heterogeneity. The results of this work can be applied to network design of multi-tier networks and for studying the optimal duty cycles for power saving states for nodes in each tier.

Secure group communication (SGC) has attracted much attention, as group-oriented communications have been increasingly facilitating many emerging applications that require packet delivery from one or more sender(s) to multiple receivers. Of all proposals reported, most have focused on addressing the issue of key management to SGC systems. The authors, however, advocate that security services are also needed to satisfy different security requirements of various applications. The authors also present here a survey on recent advances in several security requirements and security services in group communication systems (GCSs), illustrate some outstanding GCSs that deploy these security services, and describe challenges for any future research works in designing a secure GCS.

By Lingyang Song, Yan Zhang, Nirwan Ansari, Jianwei Huang and Bechir Hamdaoui, Guest Editors Welcome to this special issue of Wiley Journal of Wireless Communications and Mobile Computing (WCMC). The title of this special issue literally adopts the theme of the 2010 International Wireless Communications and Mobile Computing Conference (IWCMC 2010), as it attempts to represent the ‘best’ of IWCMC 2010 by soliciting representative quality research works presented at the conference for inclusion in this issue via a rigorous selection and review process. This special issue covers a quite broad range of topics of wireless networks, wireless communications, and mobile computing, from the physical layer through application and system design. The goal of this special issue is to create a great opportunity for high impact research from both the mobile communications industry and academia to present and discuss new trends, developments, emerging technologies, and new industrial standards. To guarantee the quality, in this special issue, we have selectively collected 11 expanded papers from the proceedings of IWCMC 2010, and clustered them in three groups: three papers dealing with physical layer aspects, six papers investigating MAC and network layer issues, and two papers focusing on applications as well as prototypes. Detailed overview of the selected works is given below. The first group includes three papers, which provide physical layer results for wireless communications and mobile computing. The first paper, by Takeda et al., studies joint transmit and iterative receive frequency-domain equalization for DS-CDMA. In the proposed scheme, simple one-tap frequency-domain equalization at the transmitter and iterative one-tap FDE at the receiver are jointly performed using the common knowledge of channel state information, and at the same time taking channel estimation constraints into account. The paper by Fan et al. investigates the relay position selection problem for the diamond network over Nakagami-m fading channels. This paper discusses the impact on the performance of diamond network caused by the relays' position for a general Nakagami-m fading channel, which extends the previous work for a special Rayleigh fading case, and gives clear restrictions of their positions based on the requirement of the throughput improvement and network stability. The third paper, by Stuber et al., studies outage probability for cooperative diversity with selective combining in cellular networks. The analysis mainly focuses on outage probability for amplify-and-forward and decode-and-forward cooperative diversity systems with selective combining, for the case of a log-normal Nakagami faded desired signal and log-normal Rayleigh faded co-channel interferers. The second group of papers mainly investigates MAC and network layer issues. The first paper, by Wong et al., deals with switching cost minimization in the IEEE 802.16e mobile WiMAX sleep mode operation in order to improve the battery lifetime of the mobile station. The paper proposes a novel approach to resolve this issue by making a heuristic decision during the listening interval to minimize the switching frequency for better energy efficiency. The second paper by Lin et al. studies Multicast Broadcast Service (MBS) zone configuration for wireless multicast and broadcast service. Two schemes, the overlapping scheme and the enhanced overlapping scheme, are provided for more flexible MBS zone configuration to achieve better performance for MBS in terms of QoS and radio resource utilization. The third paper, by Kumar et al., investigates the issue of trust advisory and its establishment in mobile networks, with application to ad hoc networks, including DTNs. The authors utilized encounters in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. The fourth paper by Znati et al. proposes robust multicast routing algorithms for mobile wireless networks by considering more practical challenges, e.g., the mobility of nodes, the tenuous status of communication links, limited resources, and indefinite knowledge of the network topology. This paper addresses these difficulties by providing a framework and architecture with proactive and reactive components to support multicasting to guarantee reliability and efficiency of end-to-end packet delivery. The fifth paper, by Pu et al., redefines the fairness concept regarding the application utility for time-constraint flows and then presents novel utility-based fair bandwidth sharing approaches in vehicular networks. Accordingly, two practical bandwidth-sharing schemes are provided for transferring data by fast-moving wireless nodes such as vehicles in order to guarantee QoS. The sixth paper, by Ali et al., provides a MAC protocol for cognitive wireless sensor body area networks to increases the critical traffic throughput. The proposed cognitive radio based MAC protocol prioritizes the critical packets access to the transmission medium by transmitting them with higher power while transmitting lower priority packets using lower transmission power. The third group consists of two papers focusing on applications and prototypes. The first paper by Fantacci et al. introduces a novel communication infrastructure for emergency management to interconnect several heterogeneous systems and provide multimedia access to groups of people involved in emergency operations as foreseen by the In.Sy.Eme. (Integrated System for Emergency) project. The main scope of the In.Sy.Eme system is to facilitate functional integration of new technologies with actual or off-the-shelf technologies to provide fast responses to any emergency situations and efficient use of all available resources. The second paper, by Manfrin et al., demonstrates the CalRAdio-Based advanced Spectrum Scanner, an open platform developed to monitor the ISM 2.4-2.499 GHz band, and reveals opportunities for a better utilization of the available spectrum resources. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it capitalizes on the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs. In conclusion, this issue of WCMC offers a state-of-the-art view of recent advances in wireless network, wireless communications, and mobile computing. It also offers both academic and industry appeal—the former as a basis toward future research directions and the latter toward viable commercial applications. In the long term, innovative wireless communications and mobile computing techniques will be characterized by their criticalness in consumer, business, and government applications to enhance the development of the whole world in realizing a better future. Finally, we would like to thank all the authors who have submitted their papers for consideration for publishing their work in this issue. We would like to extend our gratitude to the anonymous reviewers who spent much of their precious time reviewing all the papers. Their timely reviews and comments greatly helped us select the best papers in this special issue. We also would like to thank the devoted staff of Wiley for their high level of professionalism, and particularly express our gratitude to the Editor-in-Chief of WCMC, Professor Mohsen Guizani, for his advice, patience, and encouragement from the beginning until the final stage. We hope you will enjoy reading the great selection of papers in this issue.

By taking advantage of the multiple wavelength provisioning capability, WDM PON dramatically enlarges its capacity as compared to TDM PON. The capacity of a WDM PON system depends on many factors, such as the number and capacities of wavelength channels, the network architecture, the wavelength support of receivers, and the tuning ability of lasers. This paper introduces the definition of achievable rate region to describe the capacity of WDM PON and analyzes the achievable rate region for a given network architecture from the perspective of wavelength sharing. This can facilitate comparison of different WDM PON architectures and help design an efficient access control scheme.

The IEEE 802.17 is a standardized ring topology network architecture, called the resilient packet ring (RPR), to be used mainly in metropolitan and wide area networks. This paper focuses on the RPR MAC client implementation of the IEEE 802.17 RPR MAC in the aggressive mode of operation and introduces a new active queue management scheme for ring networks that achieves higher overall utilization of the ring bandwidth with simpler and less expensive implementation than the generic implementation provided in the standard. The scheme introduced in this paper provides performance comparable to the per destination queuing implementation, the best achievable performance.

Customers always complain that anti-virus softwares bog down their computers by consuming much of PC memories and resources. With the popularity and variety of zero- day threats over the Internet, security companies have to keep on inserting new virus signatures into their databases. However, is the increasing size of the signature file the sole reason to drag computers to a crawl during the virus scan? This paper outlines other three reasons for slowing down software-protected computers, which actually are not directly related to the signature file. First, the rising time consumption of de-obfuscating binary payloads by using the emulation technology requires anti-virus softwares take more time to scan a packed file than an unpacked file. Second, new technology file system causes self-similarity in file index searching and data block accessing. Even if file sizes fit the log-normal distribution, there are still many "spikes" of high virus-scanning latency which cannot be ignored. Last but not least, temporal changes in file size, file type, and storage capacity in modern operation systems are slowing down virus scan. The paper also discusses the cloud-based security infrastructure for deploying a light-weight and fast anti-virus products.

This special issue includes a collection of 19 outstanding research papers which cover a diversity of topics on the application of network coding in wireless communication networks.

Extensive efforts have been focused on deploying broadband wireless networks. Providing mobile users with high speed network connectivity will let them run various multimedia applications on their wireless devices. Satisfying users with different quality-of-service requirements while optimizing resource allocation is a challenging problem. In this paper, we discuss the challenges and possible solutions for transmitting MPEG video streams over WiMAX networks. We will briefly describe the MPEG traffic model suggested by the WiMAX Forum. A cross-layer solution for enhancing the performance of WiMAX networks with respect to MPEG video streaming applications is explained. Our solution uses the characteristics of MPEG traffic to give priority to the more important frames and protect them against dropping. Besides, it is simple and compatible with the IEEE 802.16 standards and thus easily deployable. It is shown that the proposed solutions will improve the video quality over WiMAX networks.

Application layer multicast (ALM) is highly expected to replace IP multicasting as the new technological choice for content delivery. Depending on the streaming application, ALM nodes will construct a multicast tree and deliver the stream through this tree. However, if a node resides in the tree leaves, it cannot deliver the stream to its descendant nodes. In this case, quality of service (QoS) will be compromised dramatically. To overcome this problem, topology-aware hierarchical arrangement graph (THAG) was proposed. By employing multiple description coding (MDC), THAG first splits the stream into a number of descriptions, and then uses arrangement graph (AG) to construct node-disjoint multicast trees for each description. However, using a constant AG size in THAG creates difficulty in delivering descriptions appropriately across a heterogeneous network. In this paper, we propose a method, referred to as network-aware hierarchical arrangement graph (NHAG), to change the AG size dynamically to enhance THAG performance, even in heterogeneous networks. Finally, we evaluate the proposed scheme by experiments using the network simulator ns-2. By comparing our proposed method to THAG and SplitStream, we show that our method provides better performance in terms of throughput and QoS. The results indicate that our approach is more reliable than other methods in heterogeneous networks.

During the last several years, the Internet has evolved from a wired infrastructure to a hybrid of wired and wireless domains by spreading worldwide interoperability for microwave access (WiMAX), Wi-Fi, and cellular networks. Therefore, there is a growing need to facilitate reliable content delivery over such heterogeneous networks. On the other hand, application layer multicast (ALM) has become a promising approach for streaming media content from a server to a large number of interested nodes. ALM nodes construct a multicast tree and deliver the stream through this tree. However, if a node leaves, it cannot deliver the stream to its descendant nodes. In this case, quality-of-service (QoS) is compromised dramatically. Especially, this problem is exacerbated in wireless networks because of packet errors and handovers. In order to cope with this problem, multiple-tree multicasts have been proposed. However, existing methods fail to deliver contents reliably in combined wired and wireless networks. In this paper, we propose a method to ensure the robustness of node departure, while meeting various bandwidth constraints by using layered multiple description coding (LMDC). Finally, we evaluate the proposed method via extensive simulations by using the network simulator (ns-2). By comparing our proposed method with the existing ones, we demonstrate that our method provides better performance in terms of total throughput, relative delay penalty (RDP), and relative delay variation (RDV). The results indicate that our approach is a more reliable content delivery system when compared with contemporary methods in the context of heterogeneous networks containing wired and wireless environments.

Broadcasting, in the context of ad-hoc networks, is a costly operation, and thus topology control has been proposed to achieve efficient broadcasting with low interference and low energy consumption. By topology control, each node optimizes its transmission power by maintaining network connectivity in a localized manner. Local Minimum Spanning Tree (LMST) is the state-of-the-art topology control algorithm, which has been proven to provide satisfactory performance. However, LMST almost always results in a 1-connected network, without redundancy to tolerate external factors. In this paper, we propose Local Tree-based Reliable Topology (LTRT), which is mathematically proven to guarantee k-edge connectivity while preserving the features of LMST. LTRT can be easily constructed with a low computational complexity of O(k(m + n log n)), where k is the connectivity of the resulting topology, n is the number of neighboring nodes, and m is the number of edges. Simulation results have demonstrated the efficiency of LTRT and its superiority over other localized algorithms.

Popularity of voice over IP (VoIP) applications such as Skype, Google Talk, and MSN Messenger along with emerging deployment of WiMAX networks is making VoIP over WiMAX an attractive market and a driving force for both carriers and equipment suppliers in capturing and spurring the next wave of telecommunications innovation, though challenges remain. Optimization of the VoIP call capacity over WiMAX networks is one such crucial challenge and remains an open research issue. While conventional scheduling methods have not considered the traffic characteristics of VoIP, in this paper, we propose a traffic aware scheduling algorithm for VoIP applications in WiMAX networks. We study the performance of our proposed method and compare it with that of some conventional methods. The tradeoff between delay and bandwidth efficiency is discussed, and it is shown that using our scheduling method enhances the efficiency of VoIP over WiMAX.

Recent advances in solid state and packaging technologies have enabled production of more efficient and reasonably small devices such as micro electro mechanical systems (MEMS). Wireless sensor networks (WSNs) can gather data from sensor nodes and are now at the practical stage of realizations because of the above advances. Conventional researches have mainly focused on extending the lifetime of WSNs because sensor nodes are only equipped with small-capacity batteries. Mobile ubiquitous LAN extension (MULE) is one of the approaches to meet such demand, and it can gather data from isolated nodes. The KAT mobility scheme is one of the mobility schemes on MULE focusing on the efficiency. Therefore, this scheme is expected to prolong the lifetime of the network. However, this scheme cannot ensure that the mobile sinks can gather the data from all of the nodes. In this paper, we focus on the fairness issue of data gathered by the mobile sinks while also considering the efficiency of data gathering. We propose a new mobility scheme based on a new clustering method and the set cover algorithm to ensure that the mobile sinks can gather data from all of the nodes, and simulation results show that fairness of data gathering by the proposed mobility scheme is greatly improved as compared to conventional KAT mobility scheme.

Load-balanced switches have received much attention because they are more scalable than other switch architectures. However, a load-balanced switch has the problem of packet mis-sequencing. In this paper, we propose a dynamic mailbox sharing (DMS) scheme to eliminate the mis-sequencing problem of load-balanced switches only at the cost of a very small increase of delay. The key idea is to keep packets of the same flow in order in the load-balanced switch. The DMS scheme is based on two statistical facts in operational networks: the number of simultaneous active flows in the router buffer is far less than that of in-progress flows, and most of the intra-flow packet intervals are longer than the packet delay in the high speed router. In DMS, the packet sequence of the same flow arrived in the input ports is recorded in the mailbox maintained in the output ports. Then, packets of the same flow are delivered according to the order of their arrivals. The mailbox becomes the bottleneck in order to accommodate a large number of flows. We thus propose a dynamic sharing scheme to alleviate the bottleneck and greatly enhance the scalability of the mailbox. By simulations using the real internet traffic traces, we show that with a simple flow splitter mechanism restraining mis-sequencing, the average packet delay using DMS is considerably lower than that of other schemes including uniform frame spreading, padded frame and the CR switch, and it is close to the ideal case without re-sequencing even when the load is very high. The results also demonstrate that the size of mailbox is in the hundreds.

Pulsing Denial-of-Service (PDoS) attacks seriously degrade the throughput of TCP flows and consequently pose a grave detrimental effect on network performance. The fact that they generate less traffic than traditional flood-based attacks makes PDoS detection more difficult. Most of the conventional PDoS detection shemes focus on the periodical pattern of the pulse trains. Therefore, attackers can easily escape the detection system by merely controlling the timing of pulse transmission. In this paper, we propose a novel and robust PDoS detection method which capitalizes on the bandwidth usage condition of network traffic in distinguishing the congestion due to normal traffic from that due to PDoS attacks. Simulation experiments have demonstrated the effectiveness of the proposed scheme in detecting PDoS attacks.

Non-linear predictor-based dynamic bandwidth allocation (NLPDBA) schemes for improving the time division multiplexed passive optical networks (TDM-PONs) upstream transmission efficiency have been investigated in an ad hoc manner. In this paper, we establish a general state space model to analyze the stability of the NLPDBA schemes from the TDM-PON system's point of view, and propose controller design guidelines to maintain the system stability under different scenarios. We prove that a TDM-PON system with NLPDBA is stable by proper pole placements as the traffic changes. Our analysis suggests a straightforward framework for designing and applying the NLPDBA scheme for TDM-PONs that ensures stability.

Data confidentiality is one of the most important concerns in security of ad-hoc networks which have been widely studied in recent years. In this paper, we consider the public-key cryptography which is one of the simplest and viable means to maintain data confidentiality. There are several ways to distribute a public key. Flooding is an intuitive approach to distribute each node’s public key. However, the normal flooding approach is costly, and can cause MAC-level contention in a dense region of nodes. Tree based topology flooding can be appied to mitigate these problems. The construction algorithm should use ideally only local information. In this paper, we propose a completely localized algorithm called the Local Tree-based Reliable Topology (LTRT) algorithm, which achieves both reliability and efficiency. LTRT is a localized version of TRT that has 2-edge connectivity. Each node can distribute its public key to all other nodes in the network by LTRT. Simulation results show the efficiency of LTRT and its superiority over other localized algorithms.

Abstract Low rate TCP DoS (Shrew) and reduction of quality (RoQ) attacks have been proven to be detrimental to TCP traffic in the Internet. In this paper, we investigate the effect of these attacks on QoS sensitive, real time UDP traffic, like VoIP. Both of these attacks are difficult to detect, as the average attack traffic is low. These stealthy RoQ attacks are especially hard to detect as their periodicity is not well‐defined, and the attacks can contain spoofed source as well as destination IP addresses. Extensive ns2 simulations have been performed under realistic scenarios to validate the effects of these attacks on real time VoIP traffic using the G711 and G729a audio codecs. Forward Error Correction (FEC) is also considered in our analysis to recover packets from the packet loss caused by attacks. Without FEC, these attacks can drastically reduce the quality of VoIP traffic, leading to denial of service. To evaluate the perceptual quality of voice under attacks, the network losses are mapped to user's quality perception by using the ITU E‐model. The MOS range of the E‐model decreases frombesttomediumdue to the low rate TCP DoS and RoQ attacks even when FEC is deployed. In some of the attack scenarios, the packet losses are bursty, which can lead to dropped calls. In addition, the stealthy RoQ attacks with higher burst lengths are as effective as the shrew attack in impairing VoIP traffic. We have also evaluated the effects of the attack on the VoIP traffic on the DETERLAB testbed, which has specifically been built for conducting network security research, and the results are consistent with the ns2 simulation results. We believe our investigation is important to remove the misconception about low rate TCP DoS and RoQ attacks that they affect only TCP traffic in the Internet. Copyright © 2008 John Wiley & Sons, Ltd.

Few works have been reported to address the issue of updating link state information in order to effectively facilitate quality-of-service (QoS) routing. The idea of modeling the QoS link state information as random variables has been reported, but none of the existing works have provided a comprehensive probabilistic approach to link state update that takes the probability density functions of both the user's QoS requirements and the network's QoS measurements into account. We propose the routing-oriented update scheme (ROSE) that utilizes the knowledge of the history of network operations and user's QoS requirements to improve the efficiency of link state update without increasing the network overhead. ROSE is a new class-based link state update scheme which intelligently determines class sizes to minimize the impact of inaccurate link state information. Through theoretical analysis and extensive simulations, we demonstrate that ROSE outperforms other class- based link state update policies.

Recently, among various data hiding techniques, a new subset, lossless data hiding, has received increasing interest. Most of the existing lossless data hiding algorithms are, however, fragile in the sense that the hidden data cannot be extracted out correctly after compression or other incidental alteration has been applied to the stego-image. The only existing semi-fragile (referred to as robust in this paper) lossless data hiding technique, which is robust against high-quality JPEG compression, is based on modulo-256 addition to achieve losslessness. In this paper, we first point out that this technique has suffered from the annoying salt-and-pepper noise caused by using modulo-256 addition to prevent overflow/underflow. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. By identifying a robust statistical quantity based on the patchwork theory and employing it to embed data, differentiating the bit-embedding process based on the pixel group's distribution characteristics, and using error correction codes and permutation scheme, this technique has achieved both losslessness and robustness. It has been successfully applied to many images, thus demonstrating its generality. The experimental results show that the high visual quality of stego-images, the data embedding capacity, and the robustness of the proposed lossless data hiding scheme against compression are acceptable for many applications, including semi-fragile image authentication. Specifically, it has been successfully applied to authenticate losslessly compressed JPEG2000 images, followed by possible transcoding. It is expected that this new robust lossless data hiding algorithm can be readily applied in the medical field, law enforcement, remote sensing and other areas, where the recovery of original images is desired.

Intervehicle communication is a key technique of intelligent transport systems. Ad hoc networking in the vehicular environment was investigated intensively. This paper proposes a new clustering technique for large multihop vehicular ad hoc networks. The cluster structure is determined by the geographic position of nodes and the priorities associated with the vehicle traffic information. Each cluster elects one node as its cluster head. The cluster size is controlled by a predefined maximum distance between a cluster head and its members. Clusters are independently controlled and dynamically reconfigured as nodes move. This paper presents the stability of the proposed cluster structure, and communication overhead for maintaining the structure and connectivity in an application context. The simulation is performed with comparative studies using CORSIM and NS-2 simulators.

The ocean, which covers about two-third of the Earth surface, is a largely unexplored world that has fascinated humans since the beginning of human history. Over a long period of time, there is a great interest in exploring the ocean and other underwater environments (e.g., rivers, lakes, and reservoirs) for scientific, environmental, commercial, and military purposes. With the increasing demand for acquiring localized, precise and real-time knowledge of the harsh underwater environments, traditional underwater exploration technologies such as SONAR or other remote sensing technologies can no longer meet such demands. Underwater sensor networks are an emerging network paradigm which provides a promising solution to exploring the ocean and underwater environments. An underwater sensor network consists of a number of underwater sensor nodes with sensing, data processing, and communication capabilities, which are deployed in a region of interest and collaborate to accomplish a common task such as underwater environmental monitoring, mine reconnaissance, and military surveillance. Driven by a broad range of potential applications in both civilian and military areas as well as rapid technological advances in microelectronics, wireless communications, and embedded processing, underwater sensor networks have recently received much attention from both academia and industry. Distinct from terrestrial sensor networks, an underwater sensor network has some unique characteristics that need to be particularly addressed such as low communication bandwidth, large propagation delay, harsh geographical environment, and floating node mobility. These unique characteristics present many challenges in the design of underwater sensor networks, which have recently motivated a growing interest and a considerable amount of research activities in this emerging area. This special issue includes a collection of eight outstanding research papers, which cover a diversity of topics on the design of network architectures and protocols for underwater sensor networks. The issue begins with an invited paper, ‘Prospects and Problems of Wireless Communication for Underwater Sensor Networks,’ contributed by Jun-Hong Cui et al. This paper reviews the physical fundamentals and engineering implementations for efficient information exchange via wireless communications using physical waves as the carrier among nodes in an underwater sensor network. It also makes recommendations for the selection of the communication carrier for underwater sensor networks with engineering countermeasures that can possibly enhance the communication efficiency in specified underwater environments. In the second paper, ‘Coverage and Connectivity in Three-Dimensional Underwater Sensor Networks,’ Alam and Haas studied the node deployment problem in a 3D underwater sensor network and provided a solution to the coverage and connectivity problem with limited and full communication redundancy requirements. In the third paper, ‘Placement of Multiple Mobile Data Collectors in Underwater Acoustic Sensor Networks,’ Alsalih et al. studied the placement problem of mobile data collectors in underwater sensor networks and proposed two routing and placement schemes. One is delay-tolerant placement and routing (DTPR), which can maximize the network lifetime without any delay consideration. The other is delay-constrained placement and routing (DCPR), which can maximize the network lifetime with an upper bound on the maximum delay. The fourth paper, ‘Target Tracking Based on a Distributed Particle Filter in Underwater Sensor Networks,’ by Huang et al. proposes two algorithms for tracking mobile targets in cluster-based underwater sensor networks based on a distributed particle filter. One of them can achieve higher tracking accuracy while the other can significantly reduce the communication cost, energy cost, and tracking response time. In the fifth paper, ‘Utilizing Acoustic Propagation Delay to Design MAC Protocols for Underwater Wireless Sensor Networks,’ Guo et al. proposed an efficient MAC protocol for underwater sensor networks, which makes use of the propagation delay to avoid collisions, thus reducing control overhead and energy consumption. In the sixth paper, ‘Path Unaware Layered Routing Protocol (PULRP) With Non-Uniform Node Distribution for Underwater Sensor Networks,’ Gopi et al. proposed a PULRP for 2D underwater sensor networks with mobile nodes, which has been demonstrated to have better throughput and delay performance as compared to the underwater diffusion (UWD) algorithm. In the seventh paper, ‘PAS: Probability and Sub-Optimal Distance (SOD)-Based Lifetime Prolonging Strategy for Underwater Acoustic Sensor Networks,’ Dou et al. proposed a couple of lifetime prolonging strategies for underwater sensor networks: probability-based energy-balancing (PEB) strategy and SOD-based data transmission strategy. They showed through simulation results that both strategies can efficiently save energy consumption and thus prolong the network lifetime. In the last paper, ‘Development of Routing Protocols for the Solar-Powered Autonomous Underwater Vehicle (SAUV) Platform,’ Bartos et al. presented a summary of the experience obtained in the development, evaluation, and field testing of two routing protocols for the SAUV platform. Useful suggestions based on field experience are also presented for improving the design and evaluation of routing protocols for a harsh underwater environment. We thank all the authors who submitted their papers to this special issue. Owing to the limitation of space, we can include only eight papers in the issue. We are grateful to all the reviewers for their time and efforts in carefully reviewing all the papers and providing valuable review comments. We also thank the Editor-in-Chief, Mohsen Guizani, for his continuous support for this special issue, and all the publication staff for their support during the publishing process. It is our hope that the papers included in this special issue present a good snapshot of the latest research progress in the design of network architectures and protocols for underwater sensor networks and become an important reference for researchers and practitioners in the area. Finally, we hope that the readers will find this special issue timely and informative.

With the general trend towards ubiquitous access to networks, more users will prefer to make voice calls through the Internet. Voice over IP (VoIP) as the application which facilitates voice calls through the Internet will increasingly occupy more traffic. The growth of delay sensitive traffic that requires special quality of service from the network will impose new constraints on network designers who should wisely allocate the limited resources to users based on their required quality of service. An efficient resource allocation depends upon gaining accurate information about the traffic profile of user applications. In this paper, we have studied the access level traffic profile of VoIP applications and proposed a realistic distribution model for VoIP traffic. Based on our model, we have introduced an algorithm for resource allocation in networks. It is shown that using our algorithm will enhance the delay and utilization performance of the network.

Application Layer Multicast (ALM) is highly expected to be the new technological choice contents delivery in lieu of IP multicast. Depending on each node's streaming application, ALM constructs multicast trees and delivers the stream through those trees. The problem of ALM is that when a node resides in tree leaves, the stream cannot be delivered to descendant nodes. To overcome this problem, Topology-aware Hierarchical Arrangement Graph (THAG) was proposed. By employing Multiple Description Coding (MDC), THAG first splits the stream into a number of sub-streams, and then uses Arrangement Graph (AG) to construct an independent tree for each sub-stream. However, using the same size of AG in THAG has a difficulty delivering a stream appropriately across a heterogeneous network. In this paper, we propose a method to change the size of AG dynamically in enhancing THAG performance well even in a heterogeneous network. Finally, we evaluate the proposed scheme by experiments in ns -2. By comparing with THAG, we show that our proposal scheme provides a better performance in throughput and Bandwidth Satisfaction Rate (BSR).

As data rates in the Internet increase, the Internet Protocol (IP) address lookup is required to be resolved in shorter resolution times. IP address lookup involves finding the longest matching prefix from a database of prefixes that better matches the destination address of a packet. The fastest IP-address lookup solutions are based on ternary content addressable memories (TCAMs), which can resolve the IP lookup in one memory-access time. However, TCAMs have a high power consumption and large complexity that may limit their scalability and storage capacity. An alternative is to use random access memory (RAM) that stores a forwarding table in a trie form. Proposed trie-based solutions for IP lookup require three or more memory-access times in the worst-case scenario. This makes them unattractive despite their reduced power consumption. In this paper, we propose a flexible and fast trie-based IP-lookup algorithm where parallel searching is performed. This algorithm performs lookup in two memory- access times whith a feasible amount of memory or three memory access times with reduced memory.

We analyze the dynamic bandwidth allocation (DBA) scheme over Ethernet passive optical networks (EPONs). Our focus is on the non-linear predictor-based DBA (NLPDBA), which outperforms other existing schemes with higher bandwidth utilization and shorter packet delay. We establish a system model to evaluate the controllability of NLPDBA. Analytical results show that NLPDBA maintains the EPON system controllability even when the loaded network traffic changes drastically. Our analysis suggests a straightforward framework to design the DBA scheme that would enable a completely controllable access network.

Attacks against encrypted protocols are becoming increasingly popular. They pose a serious challenge to the conventional intrusion detection systems (IDSs) which heavily rely on inspecting the network packet fields and are consequently unable to monitor encrypted sessions. IDSs can be broadly categorized into two types: signature-based and anomaly-based IDSs. The signature-based IDSs rely on previous attack signatures but are often ineffective against new attacks. On the other hand, anomaly-based detection systems depend on detecting the change in the protocol behavior caused by an attack. The latter can be employed to detect novel attacks, and therefore are often preferred over their signature-based counterpart. In this paper, we envision an anomaly-based IDS which can detect attacks against popular encrypted protocols, such as SSH and SSL. The proposed system creates a normal behavior profile and uses non-parametric Cusum algorithm to detect deviation from the normal profile. Upon detecting an anomaly, the proposed mechanism generates an alert, sets a delay to the protocol response, and traces back the attacker. The effectiveness of the proposed detection scheme is verified via simulations.

Epidemic worms has become a social problem owing to their potency in paralyzing the Internet, thus affecting our way of life. Recent researches have pointed out that epidemic worms can propagate similar payloads rapidly. It was shown that it is possible to evaluate similarities between these payloads in terms of a 256-dimensional vector based on histograms of the appearance frequencies of 256 character codes. This observation has also been confirmed by our earlier works. However, this method, if applied to flows from only one network, which means a network managed by an independent organization, is prone to a high rate of false positives in cases such as when normal emails are sent through a mailing list. To overcome this problem, we propose a new scheme which checks for any similarity between flows detected at several IDSs in a distributed environment. The proposed scheme is based on the fact that normal payloads propagating from different networks are different, whereas in the case of epidemic worms payloads even propagated through different networks but generated by the same worm exhibit similarity. We have demonstrated the effectiveness of the proposed scheme through extensive experiments using real network traffic that contains worms."

The IEEE 802.17 is a standardized ring topology network architecture, called the resilient packet ring (RPR), to be used mainly in metropolitan and wide area networks. This paper presents an overview, and focuses on the weighted fairness aspects of IEEE 802.17 RPR (in the aggressive mode of operation) by showing performance evaluation results and suggesting improvements. These aspects have not been well studied until now, and can be used to alleviate some of the issues observed in the fairness algorithm under some scenarios.

Low rate DoS attacks are emerging threats to the TCP traffic, and the VoIP traffic in the Internet. They are hard to detect as they intelligently send attack traffic inside the network to evade current router based congestion control mechanisms. We propose a practical attack model in which botnets that can pose a serious threat to the Internet are considered. Under this model, an attacker can scatter bots across the Internet to launch the low rate DoS attack, thus essentially orchestrating the low rate DoS attack that uses random and continuous IP address spoofing, but with valid legitimate IP addresses. It is difficult to detect and mitigate such an attack. We propose a low rate DoS attack detection algorithm, which relies on the core characteristic of the low rate DoS attack in introducing high rate traffic for short periods, and then uses a proactive test based differentiation technique to filter the attack packets. The proactive test was originally proposed to defend DDoS attacks and low rate DoS attacks which tend to ignore the normal operation of network protocols, but it is tailored here to differentiate the legitimate traffic from the low rate DoS attack traffic instigated by botnets. It leverages on the conformity of legitimate flows, which obey the network protocols. It mainly differentiates legitimate connections by checking their responses to the proactive tests which include puzzles for distinguishing botnets from human users. We finally evaluate and demonstrate the performance of the proposed low rate DoS attack detection and mitigation algorithm on the real Internet traces.

Users in next generation wireless networks are expected to be highly dynamic while maintaining connectivity through different devices with different processing and communication capabilities. In wireless environments, bandwidth is scarce and channel conditions are time-varying. To guarantee quality of service (QoS) to users roaming between heterogeneous wireless networks, a dynamic QoS negotiation mechanism, which allows users to dynamically negotiate their service-levels with the network, is required. Several protocols for dynamic service level negotiation have been proposed, each focusing on a particular mode. This paper presents an overview of these protocols and discusses their limitations. To alleviate these shortcomings, a dynamic QoS negotiation scheme to allow users to change their service levels in response to changes in both network conditions and their own resource requirements is proposed. In the proposed scheme, upon an intra-domain handoff of a mobile node, the visited access point consults the previously used access point to confirm the legitimacy of the service negotiation request issued by the mobile node. The performance of the proposed scheme has been investigated and compared with other dynamic negotiation approaches. It was demonstrated that the proposed scheme outperforms the state-of-the-art method, in terms of the signaling overhead and data storage, at the expense of a slight increase in the overall negotiation delay.

To provision the enhanced support for wireless Internet services, high speed downlink packet access (HSDPA) was standardized in 3GPP. It offers peak air interface data rates of up to 14.4 Mbps with 5 Mhz bandwidth, thus dramatically improving the existing packet service capability of UMTS R99. At the same time, Evolution-Data Optimized (EV-DO) has been standardized in 3GPP2 to provide up to 4.9 Mbps throughput with 1.25 Mhz bandwidth. However, because of inter-cell interferences, users in the cell edge area generally cannot enjoy the higher data rate supported by the standard. Moreover, to meet the system fairness criterion, a base station usually needs to allocate un-proportional radio resource to these users. In this paper, we propose a simple interference mitigation scheme to enhance cell edge user data rate through network coordination. It is demonstrated through extensive simulations that the scheme significantly improves cell edge user throughput without lowering the total system capacity and spectral efficiency.

Explicit endpoint admission control with service vector (EEAC-SV) is a solution that allows a multi-hop connection in a Diffserv network to utilize different service classes in different routers along the path, thus enabling the network to provide QoS with fine granularity and improved network utilization. EEAC-SV relies on the QoS information in the pre-marked probing packet to select the optimal service vector. However, owing to the overhead constraint, the QoS information in the probing packet can only be conveyed in a quantized manner. The mathematical model indicates that with the same amount of overhead, the performance of EEAC-SV can be optimized with the proper packet marking strategy. In this paper, we propose the smart packet marking strategy to improve the EEAC-SV performance in terms of reducing the false routing probability. Smart packet marking is a stochastic solution which, based on the knowledge of the history of network QoS performances and user behaviors, defines the boundaries of each quantization level in such a way to improve the routing accuracy. Through extensive simulations, we demonstrate that smart packet marking outperforms the packet marking strategy that does not take stochastic information into consideration.

This paper addresses the issue of bandwidth allocation over Ethernet passive optical networks (EPONs). We develop a state space model to evaluate the controllability of available bandwidth allocation schemes. We show that the predictor-based scheme is completely controllable when the loaded network traffic changes dynamically, while other schemes are either partially controllable or uncontrollable.

Many works have been reported to address the issue of updating link state information in order to effectively facilitate Quality-of-Service (QoS) routing. However, most of them, if not all, only consider concave metrics, e.g., bandwidth. In this paper, we first observe that due to the inherently different nature of additive and concave QoS metrics, directly applying existing link state update policies cannot provide satisfactory performance. As such, it is essential to consider the additive metrics of link state update for the purpose of reducing the protocol overhead and improving the accuracy of link state information. By applying the central limit theorem, the additive QoS constraint imposed on each link can be modeled as a normal random variable. Then, based on our previous proposal, ROSE, we present a high performance link state update policy, ROSE II. Via theoretical analysis and extensive simulations, we show that ROSE II greatly outperforms the state of the arts in terms of protocol overhead and the accuracy of the link state information for additive metrics.

The work presented in this paper1 focuses on a new approach in provisioning Quality of Service (QoS) in ad hoc wireless networks, aiming at making the best use of ad hoc networking as a candidate technology for next generation wireless networks. Specifically, a cross-layer QoS provisioning architecture for wireless ad hoc networks is introduced and described, based on the integration of the recently proposed service vector concept at the network layer and a delay bounded power efficient scheduling at the data link layer. It is demonstrated through modeling and simulations that this novel architecture can provide considerable performance improvements in terms of both power savings and enhanced QoS granularity in wireless ad hoc networks. Furthermore, the performance of the proposed scheme under various traffic arrival rates and distributions is evaluated.

The low rate TCP DoS (Shrew) and the reduction of quality (RoQ) attacks have been proved to be detrimental to the TCP traffic in the Internet. In this paper, we investigate the effect of these attacks on the QoS sensitive real time UDP traffic like VoIP. The stealthy RoQ attacks in particular are hard to detect as its periodicity is not well defined as compared to the shrew attack, and they can be launched even with the destination IP address spoofed! The attack potency formula is tailored for our work to observe the detrimental effect of the attack on the VoIP traffic. We study the effect of the attack flow on three QoS parameters of a VoIP flow, namely, the delay, the jitter, and the packet loss. We have performed extensive ns2 simulations under realistic scenarios to validate the effect of these attacks on the real time UDP traffic. Our results indicate that both these attacks are capable of driving a VoIP call from good to acceptable quality, i.e., reduction of quality, and from acceptable quality to complete denial of service. We believe that it is critical to investigate the characteristics of any attack in order to develop sound mitigation mechanisms; this work highlights one such characteristic of the low rate DoS attack.

Finding a least-cost path subject to a delay constraint in a network is an NP-complete problem and has been extensively studied. Many works reported in the literature tackle this problem by using /spl epsiv/-approximation schemes and scaling techniques, i.e., by mapping link costs into integers or at least discrete numbers, a solution which satisfies the delay constraint and has a cost within a factor of the optimal one, that can be computed with pseudopolynomial computational complexity. In this paper, having observed that the computational complexities of the /spl epsiv/-approximation algorithms using the linear scaling technique are linearly proportional to the linear scaling factor, we investigate the issue of finding the optimal (the smallest) linear scaling factor to reduce the computational complexities, and propose two algorithms, the optimal linear scaling algorithm (OLSA) and the transformed OLSA. We analytically show that the computational complexities of our proposed algorithms are very low, as compared with those of /spl epsiv/-approximation algorithms. Therefore, incorporating the two algorithms can enhance the /spl epsiv/-approximation algorithms by granting them a practically important capability: self-adaptively picking the optimal linear scaling factors in different networks. As such, /spl epsiv/-approximation algorithms become more flexible and efficient.

Core-stateless scheduling algorithms have been proposed in the literature to overcome the scalability problem of the stateful approach. Instead of maintaining per-How information or performing per-packet How classification at core routers, packets are scheduled according to the information (time stamps) carried in their headers. They can hence provision quality of service (QoS) and achieve high scalability. In this paper, which came from our observation that it is more convenient to evaluate a packet's delay in a core-stateless network with reference to its time stamp than to the real time, we propose a new traffic model and derive its properties. Based on this model, a novel time-stamp encoding scheme, which is theoretically proven to be able to minimize the end-to-end worst case delay in a core-stateless network, is presented. With our proposed traffic model, performance analysis in core-stateless networks becomes straightforward.

A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

Abstract Emerging real‐time communications and multimedia applications necessitate the provisioning of Quality of Service (QoS) in Internet. Recently, a new concept, referred to asservice vector, has been introduced to enhance the end‐to‐end QoS granularity, and at the same time, maintain the simplicity and scalability feature of the current differentiated services (DiffServ) networks. This work extends this concept to wireless ad hoc networks and proposes a cross‐layer architecture based on the combination of delay‐bounded wireless link level scheduling and the network layer service vector concept, resulting in significant power savings and finer end‐to‐end QoS granularity. The impact of various traffic arrival distributions and flows with different QoS requirements on the performance of this cross‐layer architecture is also investigated and evaluated. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we show that routing without considering the staleness of link state information introduced by update policies may generate significant percentage of false routing. Hence, we introduce and investigate the issue of minimizing the impact of stale link state information on the performance of QoS routing without stochastic link state knowledge. Under the assumption that trigger-based link state policies are adopted for updating link state information, we theoretically decouple the problem of finding the most probable feasible path (without link state stochastic knowledge) to the problems of finding the multiple additively constrained path (MACP) and finding the least cost multiple additively constrained path (LCMACP), respectively, and propose a framework for minimizing the impact of stale link state information on the performance of QoS routing. We show by theoretical analysis and extensive simulations that our proposed framework is effective in minimizing the undesirable effect of the staleness of link state information.

A novel mechanism based on probabilistic packet marking (PPM) for IP traceback is presented. Our proposal enhances the performance of PPM in the following aspects. First, PPM can effectively trace denial of service (DoS) attacks and small-scale distributed DoS (DDoS) attacks only while our proposal may also be used to tackle large-scale DDoS attacks. Second, our scheme eliminates a serious vulnerability of PPM, i.e., spoofed marking inscribed by the attacker intentionally. Third, by optimizing the marking probability and refining the marking mechanism, our scheme can significantly reduce the number of packets required for path reconstruction. In comparison with PPM, as many as 41.31% of marked packets required for a single path reconstruction may be reduced using our scheme.

TCP performance over wireless networks is critical as the Internet gradually expands to the wireless territory. Traditional TCP schemes have been proven to be inefficient for the wireless network, as they are designed and optimized for the wired network. Recently, schemes that are aimed to improve TCP performance in wireless networks have been proposed. In particular, TCP-Jersey, a TCP scheme that was designed for heterogeneous network consisting of wired and wireless links, has shown significant improvement over other TCP variants. In this paper, performance of TCP-Jersey over high speed downlink packet access (HSDPA) in the Universal Mobile Telecommunication System (UMTS) is evaluated and compared with other TCP variants through detailed system modeling and extensive simulations. The result shows that TCP-Jersey produces significant goodput improvement over other TCP variants tested.

Many works reported in the literature tackle the problem of delay constrained least cost path selection (DCLC) by using /spl epsi/-approximation schemes and scaling techniques, i.e., by mapping link costs into integers or, at least, discrete numbers, a solution that satisfies the delay constraint and has a cost within a factor of (1 + /spl epsi/) of the optimal one can be computed with pseudo polynomial computational complexity. In this paper, having observed that the computational complexities of the /spl epsi/-approximation algorithms using the linear scaling technique are linearly proportional to the linear scaling factors, we investigate the issue of finding the optimal (the smallest) linear scaling factor to reduce the computational complexities and propose a theoretical framework.

Ethernet passive optical networks (EPONs) are a low-cost:, high-speed solution to the bottleneck problem of the broadband access network. A critical issue of EPONs is the utility of a shared upstream channel among the local users, and thus, an efficient bandwidth allocation mechanism is required in order to facilitate statistical multiplexing among the local network traffics. This paper proposes a dynamic bandwidth allocation scheme, i.e., limited sharing with traffic prediction (LSTP), for the upstream channel sharing. Based on the multipoint control protocol (MPCP) and the bursty traffic prediction, LSTP yields significant performance improvement as compared to other existing proposals.

The increasing use of intrusion detection system gives rise to a huge volume of alert logs, making it hard for security administrators to uncover hidden attack scenarios. In this paper, we propose a four-layer semantic scheme designed to allow inferring attack scenarios and enabling attack semantic queries. The modified case grammar, PCTCG, is used to convert the raw alerts into machine-understandable uniform alert streams. The 2-atom alert semantic network, 2-AASN are used to generate attack scenario classes. Afterwards, based on the alert context, attack scenario instances are extracted and attack semantic query results on attack scenario instances using spreading activation technique are forwarded to the security administrator.

Fair bandwidth allocation is one of the most challenging. research issues in the context of assured forwarding (AF) in the Differentiated Services (DiffServ) networks. There exist many works that tried to assure the fairness of bandwidth allocation. However, these works only focused on studying the simple case, in which multiple AF flows share a single bottleneck link, and they also lacked a solid theoretical analysis to validate themselves. In this paper, we propose a network-assist packet marking (NPM) scheme to offer fair bandwidth allocation among multiple aggregates. By both theoretical analysis and experimental evaluation, we demonstrate that NPM can fairly distribute bandwidth among these aggregates in both single and multiple bottleneck link networks.

A novel distributed end-to-end quality-of-service (QoS) provisioning architecture based on the concept of decoupling the end-to-end QoS provisioning from the service provisioning at routers in the differentiated service (DiffServ) network is proposed. The main objective of this architecture is to enhance the QoS granularity and flexibility offered in the DiffServ network model and improve both the network resource utilization and user benefits. The proposed architecture consists of a new endpoint admission control referred to as explicit endpoint admission control at the user side, the service vector which allows a data flow to choose different services at different routers along its data path, and a packet marking architecture and algorithm at the router side. The achievable performance of the proposed approach is studied, and the corresponding results demonstrate that the proposed mechanism can have better service differentiation capability and lower request dropping probability than the integrated service over DiffServ schemes. Furthermore, it is shown that it preserves a friendly networking environment for conventional transmission control protocol flows and maintains the simplicity feature of the DiffServ network model.

We introduce an iterative all hops k-shortest paths (IAHKP) algorithm that is capable of iteratively computing all hops k-shortest path (AHKP) from a source to a destination. Based on IAHKP, a high performance algorithm, dual iterative all hops k-shortest paths (DIAHKP) algorithm, is proposed. It can achieve 100% success ratio in finding the delay constrained least cost (DCLC) path with very low average computational complexity. The underlying concept is that since DIAHKP is a k-shortest-paths-based solution to DCLC, implying that its computational complexity increases with k, we can minimize its computational complexity by adaptively minimizing k, while achieving 100% success ratio in finding the optimal feasible path. Through extensive analysis and simulations, we show that DIAHKP is highly effective and flexible. By setting a very small upper bound to k (k=1,2), DIAHKP still can achieve very satisfactory performance. With only an average computational complexity of twice that of the standard Bellman-Ford algorithm, DIAHKP achieves 100% success ratio in finding the optimal feasible path in the typical 32-node network.

Proportional fair queuing is to ensure that a flow passing through the network only consumes a fair share of the network resource that is proportional to its committed rate or other service level agreement (SLA). It is of great importance in Differentiated Services (DiffServ) networks as well as other price incentive network services. In this paper, we propose a simple core-stateless proportional fair queuing algorithm (CSPFQ) for the assured forward (AF) traffic in DiffServ networks. We first develop our algorithm based on a fluid model analysis and then extend it to a realizable packet level algorithm. We prove analytically and instantiate through simulations that our algorithm can achieve proportional fair bandwidth allocation among competing flows without requiring routers to estimate flows' fair share rates. Our simulation results also demonstrate that our algorithm outperforms the weighted core-stateless fair queuing (WC-SFQ) in terms of proportional fairness.

In this paper, we propose a new mechanism to defend against distributed denial of service (DDoS) attacks with path information rather than IP address information. Instead of the complete binary tree model, our proposal is based on the four color theorem. The salient feature of the theorem is that it allows color reuse so that even if some portions of the map have more than 4 neighbors, 4 colors are still sufficient to mark all their borders. This idea of reuse is very important because some routers have many interfaces and the length of the ID field in the header of an IP packet, where the marking information is embedded, is very limited. Furthermore, our marking scheme takes the Internet hierarchy into account, and greatly relaxes the limitation on the number of interfaces of routers, thus making the scheme more practical. Simulation results have validated our design.

In this paper, a novel concept of decoupling the end-to-end QoS provisioning from the service provisioning at routers in the Diffserv network is proposed to enhance the QoS granularity offered in the Diffserv model and improve both the network resource utilization and user benefits. To realize the concept, we implement a new endpoint admission control, referred to as explicit endpoint admission control, with the service vector concept at the user side, which allows a data flow to choose different services at different routers. At the router side, we propose a new packet marking scheme, by which the end host can obtain the performance of each service class at each router and determine the service vector. The achievable performance of the proposed approach is studied and the corresponding results demonstrate that the proposed mechanism can have better service differentiation capability and lower request dropping probability than the Intserv over Diffserv schemes while it still maintains the simplicity feature of the Diffserv network model.

This paper addresses the burstification interval scaling problem when the timer-based burstification mechanisms, including the periodic and the nonperiodic alternatives, are employed. We investigate the impact of the burstification interval on the burst traffic characteristics in terms of the data burst inter-arrival time and the data burst length, respectively. An analytical model is developed to evaluate the burst delay at the edge node of the OBS-enabled WDM backbone. Numerical and simulation results have justified our analysis.

Recently, among various data hiding techniques, a new subset, lossless data hiding, has drawn tremendous interest. Most existing lossless data hiding algorithms are, however, fragile in the sense that they can be defeated when compression or other small alteration is applied to the marked image. The method of C. De Vleeschouwer et al. (see IEEE Trans. Multimedia, vol.5, p.97-105, 2003) is the only existing semi-fragile lossless data hiding technique (also referred to as robust lossless data hiding), which is robust against high quality JPEG compression. We first point out that this technique has a fatal problem: salt-and-pepper noise caused by using modulo 256 addition. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. This technique has been successfully applied to many commonly used images (including medical images, more than 1000 images in the CorelDRAW database, and JPEG2000 test images), thus demonstrating its generality. The experimental results show that the visual quality, payload and robustness are acceptable. In addition to medical and law enforcement fields, it has been applied to authenticate losslessly compressed JPEG2000 images.

The resilient packet ring (RPR), defined under IEEE 802.17, has been proposed as a high-speed backbone technology for metropolitan area networks. RPR is introduced to mitigate the underutilization and unfairness problems associated with the current technologies SONET and Ethernet, respectively. The key performance objectives of RPR is to achieve high bandwidth utilization, optimum spatial reuse on the dual rings, and fairness. The challenge is to design an algorithm that can react dynamically to the traffics in achieving these objectives. The RPR fairness algorithm (J. Kao et al., January 2002) is comparatively simple, but it poses some critical limitations that require further investigation and remedy. One of the major problems is that the amount of bandwidth allocated by the algorithm oscillates severely under the unbalanced traffic scenarios. These oscillations presents a barrier in achieving spatial reuse and high bandwidth utilization. We propose a low complexity fairness algorithm (LCFA) in allocating the bandwidth fairly to RPR nodes with a very low computational complexity O(1) that requires a simple hardware requirement similar to that of the RPR fairness algorithm.

This paper focuses on the control architecture and the enabling technologies for the Ethernet-supported Internet protocol-over-wavelength-division-multiplexing metropolitan area networks. We present the general architecture of an access node of such networks and propose solutions to facilitate the essential system functionalities. The aim is to render the flexible and high-capacity metropolitan network, which provides service provisioning improvement and resource utilization efficiency for the packet-dominated data traffic. Specifically, an enhanced address resolution protocol is proposed to reduce the call setup latency and the signaling overhead associated with the address probing procedure, a burst-based transmission mechanism is adopted to improve the network throughput and resource utilization efficiency, and a wavelength allocation algorithm is investigated to provide flexible bandwidth multiplexing with fairness and high scalability. Theoretical analysis and simulations are conducted to evaluate the performance of our algorithms, demonstrating that the proposed architecture and technologies deliver substantial transport performance improvement with efficient network resource utilization.

Improving the performance of the transmission control protocol (TCP) in wireless Internet protocol (IP) communications has been an active research area. The performance degradation of TCP in wireless and wired-wireless hybrid networks is mainly due to its lack of the ability to differentiate the packet losses caused by network congestions from the losses caused by wireless link errors. In this paper, we propose a new TCP scheme, called TCP-Jersey, which is capable of distinguishing the wireless packet losses from the congestion packet losses, and reacting accordingly. TCP-Jersey consists of two key components, the available bandwidth estimation (ABE) algorithm and the congestion warning (CW) router configuration. ABE is a TCP sender side addition that continuously estimates the bandwidth available to the connection and guides the sender to adjust its transmission rate when the network becomes congested. CW is a configuration of network routers such that routers alert end stations by marking all packets when there is a sign of an incipient congestion. The marking of packets by the CW configured routers helps the sender of the TCP connection to effectively differentiate packet losses caused by network congestion from those caused by wireless link errors. This paper describes the design of TCP-Jersey, and presents results from experiments using the NS-2 network simulator. Results from simulations show that in a congestion free network with 1% of random wireless packet loss rate, TCP-Jersey achieves 17% and 85% improvements in goodput over TCP-Westwood and TCP-Reno, respectively; in a congested network where TCP flow competes with VoIP flows, with 1% of random wireless packet loss rate, TCP-Jersey achieves 9% and 76% improvements in goodput over TCP-Westwood and TCP-Reno, respectively. Our experiments of multiple TCP flows show that TCP-Jersey maintains the fair and friendly behavior with respect to other TCP flows.

A modification to the basic deterministic packet marking (DPM), a promising IP traceback scheme, to handle fragmented traffic is proposed. The modification introduces no additional bandwidth overhead, but limited additional memory requirements and processing overhead on the DPM-enabled interface.

We investigate the problem of finding the delay constrained least cost path (DCLC) in a network, which is NP-complete and has been extensively studied. Many proposed algorithms tackle this problem by transforming it into the shortest path selection problem or the k-shortest paths selection problem, which are NP-complete, with an integrated weight function that maps the delay and cost for each link into a single weight. However, they suffer from either high computational complexity or low success ratio in finding the optimal paths (the least cost path satisfying a given delay constraint). Based on the extended Bellman-Ford (EB) algorithm, we propose a high performance algorithm, dual extended Bellman-Ford (DEB) algorithm, which achieves a high success ratio in finding the least cost path subject to a delay constraint with low computational complexity. Extensive simulations show that DEB outperforms its contender on: the worst-case computational complexity; average cost of the solutions; the success ratio in finding the delay constrained least cost path.

Core-stateless scheduling algorithms can provide a similar level of guaranteed services as the stateful approach, while they do not need per-flow management. They hence possess both the properties of quality-of-service (QoS) provisioning and high scalability. In this paper, by showing the current existing traffic models are not applicable to core-stateless networks, a new and efficient traffic model for characterizing traffic in a core-stateless network is proposed, and its properties are presented.

This article proposes a survivability evaluation model for multifiber WDM networks, in which the extended layered graph is developed to jointly optimize the wavelength routing and wavelength assignment problems, and three different edge cost functions are evaluated in terms of traffic engineering. Linear programming (LP) equations are formulated to optimally determine the working lightpath and the corresponding protection lightpath under the schemes of shared protection and dedicated protection. The extensive simulations show that shared protection with the fiber and wavelength-based cost function provides the best survivability.

In recent years, various QoS routing algorithms have been proposed to meet the QoS requirement of today's multimedia applications. These algorithms can be divided into two categories: source routing and distributed routing. In this paper, we propose a novel QoS routing algorithm, which is an integration of source routing and distributed routing. We introduce the concept of "domain" which is a set of neighboring nodes and links. Each node has its own "domain" and has the accurate link-state information within its "domain". When a QoS request probe arrives at a node will use the link-state information within its "domain" to calculate where the probe should be forwarded to and the probe forwarding path. By doing so, the message overhead induced by probe forwarding can be reduced significantly but the overhead induced by link-state information update may increase dramatically. However, if the size of the "domain" is chosen properly according to the network topology, our algorithm can reduce the message overhead while maintaining high request admission ratio.

Finding a feasible path subject to multiple constraints in a network is an NP-complete problem and has been extensively studied. Many proposed source routing algorithms tackle this problem by transforming it into the shortest path selection problem, which is P-complete, with an integrated cost function that maps the multi-constraints of each link into a single cost. However, how to select an appropriate cost function is an important issue that has a rarely been addressed in literature. In this paper, we provide a theoretical framework for picking a cost function that can improve the performance of source routing in terms of complexity, convergence, and probability of finding a feasible path.

In this paper, we propose a path restoration algorithm for a single fiber fault in WDM network equipped with wavelength conversion. The integer linear programming (ILP) formulas for minimizing the path recovery cost can yield the optimum solution but it is NP-complete. Our heuristic algorithm relaxes the complexity by two steps. In the off-line step, the node-link topology is replaced by the node-wavelength topology, the alternate paths for source-destination node pairs are listed, and the wavelengths in each link are assigned by maximum matching. In the on-line step, traffics going through the failed link are split into subtraffics and are restored according to their priorities. The performance analysis and the simulation results indicate that out heuristic algorithm is practical for WDM networks restoration.

As the DiffServ architecture is gaining ground, traffic engineering requires major adjustments. In addition, the measurement-based strategy gas been widely adopted owing to its advantages of flexibility and easy adaptation. This article reviews measurement-based dropping schemes. Based on the definition and investigation of the "packet shortage" phenomenon, an enhanced dropping scheme for the proportional differentiated packet loss, referred to as "debt-aware," is proposed. Simulation parameters, as compared to a typical proportional dropping mechanism. More simulations have been applied to demonstrate the merits of this improved method.

Communications and communication protocols play an important role in mobile robot systems able to address real world applications. Since the advent of high-performance wireless local area network (WLAN) and ad hoc networking technology at relatively low cost, their use for wireless communications among and control of mobile robots has become a practical proposition. However, in a large system with many mobile robots, it becomes difficult for all of robots to exchange information at a time because of their limited communication capacities. In this case, an ad hoc robot networking scheme is more promising. This paper presents the background of mobile ad hoc networks, ad hoc robot wireless communications, and their applications.

We propose a forward resource reservation (FRR) scheme to reduce the data burst delay at edge nodes in optical burst switching (OBS) systems. We also explore algorithms to implement the various intrinsic features of the FRR scheme. Linear predictive filter (LPF)-based methods are investigated and demonstrated to be effective for dynamic burst-length prediction. An aggressive resource reservation algorithm is proposed to deliver a significant performance improvement with controllable bandwidth cost. By reserving resources in an aggressive manner, an FRR system can reduce both the signaling retransmission probability and the bandwidth wastage as compared with a system without the aggressive reservation. An FRR-based QoS strategy is also proposed to achieve burst delay differentiation for different classes of traffic. Theoretical analysis and simulation results verify the feasibility of the proposed algorithms and show that our FRR scheme yields a significant delay reduction for time-critical traffic without incurring a deleterious bandwidth overhead.

This paper addresses the issue of providing QoS services for optical burst switching (OBS) systems. We propose a linear predictive filter (LPF)-based forward resource reservation method to reduce the burst delay at edge routers. An aggressive reservation method is proposed to increase the successful forward reservation probability and to improve the delay reduction performance. We also discuss a QoS strategy that achieves burst delay differentiation for different classes of traffic by extending the FRR scheme. We analyze the latency reduction improvement gained by our FRR scheme, and evaluate the bandwidth cost of the FRR-based QoS strategy. Our scheme yields significant delay reduction for time-critical traffic, while maintaining the bandwidth overhead within limits.

The use of fluid generalized processor sharing (GPS) algorithm for integrated services networks has received a lot of attention since early 1990s because of its desirable properties in terms of delay bound and service fairness. Many packet fair queuing (PFQ) algorithms have been developed to approximate GPS. However, owing to their implementation complexity, it is difficult to support a large number of sessions with diverse service rates while maintaining the GPS properties. The grouping architecture has been proposed to dramatically reduce the implementation complexity. However, it can only support a fixed number of service rates, thus causing the problem of granularity. We present a viable implementation of our previously proposed dual-rate grouping architecture, and demonstrate that, as compared with the original grouping architecture, our proposed scheme possesses better performance in terms of approximating per session-based PFQ algorithms without increasing the implementation complexity.

We present features of next generation SONET, focusing particularly on path management. Factors such as QoS metrics and path protection are keys to realize automatic and dynamic path management. Two algorithms (the sequential algorithm and the parallel algorithm) to provide QoS and protection path in SONET are proposed and discussed in detail. They balance the network load by circumventing heavily loaded links, and reduce the network resource consumption by selecting the shortest paths. Simulation results demonstrate that they are practical solutions for path management of next generation SONET.

Carrying IP traffic over connection-oriented networks requires the use of bandwidth allocation schemes at gateways or network interfaces. A new virtual queue occupancy, which is more accurate than the classical queue status parameter, is being proposed for periodic bandwidth allocation. Based on this virtual queue occupancy, an enhanced approach for lossless services, referred to as LAVQ, is simulated and evaluated in an IP/SONET environment. Simulations show that LAVQ outperforms its counterpart LAQ in terms of bandwidth utilization, without compromising the performance of queue occupancy.

In this paper we propose a scheme referred to as dual-rate session grouping to improve the service rate granularity for cell-based schedulers. In this scheme a session is split into two subsessions to provide the average service rate that a user requires. By applying dual-rate session grouping, the utilization of bandwidth and fairness among users can be improved, while the complexity of the scheduling algorithm remains the same as the conventional scheme. The overall computational complexity of the dual-rate session grouping does not increase with the rate granularity that is only limited by the available memory space. Several implementation issues are also presented in this paper.

Scheduling algorithms are implemented in high-speed switches to provision quality-of-service guarantees in both cell-based and packet-based networks. Being able to guarantee end-to-end delay and fairness, timestamp-based fair queuing algorithms have received much attention in the past few years. In timestamp-based fair queuing algorithms, the size of timestamp and period determines the supportable rates in terms of the range and accuracy. Furthermore, it also determines the scheduler's memory in terms of access bandwidth and storage space. An efficient expression can reduce the size of the timestamp and period without compromising the supportable rate range and the accuracy. In this paper, we propose a compressed and dynamic-range-based expression of the timestamp and period, which can be readily implemented in hardware for both high-speed packet-based and cell-based schedulers. As compared to fixed-point and floating-point number expression, when the size is fixed, the proposed expression has a better accuracy. Regarding to efficiency and relative error consistency, it even better than our earlier proposal.

This paper traces the development/evolution of three of our previously proposed MPEG video traffic models, that can capture the statistical properties of MPEG video data. The basic ideas behind these models are to decompose an MPEG compressed video sequence into several parts according to motion/scene complexity or data structure. Each part is described with a self-similar process. These different self-similar processes are then combined to form the respective models. In addition, the Beta distribution is used to characterize the marginal cumulative distribution (CDF) of the self-similar processes. Comparison among the three models shows that the latest model (called the simple models) is the most practical one in terms of accuracy and complexity. Simulations based on a real MPEG compressed movie sequence of Star Wars have demonstrated that the simple model can capture the ACF and the marginal CDF very closely.

Scheduling algorithms are implemented in hardware in high-speed switches to provision quality-of-service guarantees in both cell-based and packet-based networks. Being able to guarantee end-to-end delay and fairness, timestamp-based fair queuing algorithms have received much attention in the past few years. In timestamp-based fair queuing algorithms, the size of timestamp and period determines the supportable rates in terms of the range and accuracy. Furthermore, it also determines the scheduler's memory in terms of off-chip bandwidth and storage space. An efficient expression can reduce the size of the timestamp and period without compromising the accuracy. We propose a new expression of the timestamp and period, which can be implemented in hardware for both high-speed packet-based and cell-based switches. As compared to fixed-point and floating-point number expression, when the size is fixed, the proposed expression has a better accuracy.

A two-stage adaptive multiuser detector in an additive white Gaussian noise code-division multiple-access channel is proposed and analyzed. Its first stage is an asynchronous one-shot decorrelator which in terms of computational complexity only requires inversion of K symmetric K/spl times/K matrices for all K users. In addition, the K inversions can be done in parallel, and the computed results for one user can be reused by all other users as well, resulting in a latency of only one bit, same as its synchronous counterpart. The decorrelated tentative decisions are utilized to estimate and subtract multiple-access interference in the second stage. Another novel feature of the detector is the adaptive manner in which the multiple-access interference estimates are formed, which renders prior estimation of the received signal amplitudes and the use of training sequences unnecessary. Adaptation algorithms considered include steepest descent (as well as its stochastic version), and a recursive least squares-type algorithm that offers a faster transient response and better error performance. Sufficient conditions for the receiver to achieve convergence are derived. The detector is near-far resistant, and is shown to provide substantial steady-state error performance improvement over the conventional and decorrelating detector, particularly in the presence of strong interfering signals.

Input-queued switching architectures are becoming an attractive alternative for designing very high speed switches owing to its scalability. Tremendous efforts have been made to overcome the throughput problem caused by contentions occurred at the input and output sides of the switches. However, no QoS guarantees can be provided by the current input-queued switch design. In this paper, a frame based scheduling algorithm, referred to as store-sort-and-forward (SSF), is proposed to provide QoS guarantees for input-queued switches without requiring speedup. SSF uses a framing strategy in which the time axis is divided into constant-length frames, each made up of an integer multiple of time slots. Cells arrived during a frame are first held in the input buffers, and are then "sorted-and-transmitted" within the next frame. A bandwidth allocation strategy and a cell admission policy are adopted to regulate the traffic to conform to the (r,T) traffic model. A strict sense 100% throughput is proved to be achievable by rearranging the cell transmission orders in each input buffer, and a sorting algorithm is proposed to order the cell transmission. The SSF algorithm guarantees bounded end-to-end delay and delay jitter. It is proved that a perfect matching can be achieved within N(ln N+O(1)) effective moves.

It is estimated that video traffic will increasingly occupy a major portion of future network bandwidth, and thus traffic modeling plays an important role for network design and management. In this paper, we propose Markov modulated self-similar processes to model MPEG video sequences that can capture the LRD (long range dependency) characteristics of video ACF (auto-correlation function). The basic idea behind this modeling is to decompose an MPEG compressed video sequence into three parts according to different motion/change complexity. Each part can individually be described by a self-similar process. In addition, beta distribution is used to characterize the marginal cumulative distribution (CDF) of the video traffic. To model the whole data set, a Markov chain is used as a dominating process to govern the transitions among these three self-similar processes. Initial simulations on a real MPEG compressed movie sequence of Star Wars have demonstrated that our new model can capture the LRD of ACF and the marginal CDF very well. Video traffic synthesis using our model is presented. Further research in this direction is discussed.

A new threshold decomposition architecture is introduced to implement stack filters. The architecture is also generalized to a new class of nonlinear filters known as threshold decomposition (TD) filters which are shown to be equivalent to the class of L1-filters under certain conditions. Another new class of filters known as linear and order statistic (LOS) filters result from the intersection of the class of TD and L1-filters. Performance comparisons among several filters are then presented. It was found that TD is compatible with L1, LOS, and linear filters in suppressing Gaussian noise, and is superior in suppressing salt-and-pepper noise. LOS filters, however, provide a better compromise in performance and complexity.

The input-queued switching architecture is becoming the alternative architecture for high speed switches owing to its scalability. Tremendous amount of effort has been made to overcome the throughput problem caused by head of line blocking and the contentions occurred at input and output sides of a switch. Existing algorithms only aim at improving throughput but inadvertently ignore undesired effects on the traffic shape and quality of service features such as delay and fairness. In this paper a new algorithm, referred to as longest normalized queue first, is introduced to improve upon existing algorithms in terms of delay, fairness and burstiness. The proposed algorithm is proven to be stable for all admissible traffic patterns. Simulation results confirm that the algorithm can smooth the traffic shape, and provide good delay property as well as fair service.

The routing and signaling protocols for supporting multipoint-to-multipoint connections in ATM networks have been presented earlier. VP-merge and VC-merge techniques have been proposed as the likely candidates for resolving the sender identification problem associated with these connections. The additional buffer requirements in the VC-merge mechanism and the excessive rise of VPI/VCI space in the VP-merge mechanism have been the main reasons for concern about their effective utility. We propose improvements to the traditional VC-merge technique to minimize the need for additional buffers at intermediate merge points. Aptly named dynamic multiple VC-merge (DMVC), fixed multiple VC-merge (FMVC) and selective multiple VC-merge (SMVC), these mechanisms define a generic scheme for merging the data from multiple senders onto one or more outgoing links. By appropriately choosing the number of connection identifiers per connection, these schemes lead to a large reduction in the buffer requirements and an effective utilization of the VPI/VCI space. Based on extensive simulations, we show that by using two connection identifiers per connection, there is an 80% reduction in buffer requirements for DMVC and FMVC when compared to the buffer required for traditional VC-merge.

In an integrated time-division multiple access (TDMA) communication system, voice and data are multiplexed in time to share a common transmission link in a frame format in which time is divided into slots. A certain number of time slots in a frame are allocated to voice and the rest are used to transmit data. Maximum data throughput can be achieved by searching for the optimal configuration(s) of relative positions of voice and data transmissions in a frame (frame pattern). When the problem size becomes large, the computational complexity in searching for the optimal patterns becomes intractable. In this paper, mean field annealing (MFA), which provides near-optimal solutions with reasonable complexity, is proposed to solve this problem. The determination of the related parameters are addressed. Comparison with the random search and simulated annealing algorithm is made in terms of solution optimality and computational complexity. Simulation results show that the MFA approach exhibits a good tradeoff between performance and computational complexity.

An adaptive fusion algorithm is proposed for an environment where the observations and local decisions are dependent from one sensor to another. An optimal decision rule, based on the maximum posterior probability (MAP) detection criterion for such an environment, is derived and compared to the adaptive approach. In the algorithm, the log-likelihood ratio function can be expressed as a linear combination of ratios of conditional probabilities and local decisions. The estimations of the conditional probabilities are adapted by reinforcement learning. The error probability at steady state is analyzed theoretically and, in some cases, found to be equal to the error probability obtained by the optimal fusion rule. The effect of the number of sensors and correlation coefficients on error probability in Gaussian noise is also investigated. Simulation results that conform to the theoretical analysis are also presented.

The central issue of explicit rate control for available bit rate (ABR) service in ATM networks is the computation of fair rate for every connection. In this paper, we propose a new fair-rate allocation algorithm called fast max-min rate allocation (FMMRA) for ATM switches supporting ABR services. The FMMRA algorithm provides the means to compute the max-min fair rates with O(1) computational complexity. This exact calculation of fair rates expedites quick convergence to max-min fair shares, and offers excellent transient response. At the steady state, the algorithm operates without causing any oscillations in rates. The FMMRA algorithm does not require any parameter tuning and proves to be very robust in a large ATM network. Some simulation results are provided to show the effectiveness of the algorithm.

A coherent multiuser decorrelating detector for an asynchronous CDMA, time-varying Rayleigh fading channel is proposed and analyzed. The detector employs fractionally sampled correlators' outputs at time instants corresponding to users' relative delays to simultaneously achieve two goals: the novel realization of a one-shot decorrelator with lower computational complexity; and to exploit a form of the time diversity for improved error performance compared to symbol spaced sampling. The decision statistics are formed from the decorrelator output according to the maximal ratio combining rule. The proposed detector performance is compared to that of a conventional symbol-spaced receiver in a single-user environment, and the impact of the channel estimation error is illustrated.

We present an efficient broadcast scheduling algorithm based on mean field annealing (MFA) neural networks. Packet radio (PR) is a technology that applies the packet switching technique to the broadcast radio environment. In a PR network, a single high-speed wideband channel is shared by all PR stations. When a time-division multi-access protocol is used, the access to the channel by the stations' transmissions must be properly scheduled in both the time and space domains in order to avoid collisions or interferences. It is proven that such a scheduling problem is NP-complete. Therefore, an efficient polynomial algorithm rarely exists, and a mean field annealing-based algorithm is proposed to schedule the stations' transmissions in a frame consisting of certain number of time slots. Numerical examples and comparisons with some existing scheduling algorithms have shown that the proposed scheme can find near-optimal solutions with reasonable computational complexity. Both time delay and channel utilization are calculated based on the found schedules.

A new class of adaptive filters called generalized adaptive neural filters (GANFs) has emerged. They share many things in common with stack filters and include all stack filters as a subset. The GANFs allow a very efficient hardware implementation once they are trained. However, the training process can be slow. This paper discusses structural modifications to allow for faster training. In addition, these modifications can lead to an increase in the filter's robustness, given a limited amount of training data. This paper does not attempt to justify use of a GANF; it only presents an alternative implementation of the filter. To verify the results, several simulations were performed by corrupting two images with varying amounts of mixture noise and Gaussian noise.

The performance of nonhierarchical circuit switched networks at moderate load conditions is improved when alternate routes are made available. Alternate routes, however, introduce instability under heavy and overloaded conditions, and under these load conditions network performance is found to deteriorate. To alleviate this problem, a control mechanism is used where, a fraction of the capacity of each link is reserved for direct routed calls. In this work, a traffic management scheme is developed to enhance the performance of a mesh-connected, circuit-switched satellite communication network. The network load is measured and the network is continually adapted by reconfiguring the map to suit the current traffic conditions. The routing is performed dynamically. The reconfiguration of the network is done by properly allocating the capacity of each link and placing an optimal reservation on each link. The optimization is done by using two neural network-based optimization techniques: simulated annealing and mean field annealing. A comparative study is done between these two techniques. The results from the simulation study show that this method of traffic management performs better than the pure dynamic routing with a fixed configuration.

This article addresses the structure and properties of a new class of nonlinear adaptive filters called generalized adaptive neural filters (GANFs). Various properties, such as an upper bound of the mean absolute error of the filters, are analytically derived. Experimental results are presented to demonstrate the performance of the filters for signal and image enhancement. It is shown that GANFs not only extend the class of stack filters, but also have better performance in noise suppression.

A thorough investigation on the convergence and stability of an adaptive synchronous CDMA receiver is presented. The receiver consists of a decorrelator at the first stage and an adaptive interference canceler at the second stage. By using a steepest descent algorithm that minimizes the output signal energy to adaptively control the weights, neither the knowledge of the users' received amplitudes nor the use of training sequences is required. The system is near-far resistant, and its error performance approaches the single-user bound when the interferers' SNRs are high. Sufficient conditions for the receiver to achieve convergence are derived, and their properties are analyzed.

Reports a new method for optimizing satellite broadcasting schedules based on the Hopfield neural model in combination with the mean field annealing theory. A clamping technique is used with an associative matrix, thus reducing the dimensions of the solution space. A formula for estimating the critical temperature for the mean field annealing procedure is derived, hence enabling the updating of the mean field theory equations to be more economical. Several factors on the numerical implementation of the mean field equations using a straightforward iteration method that may cause divergence are discussed; methods to avoid this kind of divergence are also proposed. Excellent results are consistently found for problems of various sizes.

The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented.>

The paper analyzes the use of vanishing points for steering a robot. Parallel lines in the environment of the robot are used to compute vanishing points which serve as a reference for guiding the robot. To accomplish the steering task, three subtasks are performed: detection of straight lines, computation of vanishing points, and robot steering using vanishing points. Straight lines are detected by employing a high precision edge detector and a line-fitting algorithm. The cross product method introduced by Magee and Aggarwal (1984) is modified to make the detection of vanishing points appropriate for an indoor environment. Properties of vanishing points under camera rotation and translation are derived. Using these properties, the location of the vanishing points can serve as a reference for steering the robot. A model of the robot environment is defined, summarizing the minimum number of constraints necessary for the method to work. Finally, the limitations as well as the advantages of using vanishing points in robot navigation are discussed.>

Stack filters are a class of sliding-window nonlinear digital filters that possess the weak superposition property (threshold decomposition) and the ordering property known as the stacking property. They have been demonstrated to be robust in suppressing noise. Two methods are introduced to adaptively configure a stack filter. One is by employing the least mean square (LMS) algorithm and the other is based on perceptron learning. Experimental results are presented to demonstrate the effectiveness of the methods for noise suppression.>

This article addresses the path-planning problem for a mobile manipulator system that is used to perform a sequence of tasks specified by locations and minimum oriented force capabilities. The problem is to find an optimal sequence of base positions and manipulator configurations for performing a sequence of tasks given a series of task specifications. The formulation of the problem is nonlinear. The feasible regions for the problem are nonconvex and unconnected. Genetic algorithms applied to such problems appear to be very promising while traditional optimization methods cause difficulties. Computer simulations are carried out on a three-degrees-of-freedom manipulator mounted on a two-degrees-of-freedom mobile base to search for the near optimal path-planning solution for performing the sequence of tasks. © 1994 John Wiley & Sons, Inc.

A method of recognizing partially occluded objects is presented in which each object is represented by a set of landmarks. Given a scene consisting of partially occluded objects, a model object in the scene is hypothesized by matching the landmarks of the model with those in the scene. A measure of similarity between two landmarks is needed to perform this matching. A local shape measure, sphericity, is introduced. It is shown that any invariant function under a similarity transformation is a function of the sphericity. To match landmarks between the model and the scene, a table of compatibility is constructed. A technique, known as hopping dynamic programming, is described to guide the landmark matching through the compatibility table. The location of the model in the scene is estimated with a least-squares fit among the matched landmarks. A heuristic measure is then computed to decide if the model is in the scene.>

A technique to guide landmark matching known as hopping dynamic programming is described. The location of the model in the scene is estimated with a least-squares fit. A heuristic measure is then computed to decide if the model is in the scene. The shape features of an object are the landmarks associated with the object. The landmarks of an object are defined as the points of interest of the object that have important shape attributes. Examples of landmarks are corners, holes, protrusions, and high-curvature points.>