A version of the accepted manuscript is available in arXiv at arXiv:2603.04898v1 [cs.LG] (https://arxiv.org/abs/2603.04898). Comments: This paper has been accepted by infocom. The source code has been released at: https://github.com/qiongwu86/U-Parking . Submission history: From: Qiong Wu: [v1] Thu, 5 Mar 2026 07:38:51 UTC (499 KB).

Accurately estimating the real-time state of dynamic systems from noisy measurements is a fundamental task in signal processing. State-space models (SSM) are commonly utilized to model system dynamics and account for uncertainty in both system state evolution and measurement data. Kalman filter (KF) and its variants are widely recognized for their low computational complexity and ability to address state evolution and measurement update. In practice, accurately modeling the system dynamics and obtaining model parameters are difficult, especially for nonlinear systems. These tasks often require predefined motion and measurement models that rely on coarse approximations and extensive manual tuning. In this paper, we propose DL-KF, a robust Deep Learning aided Kalman Filter designed to perform KF in nonlinear dynamic environments where the process model is unavailable and the system noise is also unknown. However, a linear measurement model is assumed. DL-KF utilizes a Long Short-Term Memory (LSTM) network to capture nonlinear process dynamics and generate state priors from historical measurements. Additionally, a Gated Recurrent Unit (GRU) is utilized to independently learn innovation covariance, addressing measurement noise uncertainty, followed by the computation of the Kalman gain within the Kalman filter framework. By integrating an SSM framework with deep learning modules, the proposed method strikes the balance between the flexibility of data-driven approach and the interpretability of classical Kalman based approaches. Empirical results demonstrate that DL-KF significantly outperforms traditional model-based filters, including the KF, EKF, and UKF. Furthermore, it surpasses state-of-the-art hybrid methods such as KalmanNet, Split-KalmanNet, and AKNet, particularly in challenging scenarios involving high nonlinearity (e.g., the Lorenz attractor) and maneuver-induced model mismatches. The algorithm’s efficacy and robustness are further validated through extensive experiments on real-world tunnel radar and NCLT datasets.

Tracking maneuvering targets, such as connected and automated vehicles, requires modeling their movements with pre-defined kinematic models. However, sudden and unpredictable maneuvers often lead to model mismatch, resulting in significant peak tracking errors. To address this problem, we propose a maneuver detection-aided deep learning multiple model filter (MD-DL-MM) technique for target tracking and state estimation, designed to suppress peak errors and improve tracking performance. The core innovation of the MD-DL-MM technique is the integration of a self-attention-based discrimination network, which dynamically determines the weights of the target’s motion models. To consider the potential impact of a specific kinematic models on the state estimation accuracy, a statistical hypothesis testing-based method is introduced to evaluate the validity of kinematic models. This metric system-atically examines the suitability of the motion model, ensuring that the most appropriate model is selected and applied at each stage of the tracking process. On that basis, two distinct state estimation methods are designed. Specifically, when the kinematic model is more appropriate and the target exhibits non-maneuvering, state estimates are obtained using a recursive model-based Kalman filter (MB-KF), which provides optimal estimation with the minimum mean square error (MMSE). On the other hand, when the target exhibits sudden maneuvers or unpredictable maneuvers, a data-driven learning-based network is utilized to achieve high-precision state estimation. Extensive simulation results and real-world experimental data demonstrate that the proposed algorithm outperforms traditional methods such as the Singer, current statistical (CS), and the interactive multiple model (IMM) algorithm, as well as deep learning-based algorithms like DeepMTT and KalmanNet. The proposed algorithm achieves superior performance in terms of stability, computational efficiency, and tracking accuracy across diverse scenarios.

The integration of the global navigation satellite system (GNSS), odometer, and inertial navigation system (INS) holds significant potentials for achieving high-precision vehicle localization. However, GNSS is vulnerable to obstructions and jamming, and the odometer is unreliable in harsh road conditions. These factors can lead to cumulative positioning errors in GNSS-denied environments. To address these issues, a novel multi-source information fusion based vehicle localization method that integrates an onboard binocular camera, an INS, and smart road studs—Internet of Things (IoT) devices extensively used for road safety and data collection in intelligent transportation systems— is introduced. We construct a position measurement model directly in the camera coordinate system through an enhanced You Only Look Once 8th version (YOLOv8) algorithm for smart road stud detection, combined with binocular vision measurement and position transformations. Additionally, we propose a method to enhance vehicle localization accuracy by integrating vehicle speed without relying on additional hardware speed sensors. The vehicle’s speed is estimated from the image sequences captured by the onboard camera using a deep neural network (DNN), named Speed-Net. The final navigation results are produced by fusing the smart road stud aided positioning information, the estimated vehicle speed, and INS data through an error-state extended Kalman filter (ESEKF). Real-world experiments demonstrate the effectiveness of the proposed Smart road stud (SRS)/Velocity/INS integrated vehicle localization method.

Integrated sensing and communications (ISAC) systems promise dual use of spectrum and hardware for data transmission and environmental awareness. However, extending ISAC to satellite networks is challenged by high path loss, long delays, and the overhead of channel estimation. To address these challenges, we propose a channel-agnostic predictive beamforming framework for satellite ISAC (S-ISAC) within a crowdsourced bistatic architecture. Unlike conventional bistatic architectures that require a dedicated sensing receiver, our design aggregates echoes from multiple ground internet of things (IoT) devices (GIDs) in a crowdsourced manner to improve sensing performance without introducing any additional sensing equipment. We propose a model termed Historical Geometric-based LLM (HG-LLM) as a realization of the channel-agnostic predictive beamforming framework. HG-LLM learns to map historical geometric information (HGI) of the satellite, sensing target, and GIDs directly to future beamforming matrices, eliminating the need for channel state information (CSI). We propose two key modules in HG-LLM, namely, the Histogeometric Encoder, which transforms spatial-temporal data into LLM-compatible embeddings, and the TokenBeamformer, which translates the LLM outputs into optimized beamforming weights. Moreover, the backbone LLM is fine-tuned using low-rank adaptation for efficient adaptation to predictive beamforming tasks. Extensive simulations demonstrate that HG-LLM achieves performance levels comparable to channel-based methods across diverse settings, despite relying solely on HGI without requiring explicit CSI.

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

Traditional methods for tracking maneuvering targets, such as connected and automated vehicles, face significant challenges in complex driving environments due to the need for constant adjustments to the state transition model to match the target’s motion. These adjustments often result in decision-making delays and competition between models. Furthermore, the widely adopted first-order Markov assumption frequently fails to capture time-dependent motion patterns, leading to information loss. Although the Interacting Multiple Model (IMM) algorithm mitigates some of these issues by employing multiple motion models, it still struggles with accurately identifying motion patterns, delays in maneuver detection and reduced tracking accuracy. To address these problems, we propose a novel approach that combines deep neural networks with traditional IMM tracking methods. Leveraging the strength of deep learning in classification tasks, we introduce an attention mechanism to enhance motion model recognition. This leads to the development of an enhanced version of IMM, termed Attention-IMM. We evaluate our method on the widely-used LAST dataset and real word automated vehicles data. The results demonstrate that Attention-IMM achieves superior performance in both tracking accuracy and the timeliness and accuracy of model decision-making, offering a robust and efficient solution for maneuvering target tracking.

Sensor fusion plays an increasingly important role in real-time traffic perception using roadside sensing devices because the use of single type of sensors often fail to deliver satisfactory performance in certain harsh environment. This paper investigates asynchronous data fusion for real-time vehicle tracking with inaccurate and randomly delayed measurements from millimeter-wave (MMW) radars and magnetic sensors in tunnel environment. We first propose a multisensor data association algorithm to assign the measurements of MMW radar and magnetic sensors to a particular vehicle. A tracking algorithm is then designed to asynchronously update the current vehicle states with randomly delayed magnetic sensor measurements. The proposed algorithm is implemented in the Xianfengding Tunnel, Jiangxi Province, China. Experiments validate the proposed method's accuracy using real data. The method and collected data form the basis of a real-time digital twin system to support advanced traffic management. The fusion results and measurement dataset are available at https://github.com/futianxuan/data.

Smart road studs are widely used for road safety and traffic data collection. Their accurate and reliable detection, and integration into the perception and control modules of connected and autonomous vehicles (CAVs), enhances road boundary detection, vehicle localization, and driving safety. However, real-time, accurate and reliable detection of the small-sized smart road studs is challenging for fast moving CAVs, especially in harsh environments. To address the challenges, we first build a real-world smart road stud dataset, and then propose and validate a lightweight and efficient smart road stud detection model based on the you only look once 8th version (YOLOv8). We then introduce a novel downsampling module (DownS) combining the average pooling and the max pooling to reduce the number of parameters and minimize information loss during downsampling. Furthermore, we replace the loss function with Normalized Wasserstein Distance (NWD) loss to reduce sensitivity to location deviations in small target detection. Finally, we deploy a real-time smart road stud detection system on an experimental vehicle to validate the feasibility and effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed algorithm significantly enhances the accuracy and efficiency of smart road stud detection, increasing the mean average precision by 9.58% and reducing the number of parameters by 13.71 %. Our dataset is available at: https://github.com/wky-xidian/smart-road-stud-dataset.

As the brain of B5G networks, the core networks enable more ubiquitous intelligent connectivity over previous generations of mobile networks, thanks to the decentralized user plane close to edges. As mitigation against abnormal signaling attacks on edge core networks, signal protection mechanisms for the user planes at the N4 interface are widely investigated. However, the prior art fails to adequately address the distribution characteristics of abnormal signaling at this interface, where single-point defences are insufficient for the complexities of beyond 5G (B5G) distributed architecture. This article proposes a decentralized federated anomaly signaling protection framework, called B5GCASP, based on a functionally layered anomalous signaling detection model (FLAD). Mainly, B5GCASP analyses abnormal signaling distribution under the packet forwarding control protocol (PFCP) at the N4 interface, distinguishing significant and nonsignificant anomalies. Coupled with a decentralized, federated detection mechanism, B5GCASP creates a comprehensive point-and-area detection architecture. Extensive experiments on the 5GC PFCP dataset show that B5GCASP achieves higher accuracy and faster detection of abnormal signaling compared to single-point defending baselines, which offer robust anomaly signaling protection for the B5G core network.

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving.

Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off between scalability and optimality. Most existing RTSC approaches partition a traffic network into several disjoint regions, followed by applying centralized reinforcement learning techniques to each region. However, the pursuit of cooperation among RTSC agents still remains an open issue and no communication strategy for RTSC agents has been investigated. In this paper, we propose communication strategies to capture the correlation of micro-traffic states among lanes and the correlation of macro-traffic states among intersections. We first justify that the evolution equation of the RTSC process is Markovian via a system of store-and-forward queues. Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules—GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states. While GA2-Naive only considers the movements at each intersection, GA2-Aug also considers the lane-changing behavior of vehicles. Two proposed communication modules are then aggregated into two existing novel RTSC frameworks—RegionLight and Regional-DRL. Experimental results demonstrate that both GA2-Naive and GA2-Aug effectively improve the performance of existing RTSC frameworks under both real and synthetic scenarios. Hyperparameter testing also reveals the robustness and potential of our communication modules in large-scale traffic networks.

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

Sensor fusion plays an increasingly important role in real-time traffic perception using roadside sensing devices because the use of single type of sensors often fail to deliver satisfactory performance in certain harsh environment. This paper investigates asynchronous data fusion for lane-level vehicle tracking with randomly delayed measurements and inaccurate detections from millimeter wave (MMW) radars and magnetic sensors in tunnels, where vehicle tracking with single type of sensors can not meet the requirements of reliable and accurate lane-level tracking due to inaccurate radar detections at far distances, noisy radar detections in tunnel environment, and missed or false vehicle detections by magnetic sensors. A multisensor data association algorithm is first designed to assign the measurements of MMW radar and magnetic sensors to a particular vehicle. A multi-lane estimation model is then developed, which employs Bayesian weight mixture filtering to fuse MMW radar and magnetic sensor measurements and to estimate the lane in which a vehicle is located. Finally, the proposed algorithm is implemented in a real environment - the Xianfengding Tunnel located in Jiangxi Province, China. Experiments are conducted to validate the accuracy of the proposed method using real data. The proposed method and the collected data are further integrated to establish a real-time digital twin system aimed at supporting advanced traffic management. The fusion results and the real radar measurement dataset of the tunnel are made available athttps://github.com/futianxuan/data.

Smart road studs have been extensively deployed as road safety and data collection devices. Accurate and reliable detection of smart road studs and its further integration into the perception and control modules of connected and autonomous vehicles (CAVs) undoubtedly benefit road boundary detection, localization of CAVs and augument the safety of CAVs’ driving. This work investigates real-time, accurate and reliable detection of smart road studs, which is a challenging task for CAVs because existing methods fail to achieve accurate and real-time smart road stud detection, especially in harsh road environment. To address these challenges, we first build a real-world smart road stud dataset, and then propose and validate a lightweight and efficient smart road stud detection model based on the you only look once 8th version (YOLOv8), called SRS-YOLO. First, a Squeeze-and-Excitation (SE) attention module is used to improve the coarse-to-fine (C2F) module to differentiate the channel importance of feature maps and improve the detection accuracy of smart road studs. Second, a novel downsampling module (DownS) that integrates the average pooling and the max pooling is designed to reduce the number of parameters and minimize information loss during the downsampling process. Third, the loss function is replaced with the Normalized Wasserstein Distance (NWD) loss to alleviate the sensitivity to location deviations when computing the loss for small targets. The experimental results demonstrate that the proposed SRS-YOLO outperforms other state-of-the-art methods, and achieves a 87.92% mean average precision at a real-time speed of 78 frames/s. Our dataset is available at:https://github.com/wky-xidian/smart-road-stud-dataset.

With its dual-functional advantages, integrated sensing and communications (ISAC) technologies can be further extended to satellite communications, enhancing global coverage services. However, achieving vast coverage would result in significant delays and considerable path losses. Motivated by this, in this paper, we focus on satellite-based ISAC (S-ISAC) systems and propose a general transceiver design framework incorporating both transmit waveform and receive filter. Unlike existing approaches, our approach uses a predictive joint transmit waveform and receive filter design that eliminates the need of channel estimation, thereby reducing time overhead. Additionally, a versatile weighting mechanism is designed to allow flexible prioritization between communications and sensing. To tackle the intractability of the ISAC transceiver design problem, we adopt a data-driven deep learning-based approach, where the model learns to design the transmit waveform and receive filter from historical channel data. Specifically, we propose a predictive optimization network (PONet), leveraging convolutional layers and a Transformer encoder to capture long-term spatial-temporal features and facilitate the learning capability. Numerical results demonstrate the effectiveness of the proposed PONet in terms of communications and sensing rates in S-ISAC networks in various system settings.

Integration of global navigation satellite system (GNSS) and inertial navigation system (INS) presents significant potential for high-precision vehicle localization. However, this approach suffers from cumulative INS errors in GNSS-denied environments. To address this issue, this paper proposes a method to correct the navigation errors due to INS by using Internet of Things (IoT)-based vision positioning. More specifically, this method employs a binocular camera to assist in obtaining the vision positioning of the vehicle through recognition of LED lights installed in a type of ubiquitously deployed IoT devices that are increasingly used in smart transportation systems. The final navigation outcomes are attained by fusing the vision positioning results with INS information using an unscented Kalman filter (UKF). Real-world experiment results validate the effectiveness of the proposed vehicle localization method.

Crowdsourcing dramatically benefits WiFi fingerprinting localization in reducing the costs of collecting received signal strength (RSS) data during offline site survey and has gained much attention in the literature. This article proposes a deep-learning-based indoor positioning system (IPS), termed SeqIPS, to sufficiently exploit the available information in the crowdsourced sequential RSS data and inertial measurement unit (IMU) data. However, there exist the following three challenges: 1) the relatively large label noises of crowdsourced RSS data; 2) the unavailability of the labels of crowdsourced IMU data; and 3) the incorporation of the knowledge in IMU data into the localization model using RSS measurement as inputs during online localization. To this end, a co-teaching network is developed to effectively extract spatiotemporal features from sequential RSS data and meanwhile alleviate the influence of label noises. Also, a novel loss function involving IMU data is defined to impose spatial penalties, so as to further refine the localization model. Moreover, a domain adaptation module is included to effectively label the crowdsourced IMU data. Extensive experiments are conducted in a real scenario and show that SeqIPS can achieve an average localization error of 3.37 m, outperforming both traditional methods and recent deep-learning-based methods by 18.4% at least. In summary, the novelty of SeqIPS is that extra crowdsourced IMU data is exploited to refine the localization model during offline training, while only sequential RSS data is required as inputs during online localization, such that the system accuracy, simplicity, and costs are reasonably balanced.

Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control lbecomes a popular research topic in recent years. To alleviate the scalability issue of completely centralized reinforcement learning (RL) techniques and the non-stationarity issue of completely decentralized RL techniques on large-scale traffic networks, some literature utilizes a regional control approach where the whole network is firstly partitioned into multiple disjoint regions, followed by applying the centralized RL approach to each region. However, the existing partitioning rules either have no constraints on the topology of regions or require the same topology for all regions. Meanwhile, no existing regional control approach explores the performance of optimal joint action in an exponentially growing regional action space when intersections are controlled by 4-phase traffic signals (EW, EWL, NS, NSL). In this paper, we propose a novel RL training framework named RegionLight to tackle the above limitations. Specifically, the topology of regions is firstly constrained to a star network which comprises one center and an arbitrary number of leaves. Next, the network partitioning problem is modeled as an optimization problem to minimize the number of regions. Then, an Adaptive Branching Dueling Q-Network (ABDQ) model is proposed to decompose the regional control task into several joint signal control sub-tasks corresponding to particular intersections. Subsequently, these sub-tasks maximize the regional benefits cooperatively. Finally, the global control strategy for the whole network is obtained by concatenating the optimal joint actions of all regions. Experimental results demonstrate the superiority of our proposed framework over all baselines under both real and synthetic scenarios in all evaluation metrics.

Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsity induced by passive WiFi sensing, an attention-based deep convolutional autoencoder model is designed to recover accurate crowd density maps in a way similar to image reconstruction; to combat the anonymity caused by MAC randomization, following the identification of local high-density crowds (LHDCs) with the density clustering algorithm, i.e. DM-DBSCAN, a bidirectional convolutional LSTM based model is employed to infer LHDC speeds; to overcome the absence of passive WiFi sensing datasets for model training, three semi-synthetic datasets are produced by emulating passive WiFi sensing with practical pedestrian tracking datasets. Extensive experiments confirm that, the proposed scheme significantly outperforms existing WiFi-based methods in terms of crowd density estimation and provides superior crowd speed estimation. More importantly, the scheme can also produce consistent crowd states on a real-world dataset, revealing that it has the ability to support accurate, visualized and real-time crowd monitoring in large surveillance areas.

Crowd counting aims to estimate the number of individuals in targeted areas. However, mainstream vision-based methods suffer from limited coverage and difficulty in multi-camera collaboration, which limits their scalability, whereas emerging WiFi-based methods can only obtain coarse results due to signal randomness. To overcome the inherent limitations of unimodal approaches and effectively exploit the advantage of multi-modal approaches, this paper presents an innovative WiFi and video-fused multi-modal paradigm by leveraging a heterogeneous dual-attentional network, which jointly models the intra- and inter-modality relationships of global WiFi measurements and local videos to achieve accurate and stable large-scale crowd counting. First, a flexible hybrid sensing network is constructed to capture synchronized multi-modal measurements characterizing the same crowd at different scales and perspectives; second, differential preprocessing, heterogeneous feature extractors, and self-attention mechanisms are sequentially utilized to extract and optimize modality-independent and crowd-related features; third, the cross-attention mechanism is employed to deeply fuse and generalize the matching relationships of two modalities. Extensive real-world experiments demonstrate that our method can significantly reduce the error by 26.2%, improve the stability by 48.43%, and achieve the accuracy of about 88% in large-scale crowd counting when including the videos from two cameras, compared to the best WiFi unimodal baseline.

With the development of Internet of Things (IoT) and communication technology, abnormal parking detection based on geomagnetic sensors equipped with solar panels has become increasingly feasible and important. False parking detection is a main challenge affecting the widespread use of the aforementioned technology. Especially when ambient light change triggers changes in the current of solar panel, which may in turn cause local EM field changes and affect the detection of nearby geomagnetic sensor. To that end, we propose an abnormal parking detection scheme to distinguish between real parking and false detections. Specifically, we establish an equivalent model of the magnetic field around the solar panel, and design a method to compute the magnetic field at the periphery of the solar panel. Then, we analyze the difference between the magnetic field caused by the changes in ambient light and the magnetic field produced by vehicles, and extract features that distinguish abnormal parking from false detections. Based on the features, a state machine is designed to distinguish the false detection of light from the real parking based on the magnetic field waveform. Field tests show that the accuracy of the designed abnormal parking detection scheme can reach 97 %.

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://github.com/SUSTech2022/DRSS-sensor-array-calibration.

The rapid development of Internet of Things (IoT) technology can significantly promote the development and deployment of smart roads, enabling efficient and reliable road information sensing and analysis. As an important part of smart roads, timely and accurate detection of road cracks can improve service life of roads and reduce road management and operating costs. In this paper, we propose a vibration-sensor-based crack detection scheme for smart roads. In this scheme, by deploying the vibration sensor on the roadside, the changes in the vibration signals caused by the vehicle passing through the range of the sensor can be collected in real time. Then, considering that the seismic waves caused by vehicle driving are mostly distributed in the low-frequency range, we perform low-pass filtering on the collected vibration signals to retain the low-frequency vibration signals. After that, in order to distinguish the crack state of the road, we extract the vibration signal features of the normal road and the cracked road in the time domain, frequency domain and time-frequency domain, respectively. Based on the extracted features, we use logistic regression (LR), support vector machine (SVM) and random forest classification (RFC) machine learning algorithms to realize road crack detection. Finally, we conduct experiments to evaluate the performance of the proposed road crack detection scheme. The experimental results verify the high accuracy of the proposed scheme, and the accuracy of LR, SVM and RFC are 93.3%, 93.3% and 96.7%, respectively.

Great efforts have been devoted to solving the crowd counting problem based on vision or other fine-grained measurements. Popular vision and WiFi channel state information based approaches, though are able to achieve relatively high accuracy, suffer from limited scalability. In contrast, passive WiFi sensing-based approaches are capable of supporting large surveillance areas, but often rely on certain global linear or approximately linear regression models, which cannot accurately capture the complex mapping relationship between WiFi sensing data and the corresponding crowd count, especially in a large surveillance area during a long period of time. This paper addresses the issue from the following three aspects. Firstly, in order to combat with these coarse-grained regression models, the large surveillance is partitioned into grids, such that either a local linear model or other implicit local models can be built with respect to each grid. Secondly, sequential WiFi spatial-temporal matrix (SWSTM) is defined in alignment with grids to encode the spatial-temporal information of crowds based on passive WiFi localization and a sliding time window mechanism. Thirdly, the spatial-temporal correlations among crowd features of different grids are mined to better regress such local models by using a recurrent neural network (RNN) with SWSTMs as inputs. Extensive experiments are conducted in a real campus road network with an area of about$4000m^{2}$, and demonstrate that the proposed method significantly reduces the counting error rate from 22.54% to 13.44% compared to several state-of-the-art methods.

With the growing number of cars in cities, smart parking is gradually becoming a strategic issue in building a smart city. As the precondition in smart parking, accurate parking prediction can reduce the time drivers spend searching for parking spaces and relieve traffic congestion. Meanwhile, VANET and the Internet-of-things (IoT) are the key elements of the current intelligent transportation system. With the IoT devices based on VANET becoming more extensively employed, a large amount of parking data is generated every day, and various methods are proposed for parking prediction, therefore, it is time to systematically summarize the parking prediction issues and the state-of-the-art prediction methods. In this survey, we first provide a comprehensive review of the existing methods used for parking prediction ranging from conventional statistical methods to the latest graph neural network methods. Then, we classify a variety of parking problems such as parking availability prediction, parking behavior prediction, and parking demand prediction. We also compile all the evaluation metrics, open data, and open-source code of the surveyed literature. Finally, we present the challenges and future directions of the parking prediction technique. As far as we know, this is the first survey exploring parking prediction methods, which will be of interest to both researchers and practitioners engaging in intelligent transportation systems (ITS) and smart cities.

Passive WiFi localization refers to determining the location of WiFi-enabled mobile devices by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile devices and measure the corresponding received signal strengths (RSSs) for the use in localization. However, most existing studies fail to consider the effect of multiple channels where WiFi packets are transmitted and sniffed. The problem is further exacerbated by device heterogeneity occurring across various mobile devices. In this article, we present a unified deep neural network (DNN)-based solution, termed DHCLoc, to address these two challenges. To be specific, a Cramer–Rao lower bound (CRLB)-based analysis reveals that utilizing multichannel information will benefit localization, motivating us to include channel information into DHCLoc. Moreover, a novel maximum likelihood estimation (MLE)-based localization framework is introduced by incorporating a new variable to characterize the RSS measurement offsets caused by device heterogeneity, inspiring us to apply adversarial training to adopt such offsets against device heterogeneity. Extensive experiments using two real-world data sets are conducted, and show that, in comparison with several existing methods, DHCLoc can improve the localization accuracy by at least 25.2% and 25.8%, respectively.

With the acceleration of a new round of global scientific, technological, and industrial revolution, the next generation of information and communication technology, i.e., 6G, will inject new momentum into industry transformation and upgrading, as well as into economic innovation and development.This will subsequently promote a global industrial integration.Wireless communication will be ubiquitous in all areas of future society, supporting novel applications with various performance requirements, such as immersive-or interactive-experience applications requiring a large bandwidth, autonomous driving and vehicle-to-everything applications requiring ultrahigh reliability and ultra-low latency, and applications for industrial Internet requiring massive machine-type connectivity.Facing the challenges of the post-Moore and post-pandemic era, wireless communication needs breakthroughs in network architecture to improve the intelligence, security, robustness, bandwidth, and heterogeneity.With this background, several important tendencies have emerged in the development of 6G wireless communications

Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and contribute to lower fuel economy, even increasing the collision risk. Cooperative control for connected and automated vehicles (CAVs) has the potential to significantly reduce the negative impact and improve safety and traffic efficiency. Implementation of cooperative on-ramp merging requires the assistance of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, wherein the communication delay may cause negative impact on CAV cooperative control. In this paper, scenario of on-ramp merging for CAVs considering the V2I communication delay are studied. Statistical characteristics of the V2I communication delay are explored from both literature and real field test, and a communication delay estimation model based on statistical techniques are proposed. Specifically, we firstly model the CAV on-ramp merging scenario using optimal control in ideal situation. Then, several statistical characteristics of the V2I communication are investigated especially the probability density function of the V2I communication delay in several application scenarios. Further, we proposed a communication delay estimation model and used the modified vehicle state to compute the corresponding control law. Real field test of V2I communication delay indicated that distribution of V2I communication delay could correlate with the application scenario and normal distribution can be generally adopted to approximate the probability density function (PDF) when the number of samples is large enough. Numerical simulation of the CAV on-ramp merging scenario considering the V2I communication delay revealed that dynamic performance of the control process would be deteriorated impacted by the V2I communication delay and it might further impact the final control effect and lead to potential lateral collision in the merging area.

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

Identifying the root cause of congestion and taking appropriate strategies to improve traffic network performance are important goals of Advanced Traffic Management Systems (ATMS). On many occasions, the causes of congestion are not necessarily attributable to road infrastructures themselves. Instead, signal control strategies at intersections are very often the major contributors of congestion. In lieu of this, in this paper, a root cause identification method is developed with consideration of the impact from both road infrastructure and traffic signal control. Firstly, we differentiate congestion effects between road segments and intersections to attribute the causes of congestion to road infrastructure and signal control respectively. Then, we construct causal congestion trees to model congestion propagation and quantify congestion costs for each road segment and intersection in the whole road network. A Markov model is utilized to capture congestion spatio-temporal correlation among multiple road segments and intersections simultaneously, with which the most critical root cause can be located. Furthermore, a gradient boosting decision tree based method is presented to predict the root cause of congestion according to traffic flows, signal control strategies and road topology in traffic networks. Finally, simulations based on Simulation of Urban Mobility (SUMO) validate the effectiveness of our proposed method in identifying and predicting the congestion root cause. Experiments are further conducted using inductive loop detector data to identify the root cause for the road network of Taipei.

Traffic incidents are recognized as a key contributor to non-recurrent congestion, which causes many negative effects in economy, environment, health and lifestyle. In this article, we investigate an incident management policy considering both signal control and route choice, which presents a real-time systematic effort to provide a rapid recovery from an incident and mitigate incident-related congestion according to different incident effects. Firstly, we introduce a route choice method on a multiple-route urban road network with consideration of bottleneck delays. Then, we analyze the route travel costs under incident effects and give the equilibrium existence condition after the occurrence of an incident. Furthermore, combining with the route choice method, a novel traffic signal control policy is proposed and the condition for equilibrium existence is given with the consideration of dynamic signal control and route choice simultaneously. Sufficient conditions for the dynamic road system to be stable are also derived and validated by using Lyapunov stability theorem. The analytical results indicate that opposite signal control policies should be applied in road networks under different incident circumstances and the proposed control policy can achieve the improved recovery rate and system stability than existing control policies in terms of dynamic incident effects in road networks. Finally, numerical results have been conducted to demonstrate the effectiveness of our proposed incident control policy and confirm the conditions for road system stability when different incident circumstances had been identified.

In this paper, we theoretically analyse the uplink (UL) and downlink (DL) performance of massive multiple-input and multiple-output (mMIMO) networks, in term of coverage probability, cell spectral efficiency and network area spectral efficiency, using stochastic geometry. A sophisticated but yet practical system model is considered, taking into account a path loss model differentiating line-of-sight and non-line-of-sight transmissions, an idle mode capability at the base stations and a finite user density. Our analysis pays particular attention to the existence of a finite number of UL pilots for channel estimation and the effect of pilot contamination. We study for the first time the joint impact of the number of UL pilot sequences, the user density, and the base-station density on the pilot contamination issue in a mMIMO network, which in turn characterizes the DL and UL network performance. Moreover, using the proposed framework, we investigate two different scheduling problems—UE and pilot scheduling—, to find the optimal simultaneously scheduled UE density per time-frequency resource as well as the optimal UL pilot number to maximise the spectral efficiency.

The vision for smart city imperiously appeals to the implementation of Internet-of-Things (IoT), some features of which, such as massive access and bursty short packet transmissions, require new methods to enable the cellular system to seamlessly support its integration. Rigorous theoretical analysis is indispensable to obtain constructive insight for the networking design of massive access. In this paper, we propose and define the notion of massive and sporadic access (MSA) to quantitatively describe the massive access of IoT devices. We evaluate the temporal correlation of interference and successful transmission events, and verify that such correlation is negligible in the scenario of MSA. In view of this, in order to resolve the difficulty in any precise spatio-temporal analysis where complex interactions persist among the queues, we propose an approximation that all nodes are moving so fast that their locations are independent at different time slots. Furthermore, we compare the original static network and the equivalent network with high mobility to demonstrate the effectiveness of the proposed approximation approach. The proposed approach is promising for providing a convenient and general solution to evaluate and design the IoT network with massive and sporadic access.

We discover a new capacity scaling law in ultra-dense networks under practical system assumptions, such as a general multi-piece path loss model, a non-zero base station to user equipment antenna height difference, and a finite user equipment density. The intuition and implication of this new capacity scaling law are completely different from those found in the year 2011. That law indicated that the increase of the interference power caused by a denser network would be exactly compensated by the increase of the signal power due to the reduced distance between transmitters and receivers, and thus, network capacity should grow linearly with network densification. However, we find that both the signal and interference powers become bounded in practical ultra-dense networks, which leads to a constant capacity scaling law. Moreover, our new discovery on the constant capacity scaling law indicates three network optimization problems respectively for base station deployment, user equipment scheduling and base station coordination. These three optimization problems are justified and solved in this paper, shedding new light on the deployment and optimization of ultra-dense networks.

In public places, even if pedestrians do not have their mobile devices connected with any WiFi access point (AP), WiFi probe requests will be broadcast, so that WiFi sniffers can be employed to crowdsource these WiFi probe packets for use. This paper tackles the problem of exploiting the passive WiFi sensing approach for pedestrian flow analysis. To be specific, a passive WiFi sensing model is first established based on a probabilistic analysis of interactions between WiFi sniffers and the moving pedestrian flow, capturing the main factors affecting pedestrian flow characteristics. On that basis, a sequential filtering algorithm is proposed based on the Rao-Blackwellized particle filter (RBPF) to produce simultaneous and efficient estimates of the pedestrian flow speed and pedestrian number utilizing the real-time sniffing results. In order to validate this study, an experimental pedestrian surveillance system using WiFi sniffers is deployed at the transfer channel of a metro station in Guangzhou, China. Extensive experiments are conducted to verify the passive sensing model, and confirm the effectiveness and advantages of the proposed algorithm. The pedestrian flow estimation not only helps to improve the safety and facility management and customer services, but also paves the way for introducing other novel applications.

Identifying the root cause in urban road networks and ranking the influential factors can benefit traffic management for improving traffic condition. Traditional congestion identification studies paid attention to identify traffic bottlenecks, namely the most vulnerable points in a road network, without consideration of root causes that leading to the congestion. In this paper, we propose a gradient boosting decision trees (GBDTs) based method to identify the root cause of road network congestion and rank the influential factors using different types of explanatory variables. Based on Sioux Falls network, different signal control strategies at intersections and number of lanes on road segments under different traffic flows are conducted as samples using Simulation of Urban Mobility (SUMO) to train and test the GBDT model. Simulation results indicate that the GBDT model can achieve superior performance in average travel speed prediction and identify the root causes of congestion by prioritizing the relative importance of influential factors, such as lane numbers and signal control strategies, compared with other algorithms.

Transforming our roads into smart roads is an indispensable step towards future self-driving systems, and therefore has drawn increasing attention from both academia and industry. To this end, this paper develops a novel cost-effective IoT-based target detection system utilizing the multi-sensor data fusion technology with a particular focus on pedestrian detection, as an important component of smart road system. Particularly, the developed intelligent pedestrian detection module (${i}$PDM) consists of three major sensors, i.e., Doppler microwave radar sensor, passive infrared (PIR), and geomagnetic sensor. A multi-sensor data fusion algorithm is developed to fuse the sensor data and achieves reliable target detection. After that, ${i}$PDM sends the relevant warning signal wirelessly to nearby base station and vehicles. Experiments are conducted on real traffic environment to evaluate the performance of ${i}$PDM. The results validate the high reliability of ${i}$PDM with an average 91.7% detection accuracy. Moreover, to our best knowledge, ${i}$PDM is the first IoT-based implementation for pedestrian detection of smart roads. It is necessary to highlight that ${i}$PDM is a low-cost, low-power, wide-coverage pedestrian detection system where the cost of a single ${i}$PDM is only US $ 30, which makes it suitable to large-scale deployment.

The fifth generation (5G) wireless communication networks are expected to support communications with high mobility, e.g., with a speed up to 500 km/h. Hence 5G communications will have numerous applications in high mobility scenarios, such as high speed railways (HSRs), vehicular ad hoc networks, and unmanned aerial vehicles (UAVs) communications [1]-[3]. The 5G systems will provide advanced communication platforms enabling reliable transmission for the Wireless Train Backbone (WLTB) or Wireless Train Control & Management System (WTCMS) [4], [5]. They will also enable new services or enhancements for vehicular communications in Intelligent Transportation System (ITS) [6]-[9]. The coordination and swarming control for UAVs will also benefit from 5G capabilities, as UAV-based 5G infrastructure modeling and improvement have begun receiving attention [10]. In general, high mobility communication is not only about how large is the maximum speed, it is more about the challenges caused by mobility. In high mobility scenarios, a wireless channel is rapidly time varying, Doppler shifts and spreads can be much larger than those in cellular communications, and if modeled statistically, the channel will be non-wide-sense stationary (non-WSS) over a short time period. In addition, network topology can change quickly, and switching among base stations (BSs) and/or peer nodes can be more frequent, not forgetting 5G challenges in cross-border mobility [11].

The traffic in wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications and the complexity of scenarios. The purpose of this paper is to quantitatively analyze the impact of the wireless traffic, which fluctuates both spatially and temporally, on the performance of the wireless networks. Specially, we propose to combine the tools from stochastic geometry and queueing theory to model the spatial and temporal fluctuation of traffic, which to our best knowledge has seldom been evaluated analytically. We derive the spatial and temporal statistics, the total arrival rate, the stability of queues and the delay of users by considering two different spatial properties of traffic, i.e., the uniformly and non-uniformly distributed cases. The numerical results indicate that although the fluctuation of traffic (reflected by the variance of total arrival rate) when the users are clustered is much fiercer than that when the users are uniformly distributed, the unstable probability is smaller. Our work provides a useful reference for the design of wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.

Secure message dissemination is an important issue in vehicular networks, especially considering the vulnerability of vehicle-to-vehicle message dissemination to malicious attacks. Traditional security mechanisms, largely based on message encryption and key management, can only guarantee secure message exchanges between a known source and destination pairs. In vehicular networks, however, every vehicle may learn its surrounding environment and contributes as a source, while in the meantime, acting as a destination or a relay of information from other vehicles, and message exchanges often occur between “stranger” vehicles. This makes secure message dissemination against malicious tampering much more intricate. For secure message dissemination in vehicular networks against insider attackers, who may tamper the content of the disseminated messages, ensuring the consistency and integrity of the transmitted messages becomes a major concern which the traditional message encryption and key management-based approaches fall short to provide. However, it is challenging for a vehicle to distinguish which message is true when the messages received from multiple nearby vehicles are conflicting. In this paper, by incorporating the underlying network topology information, we propose an optimal decision algorithm that is able to maximize the chance of making a correct decision on the message content, assuming the prior knowledge of the percentage of malicious vehicles in the network. Furthermore, a novel heuristic decision algorithm is proposed that can make decisions without the aforementioned knowledge of the percentage of malicious vehicles. The simulations are conducted to compare the security performance achieved by our proposed decision algorithms with that achieved by the existing ones that do not consider or only partially consider the topological information to verify the effectiveness of the algorithms. Our results show that by incorporating the network topology information, the security performance can be much improved. This paper sheds light on the optimum algorithm design for secure message dissemination.

Motivated by the network tomography, in this paper, we present a novel methodology to estimate link travel time distributions (TTDs) using end-to-end (E2E) measurements detected by the limited traffic detectors at or near the road intersections. As it is not necessary to monitor the traffic in each link, the proposed estimator can be readily implemented in real life. The technical contributions of this paper are as follows: First, we employ the kernel density estimator (KDE) to model link travel times instead of parametric models, e.g., Gaussian distribution. It is able to capture the dynamic of link travel times that vary with the change of road conditions. The model parameters are estimated with the proposed C-shortest path algorithm, K-means-based algorithm, as well as expectation maximization (EM) algorithm. Second, to reduce the complexity of parameter estimation, we further propose a Q-opt and an X-means -based algorithm. Finally, we validate our proposed method using a dataset consisting of 3.0e +07 GPS trajectories collected by the taxicabs in Xi'an, China. With the metrics of Kullback Leibler and Kolmogorov-Smirnov test, the experimental results show that the link TTDs obtained from our proposed model are in excellent agreement with the empirical distributions, provided that ~70% of the intersections are equipped with traffic detectors.

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green time at each intersection swaps from less pressurized stages to more pressurized stages, flow may diverge at each intersection from one OD pair to other OD pairs. Unlike the dynamical system model, where bottleneck delays need to be intentionally constructed to yield the equilibrium flow vector and green-time proportion vector, we propose a novel control policy to fill the gap by only adjusting the green-time proportion vector. We derive a sufficient condition for the existence of equilibrium of the dynamical system under the mild constraints that 1) the travel cost function and stage pressure function should be continuous functions and 2) the flow and green-time proportion swap processes project all flow and green-time proportion vectors on the boundary of the feasible region onto itself. We derive the condition of unique equilibrium for fixed green-time proportion vector and show that with varying green-time proportion vector, the set of equilibria is a compact, non-convex set, and with the same partial derivative of travel cost function with respect to the flow and green-time proportion vectors. Finally, we prove the stability of the proposed dynamical system by using Lyapunov stability analysis.

Traffic data estimation plays an important role because traffic data often suffers from missing data problems, caused by a variety of reasons, i.e., temporary deployment of sensors, sensor malfunction and communication failure. Existing research on missing data estimation has mostly focused on using data-driven or model-driven models to estimate the missing data, and there is a lack of study on the achievable estimation accuracy and the conditions to achieve accurate missing data estimation. In this paper, we investigate the fundamental limits of missing traffic data estimation accuracy in urban networks using the spatial-temporal random effects model. We derive the squared flow error bound (SFEB) for the cases of the Fisher matrix being a singular and non-singular matrix, respectively. We show that the sufficient and necessary condition of the existence of an unbiased estimator is that the number of missing points is less than or equal to the rank of the Fisher matrix. For the case that no unbiased estimator can be found, we derive an inequality for the SPEB and show that the SFEB is readily determined by the covariance matrix of the unknown (missing) parameter vector, flow correlation between the unknown and the available data, and the sensor locations. Furthermore, we develop an optimal spatial-temporal Kriging estimator which is efficient in both cases where the causal relationship among available data points exists or does not exist. Our theoretical findings can be used to develop a sensor location optimization strategy to minimize the SFEB.

Hybrid analog-digital precoding is challenging for broadband millimeter-wave (mmWave) massive MIMO systems, since the analog precoder is frequency-flat but the mmWave channels are frequency-selective. In this paper, we propose a principal component analysis (PCA)-based broadband hybrid precoder/combiner design, where both the fully-connected array and partially-connected subarray (including the fixed and adaptive subarrays) are investigated. Specifically, we first design the hybrid precoder/combiner for fully-connected array and fixed subarray based on PCA, whereby a low-dimensional frequency-flat precoder/combiner is acquired based on the optimal high-dimensional frequency-selective precoder/combiner. Meanwhile, the near-optimality of our proposed PCA approach is theoretically proven. Moreover, for the adaptive subarray, a low-complexity shared agglomerative hierarchical clustering algorithm is proposed to group the antennas for the further improvement of spectral efficiency (SE) performance. Besides, we theoretically prove that the proposed antenna grouping algorithm is only determined by the slow time-varying channel parameters in the large antenna limit. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art schemes in SE, energy efficiency (EE), bit-error-rate performance, and the robustness to time-varying channels. Our work reveals that the EE advantage of adaptive subarray over fully-connected array is obvious for both active and passive antennas, but the EE advantage of fixed subarray only holds for passive antennas.

The proliferation of mobile devices, including smartphones and tablets, has been enabling new possibilities for inferring information about the positions, behavior and activities of the users carrying these devices. For instance, by leveraging the WiFi probes sent out by mobile devices in public spaces (such as shopping malls, metro stations, etc.), even if pedestrians do not have their mobile devices to be associated with any WiFi access point (AP), it is attractive to conduct pedestrian analysis in a passive sensing approach to facilitate the efficient management of public infrastructures as well as convenient customer services. This paper considers the problem of pedestrian flow analysis by implementing a pedestrian surveillance system in the transfer channel of a metro station in Guangzhou China. Firstly, a fingerprint database is generated through a Gaussian process regression (GPR) approach. On these grounds, a pedestrian number estimation method based on linear regression is presented by making use of the fingerprint-based localization method to refine the number of mobile devices residing in the surveillance area, and a pedestrian velocity estimation method is proposed based on particle filter and the inverse distance weighted (IDW) method. According to the dataset obtained in real scenarios, the effectiveness and advantages of the proposed two methods are confirmed.

Fingerprint-based localization relies on an accurate and up-to-date radio map, which is however cumbersome to obtain. In this paper, a novel scheme is proposed to online adapt radio maps to environmental dynamics by using low-cost crowdsourced received signal strength (RSS) measurements. To be specific, a coarse-grained radio map is initially established in the offline phase utilizing the standard Gaussian process regression (GPR) given a limited number of fingerprints (i.e., RSS measurements with location labels), and further can be recursively refined in the online phase given crowdsourced RSS measurements with their noisy location labels obtained through the existing radio map. Differently from existing GPR-based approaches, the proposed scheme adopts extended GPR to alleviate the model inaccuracy induced by such noisy location labels, and then presents a marginalized particle extended Gaussian process (MPEG) to recursively filter the radio map. In addition, pedestrian dead reckoning (PDR) is leveraged to calibrate such noisy location labels. Extensive experiments are carried out in a real scenario with area of nearly 1000 m2during a five-month period of time, and a thorough comparison with several existing approaches indicates that the proposed scheme gradually improves the localization accuracy on average by as much as 31.2%, while the counterparts result in fluctuant localization performance and improve the localization accuracy on average by 13.3%.

An increasing number of low-power Internet of Things (IoT) devices will be widely deployed in the near future. Considering the short-range communication of low-power devices, multihop transmissions will become an important transmission mechanism in IoT networks. It is a crucial for low-power devices to transmit data over long distances via multihop in a low-delay and reliable way. The small-world characteristics of networks indicate that the network has an advantage of a small average shortest-path length (ASL) and a high average clustering coefficient (ACC). In this article, a new IoT routing mechanism considering small-world characteristics is proposed to reduce the delay and improve the reliability. The ASL and ACC are derived for the performance analysis of small-world characteristics in IoT networks based on Cayley graphs. Besides, the reliability and delay models are proposed for small-world IoT based on Cayley graphs (SWITCH). The simulation results demonstrate that SWITCH has lower delay and better reliability than that of conventional nearest neighboring routing (NNR). Moreover, the maximum delay of SWITCH is reduced by 50.6% compared with that by NNR.

Fog computing has risen as a promising architecture for future Internet of Things, 5G and embedded artificial intelligence applications with stringent service delay requirements along the cloud to things continuum. For a typical fog network consisting of heterogeneous fog nodes (FNs) with different computing resources and communication capabilities, how to effectively schedule complex computation tasks to multiple FNs in the neighborhood to achieve minimal service delay is a fundamental challenge. To tackle this problem, a new concept named processing efficiency (PE) is first defined to incorporate computing resources and communication capacities. Further, to minimize service delay in heterogeneous fog networks, a scalable, stable, and decentralized algorithm, namely dispersive stable task scheduling (DATS), is proposed and evaluated, which consists of two key components: 1) a PE-based progressive computing resources competition and 2) a QoE-oriented synchronized task scheduling. Theoretical proofs and simulation results show that the proposed DATS algorithm can achieve effective tradeoff between computing resources and communication capabilities, thus significantly reducing service delay in heterogeneous fog networks.

In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the edge cloud. We present a comprehensive framework model and formulate a novel multivehicle and multitask offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. We invoke Lyapunov optimization framework to design TaskSche, an online task scheduling algorithm, which only utilizes the current system information. As the core components of the algorithm, we propose a task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy. Rigorous theoretical analyses and extensive trace-driven simulations indicate that our framework achieves superior performance (e.g., 20%-68% energy saving without overstepping application deadlines for network-wide vehicles compared with vehicle local processing) and scales well for a large number of vehicles and applications.

This paper proposes a unified spatio-temporal model for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amendable to suit changing road and traffic conditions. Second, unlike some existing techniques that capture the variation of spatio-temporal correlation by a complete re-design and calibration of the model, the proposed approach uses a unified model that incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two sets of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used space-time autoregressive integrated moving average (STARIMA) and the back propagation neural network approaches, and is only marginally inferior to that obtained by constructing multiple STARIMA models for different times of the day, however, with a much reduced computational and implementation complexity.

Traffic data imputation has drawn significant attention from both academia and industry because traffic data often suffer from data missing problems, caused by temporary deployment of sensors, detector malfunction, and lossy communication systems. To fully exploit the spatial-temporal correlation and road topological information in an urban traffic network, we propose an optimum closed cut (OCC)-based spatio-temporal imputation technique, which is implemented in two stages: a) employing graph theory to search the OCC in the road network, for which the traffic on roads intersected by the closed cut has the maximum correlation with that on the target road while minimizing the number of intersected roads; and b) estimating the missing data on the target road using OCC-based Kriging estimator, incorporating both the road topological information and flow conservation law to improve the estimation accuracy. Experimental results using traffic data collected on real roads indicate that the OCC search algorithm can effectively capture the optimum set of neighboring sensors. An OCC-based estimator can provide more accurate imputation results compared with nearest historical average and correlative k-NN (k-nearest neighbors) methods. The road topological information and flow conservation law can be explored to further improve the estimation performance while reducing the number of sensors involved in the data imputation, hence improving the computational efficiency.

As a hierarchical network architecture, the cluster architecture can improve the routing performance greatly for vehicular ad hoc networks (VANETs) by grouping the vehicle nodes. However, the existing clustering algorithms only consider the mobility of a vehicle when selecting the cluster head. The rapid mobility of vehicles makes the link between nodes less reliable in cluster. A slight change in the speed of cluster head nodes has a great influence on the cluster members and even causes the cluster head to switch frequently. These problems make the traditional clustering algorithms perform poorly in the stability and reliability of the VANET. A novel passive multi-hop clustering algorithm (PMC) is proposed to solve these problems in this paper. The PMC algorithm is based on the idea of a multi-hop clustering algorithm that ensures the coverage and stability of cluster. In the cluster head selection phase, a priority-based neighbor-following strategy is proposed to select the optimal neighbor nodes to join the same cluster. This strategy makes the inter-cluster nodes have high reliability and stability. By ensuring the stability of the cluster members and selecting the most stable node as the cluster head in the N-hop range, the stability of the clustering is greatly improved. In the cluster maintenance phase, by introducing the cluster merging mechanism, the reliability and robustness of the cluster are further improved. In order to validate the performance of the PMC algorithm, we do many detailed comparison experiments with the algorithms of N-HOP, VMaSC, and DMCNF in the NS2 environment.

The wireless backhaul network provides an attractive solution for the urban deployment of fifth generation (5G) wireless networks that enables future ultra dense small cell networks to meet the ever-increasing user demands. Optimal deployment and management of 5G wireless backhaul networks is an interesting and challenging issue. In this paper, we propose the optimal gateways deployment and wireless backhaul route schemes to maximize the cost efficiency of 5G wireless backhaul networks. In generally, the changes of gateways deployment and wireless backhaul route are presented in different time scales. Specifically, the number and locations of gateways are optimized in the long time scale of 5G wireless backhaul networks. The wireless backhaul routings are optimized in the short time scale of 5G wireless backhaul networks considering the time-variant over wireless channels. Numerical results show the gateways and wireless backhaul route optimization significantly increases the cost efficiency of 5G wireless backhaul networks. Moreover, the cost efficiency of proposed optimization algorithm is better than that of conventional and most widely used shortest path (SP) and Bellman-Ford (BF) algorithms in 5G wireless backhaul networks.

Pushing the cloud frontier to the network edge close to mobile users has attracted tremendous interest not only from cloud operators but also from network service providers. In particular, the deployment of cloudlets in metropolitan area networks enables network service providers to provide low-latency services to mobile users through implementing their specified virtualized network functions (VNFs) while meeting their Quality-of-Service (QoS) requirements. In this paper, we formulate a novel task offloading problem in a mobile edge-cloud network, where each offloading task requests a specified network function with a tolerable delay. We aim to maximize the number of requests admitted while minimizing the operational cost of admitted requests within a finite time horizon, through either sharing existing VNF instances or creating new VNF instances in cloudlets. We first show that the problem is NP-hard, and then devise an efficient online algorithm for the problem by reducing it to a series of minimum weight maximum matching problems. Considering dynamic changes of task offloading request patterns over time, we further develop an effective prediction mechanism for new VNF instance creations and idle VNF instance releases to further lower the operational cost of the network service provider. Also, we devise an online algorithm with a competitive ratio for a special case of the problem where the delay requirements of requests are negligible. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results indicate that the proposed algorithms are promising.

Very recent studies showed that the area spectral efficiency (ASE) of downlink cellular networks will continuously decrease and finally crash to zero as the base station (BS) density increases toward infinity if the absolute height difference between BS antenna and user equipment antenna is larger than zero. Such a phenomenon is referred to as the ASE crash. We revisit this issue by considering optimizing the BS antenna downtilt in cellular networks. It is common to adjust antenna pattern to tune the direction of the vertical beamforming and thus increasing received signal power and/or reducing inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the BS antenna downtilt and the downlink network performance in terms of the coverage probability and the ASE. Our results reveal an interesting find that there exists an optimal antenna downtilt to achieve the maximum coverage probability for each BS density. Numerically solvable expressions are derived for such optimal antenna downtilt, which is a function of the BS density. Our numerical results show that after applying the optimal antenna downtilt, the network performance can be significantly improved, and hence the ASE crash can be delayed by nearly one order of magnitude in terms of the BS density. Our results also give guidance on setting the optimum downtilt angle to maximize network performance given a fixed BS density.

The study of full duplex (FD) is important because it has been identified as one of the candidate technologies for the 5th-generation (5G) networks. Small cell networks (SCNs) are envisioned to embrace the FD transmission technology in order to increase the spectral efficiency of wireless systems. In this paper, for the first time, we consider FD communications in a realistic small cell networks (SCN) scenario, where base stations (BSs) can select FD or half-duplex (HD) mode according to the real-time downlink (DL)/uplink (UL) traffic. We present analytical results on the probabilities of BS mode selection, which match the simulation results well.

Cell discontinuous transmission (Cell DTx) improves network performance because it help mitigate inter-cell interference. However, the relationship between this technology and network performance has been little studied. The aim of this work is to understand the impact of Cell DTx on users’ quality of service (QoS) performance of multiuser ultra-dense networks (UDNs). We extend the traditional one-dimensional effective capacity model and develop a new multidimensional framework for UDNs with Cell DTx, which can be applied to different scheduling policies. Under the round-robin and the max-C/I scheduling examples, computer simulation shows that the analytical and simulation results are in good agreement and thus validate the accuracy of our proposed framework.

Recently, it has been standardized by the 3rd Generation Partnership Project (3GPP) [1] that device-to-device (D2D) communications should use uplink resources when coexisting with conventional cellular communications. With uplink resource sharing, both cellular and D2D links cause significant co-channel interference. In this paper, we consider a D2D mode selection criterion based on the maximum received signal strength (MRSS) for each user equipment (UE) to control the D2D-to-cellular interference. Specifically, a UE will operate in a cellular mode, if its received signal strength from the strongest base station (BS) is larger than a threshold β; otherwise, it will operate in a D2D mode. Furthermore, in our study, cellular UEs, D2D transmit UEs and D2D receiver UEs constitute the entire UE set, which is a more practical assumption than dropping more UEs for D2D reception only in existing works. The coverage probability and the area spectral efficiency (ASE) are derived for both the cellular network and the D2D one. Through our theoretical and numerical analyses, we quantify the performance gains brought by D2D communications and provide guidelines for selecting the parameters for network operations.

Secure message dissemination is an important issue in vehicular networks, especially considering the vulnerability of vehicle to vehicle (V2V) message dissemination to malicious attacks. Traditional security mechanisms, largely based on message encryption and key management, can only guarantee secure message exchanges between known source and destination pairs. For secure message dissemination in vehicular networks against insider attackers, who may tamper the content of the disseminated messages, ensuring the consistency and integrity of the transmitted messages becomes a major concern that traditional message encryption and key management based approaches fall short to address. In this paper, by incorporating the underlying network topology information, we propose a novel heuristic decision algorithm that enables a vehicle to make a decision on the message content using minimal information readily available. The proposed algorithm can be readily implemented in practice. Simulations are conducted to compare the security performance achieved by the proposed decision algorithm with that achieved by existing ones that do not consider or only partially consider the topological information, to establish the effectiveness of the proposed algorithm. Our results show that by incorporating the network topology information, the security performance can be significantly improved. This work sheds light on the optimum algorithm design for secure message dissemination in vehicular networks.

© 2018 IEEE. Neighbor discovery, providing the neighbor information by broadcasting discovery messages, is a promising solution for cooperative perception of Intelligent Vehicles (IVs). However, the high vehicle mobility and severe channel randomness of IV environments call for a frequent discovery, which results in a superabundant overhead. In this paper, we propose a new framework for cooperative perception of IVs by novelly introducing an improved neighbor discovery method. We first establish an analytical framework to capture the quantitive relation between the hitting probability of neighbor discovery with the vehicle mobility and channel randomness using a closed-form expression. Based on the analysis, an adaptive neighbor discovery method is developed to adaptively make tradeoff between the discovery accuracy and overhead at varying driving status of IVs. Applying the improved neighbor discovery, the process of cooperative perception is discussed. Accordingly, simulations in three IV scenarios are conducted whose results are consistent with our analysis.

The aggressive spatial spectrum reuse (SSR) by network densification using smaller cells has successfully driven the wireless communication industry onward in the past decades. In our future journey toward ultra-dense networks (UDNs), a fundamental question needs to be answered. Is there a limit to SSR? In other words, when we deploy thousands or millions of small cell base stations (BSs) per square kilometer, is activating all BSs on the same time/frequency resource the best strategy? In this paper, we present theoretical analyses to answer such question. In particular, we find that both the signal and interference powers become bounded in practical UDNs with a non-zero BS-to-UE antenna height difference and a finite UE density, which leads to a constant capacity scaling law. As a result, there exists an optimal SSR density that can maximize the network capacity. Hence, the limit to SSR should be considered in the operation of future UDNs.

Traffic congestion has seriously caused various problems in society, economy and environment, especially in urban areas. A traffic bottleneck is always seen as the root cause of congestion which frequently deduces the congestion emergence, queues formation and congestion propagation. However, bottlenecks are caused by many complicated factors and vary with spatial and temporal environment which are difficult to be defined and identified in urban areas. In this paper, we first propose a novel definition of bottlenecks in urban area based on the congestion propagation costs and the congestion weights of road segments. Then according to the definition, we present an urban bottleneck identification method using causal congestion trees and causal congestion graphs to identify some bottlenecks. This paper implements some experiments based on the urban inductive loop detector data. According to our proposed method, we identify several bottleneck groups around the urban area. Furthermore, we also improve the road capacity of identified bottlenecks and compare the congestion level and congestion propagation range before and after the improvement to verify the identified bottlenecks.

This paper studies the impact of the base station (BS) idle mode capability (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs). Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical setup with a finite number of UEs in our analysis. More specifically, we derive the probability of which BS tier a typical UE should associate to and the expression of the activated BS density in each tier. Based on such results, analytical expressions for the coverage probability and the area spectral efficiency (ASE) in each tier are also obtained. The impact of the IMC on the performance of all BS tiers is shown to be significant. In particular, there will be a surplus of BSs when the BS density in each tier exceeds the UE density, and the overall coverage probability as well as the ASE continuously increase when the BS IMC is applied. Such finding is distinctively different from that in existing work. Thus, our result sheds new light on the design and deployment of the future 5G HCNs.

In this paper, we conduct performance analysis for uplink (UL) massive multiple input and multiple output (mMI-MO) networks using stochastic geometry. With the consideration of practical system assumptions, such as sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions and a finite user equipment (UE) density, we derive the coverage probability and the area spectral efficiency (ASE) performance. In particular, we adopt a practical user association strategy (UAS) based on the smallest pathloss since we differentiate LoS and NLoS transmissions, and we consider the correlation among the positions of UEs and base stations (BSs) in realistic networks. From our simulation and analytical results, we find that the performance impacts of the probabilistic LoS/NLoS transmissions and a finite UE density on UL mMIMO networks are significant. More specifically, the coverage probability performance suffers from a moderate decrease or even a severe degradation when the UE density becomes large in sparse mMIMO networks. Moreover, our results indicate that there exists an optimal BS density to maximize the sum spectral efficiency per BS. However, the ASE performance keeps growing with network densification.

In this paper, we propose a tractable semi-analytical approach for the network performance analysis of uplink (UL) cellular networks, which is based on a deterministic network analysis using a Gaussian approximation (DNA-GA). The key contribution of this paper is to investigate the UL signal-to-interference ratio (SIR) performance using the DNA-GA analysis. In particular, the SIR is modeled as a ratio of two random variables (RVs), representing the signal power and the aggregate interference power, respectively. The signal power is further characterized by a product of two RVs, i.e., a lognormal RV and an RV with an arbitrary distribution. The former RV comes from a common assumption of lognormal shadow fading, and the latter one takes the rest of random factors into account, such as random user positions, arbitrary types of multi-path fading, and so on. The aggregate interference power is approximated by an RV with a power lognormal distribution. The proposed DNA-GA analysis has several desirable features: 1) it naturally considers lognormal shadow fading; 2) it can treat arbitrary shape and/or size of cell coverage areas; 3) it can handle non-uniform user distributions; 4) it can cope with any type of multi-path fading; and 5) it can be applied to multi-antenna base stations. These features make the DNA-GA analysis very useful for the network performance analysis of the 5th generation systems with general cell deployment and user distribution.

To meet massive wireless traffic demand in the future fifth generation (5G) cellular networks, small-cell networks are emerging as an attractive solution for 5G network deployments. The cellular coverage characteristic is a key issue for the deployment of the small-cell networks. Considering the anisotropic path loss in wireless channels of real cellular scenarios, in this paper the fractal coverage characteristic is first used to evaluate the performance of the small-cell networks. Moreover, the coverage probability, average achievable rate, and the area spectral efficiency are derived for fractal small-cell networks. Compared with the average achievable rate and area spectral efficiency with isotropic path loss models, the average achievable rate and area spectral efficiency with anisotropic path loss models have been underestimated in the fractal small-cell networks. Considering the impact of the anisotropic path loss on wireless channels, most of performances of wireless cellular networks need to be re-evaluated. This paper provides a tractable method to investigate the performance of the small-cell networks with fractal coverage characteristics.

In this paper, we study the impact of the base station (BS) idle mode capacity (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs) with both line-of-sight (LoS) and non-line-of-sight transmissions. Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical set-up with a finite number of UEs in our analysis. Moreover, in our model, the small BSs (SBSs) apply a positive power bias in the cell association procedure, so that macrocell UEs are actively encouraged to use the more lightly loaded SBSs. In addition, to address the severe interference that these cell range expanded UEs may suffer, the macro BSs (MBSs) apply enhanced inter-cell interference coordination, in the form of almost blank subframe (ABS) mechanism. For this model, we derive the coverage probability and the rate of a typical UE in the whole network or a certain tier. The impact of the IMC on the performance of the network is shown to be significant. In particular, it is important to note that there will be a surplus of BSs when the BS density exceeds the UE density, and thus a large number of BSs switch off. As a result, the overall coverage probability, as well as the area spectral efficiency, will continuously increase with the BS density, addressing the network outage that occurs when all BSs are active and the interference becomes LoS dominated. Finally, the optimal ABS factors are investigated in different BS density regions. One of major findings is that MBSs should give up all resources in favor of the SBSs when the small cell networks go ultra-dense. This reinforces the need for orthogonal deployments, shedding new light on the design and deployment of the future 5G dense HCNs.

User association has emerged as a distributed resource allocation problem in the heterogeneous networks (HetNets). Although an approximate solution is obtainable using the approaches like combinatorial optimization and game theory-based schemes, these techniques can be easily trapped in local optima. Furthermore, the lack of exploring the relation between the quality of the solution and the parameters in the HetNet [e.g., the number of users and base stations (BSs)], at what levels, impairs the practicability of deploying these approaches in a real world environment. To address these issues, this paper investigates how to model the problem as a distributed constraint optimization problem (DCOP) from the point of the view of the multiagent system. More specifically, we develop two models named each connection as variable (ECAV) and each BS and user as variable (EBUAV). Hereinafter, we propose a DCOP solver which not only sets up the model in a distributed way but also enables us to efficiently obtain the solution by means of a complete DCOP algorithm based on distributed message-passing. Naturally, both theoretical analysis and simulation show that different qualitative solutions can be obtained in terms of an introduced parameter which has a close relation with the parameters in the HetNet. It is also apparent that there is 6% improvement on the throughput by the DCOP solver comparing with other counterparts when . Particularly, it demonstrates up to 18% increase in the ability to make BSs service more users when the number of users is above 200 while the available resource blocks (RBs) are limited. In addition, it appears that the distribution of RBs allocated to users by BSs is better with the variation of the volume of RBs at the macro BS.

In this paper, we propose a cooperative communication strategy that explores the combined use of vehicle-to- infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, mobility of vehicles and cooperation among vehicles and infrastructure to improve the achievable capacity of vehicular network. An analytical framework is developed to model the data dissemination process using this strategy, and a closed form expression of the achievable capacity is obtained, which reveals the relationship between the achievable capacity and its major performance- impacting parameters such as inter-infrastructure distance, radio ranges of infrastructure and vehicles, sensing range of vehicles, transmission rates of V2I and V2V communications, vehicular density and the proportion of vehicles with download requests. Numerical result shows that the proposed cooperative communication strategy significantly increases the capacity of vehicular networks, especially when the proportion of vehicles with download request is low. Our results provide guidance on the optimum deployment of vehicular network infrastructure and the design of cooperative communication strategy to maximize the capacity.

In this paper, we theoretically study the proportional fair (PF) scheduler in the context of ultra-dense networks (UDNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance of dense small cell networks (SCNs) with the PF scheduler employed at base stations (BSs). The key point of our analysis is that the typical user is no longer a random user as assumed in most studies in the literature. Instead, a user with the maximum PF metric is chosen by its serving BS as the typical user. By comparing the previous results of the round-robin (RR) scheduler with our new results of the PF scheduler, we quantify the loss of the multi-user diversity of the PF scheduler with the network densification, which casts a new look at the role of the PF scheduler in UDNs. Our conclusion is that the RR scheduler should be used in UDNs to simplify the radio resource management (RRM).

Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it provides valuable insights on the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. Up to now, the existing works on dynamic TDD have shown that the UL of dynamic TDD suffers from severe performance degradation due to the strong DL-to-UL interference in the physical (PHY) layer. This conclusion raises a fundamental question: Despite such obvious technology disadvantage, what is the true value of dynamic TDD? In this paper, we answer this question from a media access control (MAC) layer viewpoint and present analytical results on the DL/UL time resource utilization (TRU) of synchronous dynamic TDD, which has been widely adopted in the existing 4th-generation (4G) systems. Our analytical results shed new light on the dynamic TDD in future synchronous 5G networks.

In this paper, we propose a unified framework to analyze the performance of dense small cell networks (SCNs) in terms of the coverage probability and the area spectral efficiency (ASE). In our analysis, we consider a practical path loss model that accounts for both non-line-of-sight (NLOS) and line-of-sight (LOS) transmissions. Furthermore, we adopt a generalized fading model, in which Rayleigh fading, Rician fading and Nakagami-m fading can be treated in a unified framework. The analytical results of the coverage probability and the ASE are derived, using a generalized stochastic geometry analysis. Different from existing work that does not differentiate NLOS and LOS transmissions, our results show that NLOS and LOS transmissions have a significant impact on the coverage probability and the ASE performance, particularly when the SCNs grow dense. Furthermore, our results establish for the first time that the performance of the SCNs can be divided into four regimes, according to the intensity (aka density) of BSs, where in each regime the performance is dominated by different factors.

In modern mobile communication networks, such as 3G and 4G networks, sectorized antennas have been widely used to divide each cell into multiple sectors in order to improve coverage, spectrum efficiency, and quality of service. Large-scale path loss from a transmitting antenna to a receiving antenna should include: 1) propagation attenuation that depends on transmission distance; 2) shadowing that depends on surrounding environment; and 3) antenna loss that depends on a sectorized antenna pattern and transmission angle. An in-depth analysis of statistical characteristics of large-scale path losses involving with these three factors is crucial for the design, operation, evaluation, and optimization of modern sectorized wireless networks. In this paper, a sectorized antenna pattern is, for the first time, considered in the derivation of a closed-form expression of a probability density function (pdf) of large-scale path losses. Specifically, we first discover that the normalized pdf of propagation attenuation plus shadowing, which can be approximated by the Gaussian mixture model (GMM) with all system parameters, is fully determined by our newly defined metric 10/ln 10β/σs, namely, the attenuation exponent β to standard deviation of shadowing σsratio (ASR). The convolution of GMM and antenna loss statistics is elaborately transformed to a series of differential equations. A closed-form pdf of large-scale path losses with sectorized antenna pattern can be obtained by solving these differential equations. To reduce the computational complexity, we further prove that the exciting sources of these differential equations can be tightly approximated by weighted Gaussian functions, and thus, the final solutions (i.e., pdf of path losses) can be derived in the form of Gaussian and Dawson functions. Our analytical results are verified by extensive numerical computation and Monte Carlo simulation results, e.g., the impact of ASR on the shape of pdf of propagation attenuation plus shadowing. Compared with traditional Gaussian-fitting approach, our newly derived pdf of large-scale path losses with sectorized antenna patterns is at least two orders of magnitude more accurate in terms of Kullback-Leibler divergence under typical propagation attenuation and shadowing conditions.

In this paper, we provide the detailed analysis of the achievable throughput of infrastructure-based vehicular network with a finite traffic density under a cooperative communication strategy, which explores the combined use of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, the mobility of vehicles, and cooperations among vehicles and infrastructure to facilitate the data transmission. A closed form expression of the achievable throughput is obtained, which reveals the relationship between the achievable throughput and its major performance-impacting parameters, such as distance between adjacent infrastructure points, the radio ranges of infrastructure and vehicles, the transmission rates of V2I and V2V communications, and vehicular density. Numerical and simulation results show that the proposed cooperative communication strategy significantly increases the throughput of vehicular networks, compared with its non-cooperative counterpart, even when the traffic density is low. Our results shed insight on the optimum deployment of vehicular network infrastructure and the optimum design of cooperative communication strategies in vehicular networks to maximize the throughput.

Augmented and/or virtual reality (AR/VR) are emerging as one of the main applications in future fifth-generation (5G) networks. To meet the requirements of lower latency and massive data transmission in AR/VR applications, a solution with software-defined networking architecture is proposed for 5G small cell networks. On this basis, a multipath cooperative route (MCR) scheme is proposed to facilitate the AR/VR wireless transmissions in 5G small cell networks, in which the delay of the MCR scheme is analytically studied. Furthermore, a service effective energy (SEE) optimization algorithm is developed for AR/VR wireless transmission in 5G small cell networks. Simulation results indicate that both the delay and SEE of the proposed MCR scheme outperform the delay and SEE of the conventional single-path route scheme in 5G small cell networks.

Wireless energy transfer has emerged as a promising technology for wireless sensor networks to power sensors with controllable yet perpetual energy. In this paper, we study sensor energy replenishment by employing a mobile charger (charging vehicle) to charge sensors wirelessly in a rechargeable sensor network, so that the sum of charging rewards collected from all charged sensors by the mobile charger per tour is maximized, subject to the energy capacity of the mobile charger, where the amount of reward received from a charged sensor is proportional to the amount of energy charged to the sensor. The energy of the mobile charger will be spent on both its mechanical movement and sensor charging. We first show that this problem is NP-hard. We then propose approximation algorithms with constant approximation ratios under two different settings: one is that a sensor will be charged to its full energy capacity if it is charged; another is that a sensor can be charged multiple times per tour but the total amount of energy charged is no more than its energy demand prior to the tour. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are very promising, and the solutions obtained are fractional of the optimum. To the best of our knowledge, the proposed algorithms are the very first approximation algorithms with guaranteed approximation ratios for the mobile charger scheduling in a rechargeable sensor network under the energy capacity constraint on the mobile charger.

In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-ofsight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 101~ 103BSs/km2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 102~ 103BSs/km2, while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 101 ~ 102 BSs/km2.

Cooperative geolocation has attracted significant research interests in recent years. A large number of localization algorithms rely on the availability of statistical knowledge of measurement errors, which is often difficult to obtain in practice. Compared with the statistical knowledge of measurement errors, it can often be easier to obtain the measurement error bound. This paper investigates a localization problem assuming unknown measurement error distribution except for a bound on the error. We first formulate this localization problem as an optimization problem to minimize the worst case estimation error, which is shown to be a nonconvex optimization problem. Then, relaxation is applied to transform it into a convex one. Furthermore, we propose a distributed algorithm to solve the problem, which will converge in a few iterations. Simulation results show that the proposed algorithms are more robust to large measurement errors than existing algorithms in the literature. Geometrical analysis providing additional insights is also provided.

In this paper, we study the impact of the base station (BS) idle mode capability (IMC) on the network performance in dense small cell networks (SCNs). Different from existing works, we consider a sophisticated path loss model incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions. Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance for SCNs with IMCs at the BSs. The upper bound, the lower bound and the approximate expression of the activated BS density are also derived. The performance impact of the IMC is shown to be significant. As the BS density surpasses the UE density, thus creating a surplus of BSs, the coverage probability will continuously increase toward one. For the practical regime of the BS density, the results derived from our analysis are distinctively different from existing results, and thus shed new light on the deployment and the operation of future dense SCNs.

Due to the time-varying channel conditions and dynamic topology of vehicular networks attributable to the high mobility of vehicles, data dissemination in vehicular networks, especially for content of large-size, is challenging. In this paper, we propose a cooperative communication strategy for vehicular networks suitable for dissemination of large-size content and investigate its achievable throughput. The proposed strategy exploits the cooperation of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications and the mobility of vehicles to facilitate the transmission. Detailed analysis is provided to characterize the data dissemination process using this strategy and a closed-form result is obtained on its achievable throughput, which reveals the relationship between major performance-impacting parameters such as distance between infrastructure, radio ranges of infrastructure and vehicles, transmission rates of V2I and V2V communications and vehicular density. Simulation and numerical results show that the proposed strategy significantly increases the throughput of vehicular networks even when the traffic density is low. The result also gives insight into the optimum deployment of vehicular network infrastructure to maximize throughput.

© 2002-2012 IEEE. In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 10-1∼ 10-3 BSs/km2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 10-2∼ 10-3 BSs/km2 , while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 10-1∼ 10-2,BSs/km-2.

In this paper, we propose a new approach of network performance analysis, which is based on our previous works on the deterministic network analysis using the Gaussian approximation (DNA-GA). First, we extend our previous works to a signal-to-interference ratio (SIR) analysis, which makes our DNA-GA analysis a formal microscopic analysis tool. Second, we show two approaches for upgrading the DNA-GA analysis to a macroscopic analysis tool. Finally, we perform a comparison between the proposed DNA-GA analysis and the existing macroscopic analysis based on stochastic geometry. Our results show that the DNA-GA analysis possesses a few special features: (i) shadow fading is naturally considered in the DNA-GA analysis; (ii) the DNA-GA analysis can handle non-uniform user distributions and any type of multi-path fading; (iii) the shape and/or the size of cell coverage areas in the DNA-GA analysis can be made arbitrary for the treatment of hotspot network scenarios. Thus, DNA-GA analysis is very useful for the network performance analysis of the 5th generation (5G) systems with general cell deployment and user distribution, both on a microscopic level and on a macroscopic level.

Non-line-of-sight (NLOS) propagation, which widely exists in wireless systems, will degrade the performance of wireless positioning system if it is not taken into consideration in the localization algorithm design. The 3rd Generation Partnership Project (3GPP) suggests that the probabilities of line-of-sight (LOS) and NLOS are related to the distance between the receiver and the transmitter. In this paper, we propose a Maximum Likelihood Estimator (MLE) for localization, which incorporates the distance dependent LOS/NLOS probabilities. Then, the position error bound is derived using Cramer-Rao Lower Bound (CRLB). Through numerical analysis, the impact of NLOS propagation on the position error bound is evaluated. The performance of our proposed algorithm is verified by real world experimental data.

In this article, a network model incorporating both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions is proposed to investigate impacts of blockages in urban areas on heterogeneous network coverage performance. Results show that co-existence of NLOS and LOS transmissions has a significant impact on network performance. We find in urban areas, that deploying more BSs in different tiers is better than merely deploying all BSs in the same tier in terms of coverage probability.

The precise spatio-temporal position data of vehicles is useful for most studies, such as wireless link lifetime and node degree in vehicular ad hoc networks. However, due to the system errors and random errors, the existing Global Positioning System (GPS) only provides the positional accuracy about 10m or even worse. In this paper, to address the issue of positional accuracy, a Clustering and Approximating (C-A) algorithm is proposed. We first divide each road into several small parts which are described by linear functions. Then a linear regression algorithm is utilized to approximate traces under system errors, which is reliable for reducing GPS errors. Particularly, when two roads are very close, GPS points may be mapped on adjacent roads. A clustering algorithm is taken to separate GPS points and their positions are revised by the iterative utilization of the linear regression algorithm. In the end, the method mentioned above smoothes raw GPS data of buses in Taiwan to make it available for further researches. Compared with existing methods, the method described in this paper characterized with low cost and high reliability in different situations. Besides, its simple model will make the process of revising data more convenient.

Cooperative inter-working of the long-term evolution (LTE) and the wireless fidelity (Wi-Fi) networks have drawn much attention recently, and several strategies have been proposed to enhance their network capacity. In this paper, we propose a new framework to analyze the network performance of several inter-working strategies for the LTE and the Wi-Fi. The proposed framework considers both the LTE and the Wi-Fi systems, both the downlink (DL) and the uplink (UL) transmissions, and the generated interference in both the time and the spatial domains. Based on such a framework, we theoretically analyze for the first time the performance of a Wi-Fi network, taking into account the intra-cell time efficiency and the signal and inter-cell interference with spatial randomness. Moreover, we study the performance of: 1) a coexisting architecture where Wi-Fi coexists with an ideal carrier sense multiple access (CSMA) duplex system, which represents an upper bound performance for the LTE Release 14 licensed assisted access network and 2) a brand-new architecture that allows UL on LTE and DL on Wi-Fi, referred to as the Boost architecture. We derive analytical results for both the DL and the UL network performances in terms of the signal quality distribution and the total area system throughput (AST) in these two architectures, and quantify their performance gain compared with the traditional disjoint LTE Wi-Fi architecture. Simulation results validate our analysis results, and show that, in a typical outdoor scenario, the coexisting architecture and the Boost architecture can, respectively, increase the total AST up to 11% and 25%, compared with the traditional disjoint LTE Wi-Fi.

We investigate a localization problem using time-difference-of-arrival measurements with unknown and bounded measurement errors. Different from most existing algorithms, we consider the minimization of the worst-case position estimation error to improve the robustness of the algorithm. The localization problem is formulated as a nonconvex optimization problem. We adopt semidefinite relaxation to relax the original problem into a convex optimization problem, which can be solved using existing semidefinite program solvers. Simulation results show that our proposed algorithm has lower worst-case position estimation error than other existing algorithms.

In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

In this paper, we analytically derive an upper bound on the error in approximating the uplink (UL) single-cell interference by a lognormal distribution in frequency division multiple access (FDMA) small cell networks (SCNs). Such an upper bound is measured by the Kolmogorov-Smirnov (KS) distance between the actual cumulative density function (CDF) and the approximate CDF. The lognormal approximation is important because it allows tractable network performance analysis. Our results are more general than the existing works in the sense that we do not pose any requirement on 1) the shape and/or size of cell coverage areas; 2) the uniformity of user equipment (UE) distribution; and 3) the type of multipath fading. Based on our results, we propose a new framework to directly and analytically investigate a complex network with practical deployment of multiple BSs placed at irregular locations, using a power lognormal approximation of the aggregate UL interference. The proposed interference analysis is particularly useful for the 5th generation (5G) systems with more general cell deployment and UE distribution beyond the widely used Poisson distribution.

In this paper, we introduce a sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their impact on the performance of dense small cell networks (SCNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE), assuming both a general path loss model and a special case with a linear LoS probability function. The performance impact of LoS and NLoS transmissions in dense SCNs in terms of the coverage probability and the ASE is significant, both quantitatively and qualitatively, compared with the previous work that does not differentiate LoS and NLoS transmissions. Our analysis demonstrates that the network coverage probability first increases with the increase of the base station (BS) density, and then decreases as the SCN becomes denser. This decrease further makes the ASE suffer from a slow growth or even a decrease with network densification. The ASE will grow almost linearly as the BS density goes ultra dense. For practical regime of the BS density, the performance results derived from our analysis are distinctively different from previous results, and thus shed new insights on the design and deployment of future dense SCNs.

In this paper, for the first time, we analytically prove that the uplink (UL) inter-cell interference in frequency division multiple access (FDMA) small cell networks (SCNs) can be well approximated by a lognormal distribution under a certain condition. The lognormal approximation is vital because it allows tractable network performance analysis with closed-form expressions. The derived condition, under which the lognormal approximation applies, does not pose particular requirements on the shapes/sizes of user equipment (UE) distribution areas as in previous works. Instead, our results show that if a path loss related random variable (RV) associated with the UE distribution area, has a low ratio of the 3rd absolute moment to the variance, the lognormal approximation will hold. Analytical and simulation results show that the derived condition can be readily satisfied in future dense/ultra-dense SCNs, indicating that our conclusions are very useful for network performance analysis of the 5th generation (5G) systems with more general cell deployment beyond the widely used Poisson deployment.

In this paper, we introduce a sophisticated path loss model into the stochastic geometry analysis incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions to study their performance impact in small cell networks (SCNs). Analytical results are obtained on the coverage probability and the area spectral efficiency (ASE) assuming both a general path loss model and a special case of path loss model recommended by the 3rd Generation Partnership Project (3GPP) standards. The performance impact of LoS and NLoS transmissions in SCNs in terms of the coverage probability and the ASE is shown to be significant both quantitatively and qualitatively, compared with previous work that does not differentiate LoS and NLoS transmissions. From the investigated set of parameters, our analysis demonstrates that when the density of small cells is larger than a threshold, the network coverage probability will decrease as small cells become denser, which in turn makes the ASE suffer from a slow growth or even a notable decrease. For practical regime of small cell density, the performance results derived from our analysis are distinctively different from previous results, and shed new insights on the design and deployment of future dense/ultra-dense SCNs. It is of significant interest to further study the generality of our conclusion in other network models and with other parameter sets.

This paper investigates cooperative forwarding in large highly dynamic vehicular networks. Unlike traditional coordinated cooperative forwarding schemes that require a large amount of coordination information to be exchanged before making the forwarding decision, this paper proposes an uncoordinated cooperative forwarding scheme where each node, a random transmission range, decides whether or not to forward a received packet independently based on a forwarding probability determined by its own location. Analytical results are derived on the successful end-to-end transmission probability and the expected number of forwarding nodes involved in the cooperative forwarding process. The multi-hop correlations and multi-path correlations, which constitute major challenges in the analysis, are carefully considered in our analysis. Simulations are conducted to establish the performance of the proposed scheme assuming different forwarding probability functions. In addition to developing an uncoordinated cooperative forwarding scheme, which is particularly suited for the highly dynamic vehicular networks, this paper also makes important theoretical contributions on analyzing the connectivity of networks with nodes of variable and random transmission ranges.

Cell association scheme determines which base station (BS) and mobile user (MU) should be associated with and also plays a significant role in determining the average data rate a MU can achieve in heterogeneous networks. However, the explosion of digital devices and the scarcity of spectra collectively force us to carefully re-design cell association scheme which was kind of taken for granted before. To address this, we develop a new cell association scheme in heterogeneous networks based on joint consideration of the signal-to-interference-plus-noise ratio (SINR) which a MU experiences and the traffic load of candidate BSs1. MUs and BSs in each tier are modeled as several independent Poisson point processes (PPPs) and all channels experience independently and identically distributed (i.i.d.) Rayleigh fading. Data rate ratio and traffic load ratio distributions are derived to obtain the tier association probability and the average ergodic MU data rate. Through numerical results, We find that our proposed cell association scheme outperforms cell range expansion (CRE) association scheme. Moreover, results indicate that allocating small sized and high-density BSs will improve spectral efficiency if using our proposed cell association scheme in heterogeneous networks.

In this paper, we investigate the performance of a wireless sensor network, in which multiple groups of source nodes communicate with their respective destination nodes with the help of a common relay network. A network code division multiplexing (NCDM) scheme is proposed to remove the inter-session interference among multiple transmission sessions at each destination. We focus on analyzing the soft processing algorithm of the NCDM scheme. Based on the analysis results, a new code design criteria for the construction of the generator matrix is proposed. Simulation results show that by following the proposed code design criteria, the bit error ratio (BER) performance gap between the scheme we studied and the serial session scheme can be managed effectively. In serial session scheme, source nodes in a number of groups communicate with their respective destinations in a time division manner.

In this paper, we propose a novel network coded non-binary low-density generator matrix (LDGM) code structure for a multi-access relay system, where multiple sources transmit lattice signals to a destination with the help of a relay. Specifically, we first develop a network coded non-binary LDGM code structure by jointly considering lattice-signal transmissions at the sources and the relay. Then we derive the achievable computation rate (ACR) for the proposed system and on that basis optimize the key parameters in the proposed structure to maximize the ACR. Furthermore, we optimize the network coded non-binary LDGM codes based on lattices to approach the ACR. Simulation results show that the optimal setting of the parameters is consistent with that obtained from our analysis and the proposed code structure outperforms the designed reference scheme.

It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To evaluate the user access to the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks.

Despite intensive research in the area of network connectivity, there is an important category of problems that remain unsolved: how to characterize and measure the quality of connectivity of a wireless network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and which has unreliable connections, reflecting the inherent unreliability of wireless communications? The quality of connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of capacity to measure the quality of a network in saturated traffic scenarios and provides an intuitive measure of the quality of (end-to-end) network connections. In this paper, we introduce a probabilistic connectivity matrix as a tool to measure the quality of network connectivity. Some interesting properties of the probabilistic connectivity matrix and their connections to the quality of connectivity are demonstrated. We demonstrate that the largest magnitude eigenvalue of the probabilistic connectivity matrix, which is positive, can serve as a good measure of the quality of network connectivity. We provide a flooding algorithm whereby the nodes repeatedly flood the network with packets, and by measuring just the number of packets a given node receives, the node is able to asymptotically estimate this largest eigenvalue.

Wireless broadcast has been increasingly used to deliver information of common interest to a large number of users. There are two major challenges in wireless broadcast: the unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large. In this paper, by resorting to stochastic geometry analysis, we develop a network coding based broadcast scheme that allows a base station (BS) to broadcast a given number of packets to a large number of users, without user acknowledgment, while being able to provide a performance guarantee on the probability of successful delivery. Further, the BS only has limited statistical information about the environment including the spatial distribution of users (instead of their exact locations and number) and the wireless propagation model. Performance analysis is conducted. On that basis, an upper and a lower bound on the number of packet transmissions required to meet the performance guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper are useful for designing efficient and reliable wireless broadcast strategies.

In this paper, we investigate the performance of a wireless relay network with multiple transmission sessions, in which multiple groups of source nodes communicate with their respective destination nodes via a shared wireless relay network. A multiple transmission session model with network code division multiplexing (NCDM) scheme is proposed to remove the inter-session interference at each destination. The fundamental idea of the NCDM scheme takes advantage of the property of G Θ HT= 0 of the low-density generator matrix (LDGM) codes. Based on the analysis of the NCDM scheme, we investigate the relationship among the equivalent received signal vector, the number of sessions and the column weight of the generator matrix. New code design criteria for the construction of the generator matrix is proposed. We further evaluate the multiple transmission session model with the proposed NCDM scheme in terms of throughput and complexity. Our evaluation demonstrates that the proposed scheme not only has a linear computational complexity, but also shows a similar error performance in the AWGN case and a considerable throughput improvement compared with its counterpart, which is referred to as a serial session scheme, where groups of source nodes communicate with their respective destinations in a time division manner.

Wireless communication in a network of mobile devices is a challenging and resource-demanding task, due to the highly dynamic network topology and the wireless channel randomness. This paper investigates information broadcast schemes in 2-D mobile ad hoc networks where nodes are initially randomly distributed and then move following a random direction mobility model. Based on an in-depth analysis of the popular susceptible-infectious-recovered epidemic broadcast scheme, this paper proposes a novel energy and bandwidth-efficient broadcast scheme, named the energy-efficient broadcast scheme, which is able to adapt to fast-changing network topology and channel randomness. Analytical results are provided to characterize the performance of the proposed scheme, including the fraction of nodes that can receive the information and the delay of the information dissemination process. The accuracy of analytical results is verified using simulations driven by both the random direction mobility model and a real-world trace.

Compared with the conventional hexagonal cellular network structure, Poisson-Voronoi tessellated (PVT) random cellular network models can better capture the topology of real cellular networks. However, the random cellular network models are often complicated to analyze. To overcome this gap, in this paper we propose to analyze the performance of PVT random cellular networks using Markov chains. Using this technique, the blocking probability and the area spectral efficiency (ASE) models are obtained. Numerical results are demonstrated which show that our proposed techniques are effective approaches to evaluate the performance of random cellular networks.

Greedy Forwarding algorithm is a widely-used routing algorithm for wireless networks. However, it can fail if the wireless network topologies contain voids, where a packet cannot be moved closer to destination. Since Yao graph and Theta graph are two types of geometric graphs exploited to construct wireless network topologies, this paper firstly studied whether these two types of graphs can contain voids, showing that when the number of cones in a Yao graph or Theta graph is less than six, Yao graph and Theta graph can have voids, and when the number of cones equals or exceeds six, Yao graph and Theta graph are free of voids. Secondly, this paper experimented on how well Greedy Forwarding is supported on Yao graphs and Theta graphs in terms oí stretch, i.e., the ratio between the path length found by Greedy Forwarding and the shortest path length in a graph. The experiments also included comparison with the stretch on Delaunay triangulation, another well-known geometric graph exploited in constructing wireless networks. Overall, our experiments revealed several interesting results.

Connectivity is one of the most fundamental properties of wireless multi-hop networks. This paper studies the connectivity of large wireless networks with secrecy constraint, i.e. a pair of nodes can communicate securely against eavesdropping. Specifically, we consider a network with a mixture of legitimate nodes and eavesdroppers that are distributed according to two independent Poisson point processes on a √n × √n square. Assuming that legitimate nodes can generate artificial noise, which is shown in the literature to be an effective way of suppressing eavesdropping, we provide sufficient conditions on the transmission power and the noise generation power required for a network with known intensity of eavesdroppers to be asymptotically almost surely connected with secrecy constraint as n → ∞, considering the cases of both non-colluding and colluding eavesdroppers.

There is an increasing demand for broadcasting information of common interest to a large number of users. The unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large are two major challenges for wireless network broadcasting. In this paper we investigate the problem that a base station broadcasts a given number of packets to a given number of users, without user acknowledgment, while being able to provide a guarantee on the probability of successful delivery. Network coding technique is employed to improve both the efficiency and the reliability of the broadcast. Performance analysis is conducted. Based on the analysis, an upper and a lower bound on the number of packet transmissions required to meet the reliability guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper can be useful for designing strategies to deliver information of common interest to a large number of users efficiently and reliably.

Heterogeneous network (HetNet) [1] is considered as an energy efficient system structure to alleviate the problem of rapidly increasing power consumption in the wireless communication system. Significant research on HetNet energy efficiency has been conducted. However, most of them only consider power consumption of Base Stations (BSs) while ignoring influence on energy efficiency of Mobile Devices (MDs) brought by new BSs deployment. In this work, we propose a novel power saving metric for HetNet. Under the coexisting scenario of a single macrocell and a single microcell, we analyze the changes in power consumption at both the BSs side and the MDs side with the deployment of a micro BS. Optimum microcell radii for maximum power saving at the MDs sides and for highest network energy efficiency are obtained through analytical studies. It is found that total power saving for microcell MDs is close to 18% with a proper deployment of a microcell. Finally, extensive simulations have been provided to establish the accuracy of our theoretical analyses.

This paper investigates the information dissemination process in wireless communication networks formed by vehicles. As vehicles are moving constantly, a vehicular ad-hoc network exhibits a highly-dynamic network topology and a fast-changing radio environment. These distinguishing characteristics result in random and unreliable wireless connections between vehicles. Consequently, vehicles need to work cooperatively to disseminate a piece of information to the destination. This paper analyses cooperative information forwarding schemes where each vehicle determines whether or not to forward a received packet in a decentralized manner, without the costly or even impractical demand for the knowledge of network topology. Considering a generic wireless connection model incorporating wireless channel randomness, analytical results are derived for the probability of successful delivery and the expected number of packet forwardings. Moreover, analysis is conducted on the optimal information forwarding scheme that meets a pre-designated probability of successful delivery objective using the minimum number of packet forwardings.

In this paper, we study the use of multiple mobile charging vehicles to charge sensors in a large-scale wireless sensor network for a given monitoring period, where sensors can be charged by the vehicles with wireless power transfer. Since each sensor may experience multiple charges to avoid its energy expiration for the period, we first consider a charging problem of scheduling the multiple mobile vehicles to collaboratively charge sensors so that none of the sensors will run out of its energy and the sum of traveling distance (referred to as the service cost) of these vehicles can be minimized. Due to NP-hardness of the problem, we then propose a novel approximation algorithm for it, assuming that sensor energy consumption rates do not change over time. Otherwise, we devise a heuristic algorithm through minor modifications to the approximation algorithm. We finally evaluate the performance of the proposed algorithms via simulations. Experimental results show that the proposed algorithms are very promising, which can reduce upto 45% of the service cost in comparison with the service cost delivered by a greedy algorithm.

In heterogeneous networks (HetNets), the load between macro-cell base stations (MBSs) and small-cell BSs (SBSs) is imbalanced due to transmit power disparities and ad-hoc deployment of SBSs. This significantly impacts the system performance and user experience. Associating more users to the SBSs is an effective way to solve this problem. In this paper, we formulate the user-BS association problem as a distributed optimization problem with proportional fairness as the objective. Specifically, we propose a novel distribute algorithm based on the belief propagation (BP) method to solve the user-BS association problem via iteratively message passing between the users and BSs. Also, we develop an approximation calculation in the BP method to reduce the computational complexity and transmission overhead of message passing. Simulation results show that the proposed algorithm well approaches the optimal system performance (by exhausting search) with low complexity and fast convergence.

In this paper, we propose a distributed network coding (DNC) scheme based on the Raptor codes for wireless relay networks (WRNs), where a group of source nodes communicate with a single sink through a common relay network in a multi-hop fashion. At the sink, a graph-based Raptor code is formed on the fly. After receiving a sufficient number of encoded packets, the sink begins to decode. The main contributions of this paper are the derivations of upper and lower bit error rate (BER) bounds for the proposed Raptor-based DNC scheme.

This paper investigates the energy efficiency of base station sleep scheduling strategies in 1-D infrastructure-based wireless communication networks formed by vehicles traveling on a highway. In certain scenarios, vehicular speeds and locations can be measured with a high degree of accuracy, and this information can be exploited to reduce the energy consumption of base stations. This paper considers cooperative base station scheduling strategies where base stations can switch between sleep and active modes to reduce the average energy consumption, while guaranteeing the connectivity of every vehicle. Analytical results on the expected amount of energy saving for a base stations are derived, which reveals for the first time the existence of a threshold parameter, determined by both the vehicular density and mobility, below which base stations can switch off and save energy, but above which no energy saving can be achieved.

Wireless broadcast has been widely utilized to deliver information of common interest to a large number of users. A major challenge for wireless broadcast is that wireless links are often unreliable. Further, it is not feasible for every receiver to acknowledge the correct reception of broadcasted packets. In this paper we investigate the use of wireless broadcast to deliver a given number of packets by a common transmitter to a given number of receivers, without feedback from the receivers, while meeting the reliability constraint, i.e. the probability that all receivers successfully receive all broadcasted packets is above a certain threshold. Rateless codes(RCs) technology is used to assist the broadcast. Performance analysis with the use of RCs is conducted. Simulations are conducted to validate the accuracy of the theoretical analysis. It is shown that the use of RCs can significantly reduce the number of transmissions required to meet the reliability constraint.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs transmitting on the same channel will be affected when the channel is requested by the PUs, thereby resulting in a possible network partition of CRNs. Therefore, how to maintain the connectivity of CRNs considering the activity of PUs is a critical problem. In this paper, we propose a centralized and a distributed topology control algorithm respectively to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using the minimum number of channels. In the power control phase, we tailor the topology for the channel assignment in the second phase. In the channel assignment phase, we utilize the graph coloring algorithm to achieve conflict-free transmission by assigning a channel to each SU. Theoretical analysis and simulation study show that the derived topology can maintain connectivity in the event of any single channel interruption by PUs. Simulation results also demonstrate that the proposed algorithms can efficiently reduce the average number of required channels for achieving bi-channel-connectivity and conflict-free transmission and ensure that the minimum power paths in the original network preserved in the final topology.

This paper studies the information propagation process in wireless communication networks formed by vehicles traveling on a highway. Corresponding to different lanes of the highway and different types of vehicles, we consider that vehicles in the network can be categorized into a number of traffic streams, where the vehicles in the same traffic stream have the same speed distribution while the speed distributions of vehicles in different traffic streams are different. We analyze the information propagation process of the aforementioned vehicular network and obtain an analytical formula for the information propagation speed (IPS). Using the formula, one can straightforwardly study the impact of parameters such as radio range, vehicular traffic density, vehicular speed distribution, and the time variation of vehicular speed on the IPS. The accuracy of the analytical results is validated using simulations.

This paper investigates the capacity of a random network in which the nodes have a general spatial distribution. Our model assumes n nodes in a unit square, with a pair of nodes directly connected if and only if their Euclidean distance is smaller than or equal to a threshold, known as the transmission range. Each link has an identical capacity of W bits/s. The transmission range is the same for all nodes and can be any value so long as the resulting network is connected. A capacity upper bound is obtained for the above network, which is valid for both finite n and asymptotically infinite n. We further investigate the capacity upper bound and lower bound for the above network as n → ∞ and show that both bounds can be expressed as a product of four factors, which represents respectively the impact of node distribution, link capacity, number of source destination pairs and the transmission range. The bounds are tight in that the upper bound and lower bound differ by a constant multiplicative factor only. For the special case of networks with nodes distributed uniformly or following a homogeneous Poisson distribution, the bounds are of the same order as known results in the literature.

In this paper, we study the transport capacity of large multi-hop wireless CSMA networks. Different from previous studies that rely on the use of a centralized scheduling algorithm and/or a centralized routing algorithm to achieve the optimal capacity scaling law, we show that the optimal capacity scaling law can be achieved using entirely distributed routing and scheduling algorithms. Specifically, we consider a network with nodes Poissonly distributed with unit intensity on a$\sqrt{n}\times\sqrt{n}$square$B_{n}\subset\Re^{2}$. Furthermore, each node chooses its destination randomly and independently and transmits following a CSMA protocol. By resorting to the percolation theory and by carefully tuning the three controllable parameters in CSMA protocols, i.e., transmission power, carrier-sensing threshold, and countdown timer, we show that a throughput of$\Theta(1/\sqrt{n})$is achievable in distributed CSMA networks. Furthermore, we derive the pre-constant preceding the order of the transport capacity by giving an upper and a lower bound of the transport capacity. The tightness of the bounds is validated using simulations.

Spectrum sharing between cellular and ad-hoc networks is studied in this work. Weak signals and strong interferences at the cell-edge area usually cause severe performance degradation. To improve the cell-edge users' performance quality while keeping high spectrum efficiency, in this paper, we propose a cooperative spectrum sharing scheme. In the proposed scheme, the ad-hoc users can actively employ cooperative diversity techniques to improve the cellular network downlink throughput. As a reward, a fraction of the cellular network spectrum is released to the ad-hoc network for its own data transmission. To determine the optimal spectrum allocation, we maximize the ad-hoc transmission capacity subject to the constraints on the outage probability of the ad-hoc network and on the throughput improvement ratio of the cellular network. Both the transmission capacity of the ad-hoc network and the average throughput of the cellular network are analyzed using the stochastic geometry theory. Numerical and simulation results are provided to validate our analytical results. They demonstrate that our proposed scheme can effectively facilitate ad-hoc transmissions while moderately improving the cellular network performance.

ABSTRACT Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum‐likelihood estimator to estimate distances between a pair of neighboring nodes in a static wireless sensor network using their local connectivity information, namely the numbers of their common and non‐common one‐hop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log‐normal (shadowing) model as special cases. Under the log‐normal model, we investigate the impact of the log‐normal model uncertainty; we numerically evaluate the bias and standard deviation associated with our method, which show that for long distances our method outperforms the method based on received signal strength; and we provide a Cramér–Rao lower bound analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on implementing the proposed method on the basis of measurement data from a realistic environment and applying it in connectivity‐based sensor localization, the advantages of the proposed method are confirmed. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless communication is notoriously lossy due to channel fading, interference and multi-path effects. This work investigates the reliability of all-to-all broadcast in lossy wireless networks where the reliability is measured by the probability that every node in the network receives or decodes the native packet of every other node. To improve the reliability, a novel network coding scheme, namely random neighbour network coding (RNNC) scheme is proposed, which is capable of adaptively generating encoding packets according to the packets received from lossy wireless channels. The network reliability is analysed theoretically and the optimal RNNC scheme that maximises the reliability of a given network is obtained. The theoretical analysis is validated using simulations and it is shown that RNNC can improve the network reliability significantly.

Since the seminal work of Gupta and Kumar, extensive research has been done on studying the capacity of large wireless networks under various scenarios. Most of the existing work focuses on studying the capacity of networks with uniformly or Poissonly distributed nodes. While uniform and Poisson distribution form an important class of spatial distributions, their capability in capturing the spatial distribution of users in various scenarios and application settings is limited. Therefore it is critical to investigate to what extent, the aforementioned results on capacity of networks with uniformly or Poissonly distributed nodes depend on the underlying node distribution being uniform or Poisson. In this paper, we study the capacity of networks under a general node distribution. A capacity upper bound on networks with generally distributed nodes is obtained, which is valid for both finite networks and asymptotically infinite networks. By imposing some mild conditions on the transmission range, we further simplify the result and show that the asymptotic capacity upper bound can be expressed as a product of four factors, which represents respectively the impact of node distribution, link capacity, number of source destination pairs and the transmission range. The upper bound is shown to be tight in the sense that for the special case of networks with uniformly distributed nodes, the bound is in the same order as known results in the literature.

In this paper, we propose a cooperative spectrum sharing scheme between cellular network downlink and mobile ad-hoc network based on the analysis using stochastic geometry theory. The licensed spectrum belongs to the cellular network and the strong interference at cell-edge becomes a bottleneck to guarantee the quality of service requirement. In this case, the secondary ad-hoc users can assist the transmission between the base station and cell-edge mobile users in exchange for spectrum usage. Through maximizing the transmission capacity of secondary system under the constraint of throughput improvement of primary system, an optimal spectrum allocation can be obtained. Numerical and simulation results are provided to validate the analysis and verify the efficiency of the proposed scheme.

Broadcast in mobile ad-hoc networks is a challenging and resource demanding task, due to the effects of dynamic network topology and channel randomness. In this paper, we consider 2D wireless ad-hoc networks where nodes are randomly distributed and move following a random direction mobility model. A piece of information is broadcast from an arbitrary node. Based on an in-depth analysis into the popular Susceptible-Infectious-Recovered (SIR) epidemic routing algorithm for mobile ad-hoc networks, an energy and spectrum efficient broadcast scheme is proposed, which is able to adapt to fast-changing network topology and channel randomness. Analytical results are provided to characterize the performance of the proposed scheme, including the fraction of nodes that can receive the information and the delay of information propagation. The accuracy of analytical results is verified using simulations.

Cellular relay networks prove to be a cost-effective approach that offers significant performance benefits in coverage extension, cell-edge throughput enhancement and increased spectral efficiency. Radio resource management for cellular relay networks, where the network topology is highly dynamic, is particularly challenging. The highly dynamic topology may be caused by an ever increasing number of users accessing Internet and other multimedia services using mobile devices carried by pedestrians, in a train or in vehicles. This paper studies the subcarrier allocation problem in highly dynamic cellular relay networks with an objective to maximize the overall throughput. This optimization problem is formulated and solved as an expectation maximization problem. Statistical multiplexing gain is explicitly explored to further improve the channel utilization and spectral efficiency. Numerical evaluations are performed which demonstrate that the proposed scheme offers higher system throughput and improved radio resource utilization compared with existing schemes.

Road traffic density estimation provides important information for road planning, intelligent road routing, road traffic control, vehicular network traffic scheduling, routing and dissemination. The ever increasing number of vehicles equipped with wireless communication capabilities provide new means to estimate the road traffic density more accurately and in real time than traditionally used techniques. In this paper, we consider the problem of road traffic density estimation where each vehicle estimates its local road traffic density using some simple measurements only, i.e. the number of neighboring vehicles. A maximum likelihood estimator of the traffic density is obtained based on a rigorous analysis of the joint distribution of the number of vehicles in each hop. Analysis is also performed on the accuracy of the estimation and the amount of neighborhood information required for an accurate road traffic density estimation. Simulations are performed which validate the accuracy and the robustness of the proposed density estimation algorithm.

Trilateration-based localization techniques have been widely used in sensor networks due to their computational efficiency and distributedness. However in sparse networks or in the boundary area of networks, trilateration-based techniques often fail to localize all localizable nodes. Bilateration-based techniques emerge as a generalization of trilateration techniques to a broader class of networks. Compared with trilaterationbased techniques, the main benefit of bilateration-based schemes is that they can localize a higher percentage of nodes while still maintaining the low computational complexity and distributedness properties. One potential drawback of bilaterationbased schemes is that the number of estimated possible positions (hence the memory required to store these positions) may grow exponentially with the number of nodes in the network. Despite the empirical observations reported in the literature that such exponential growth is a rare event, there is a lack of rigorous analysis quantifying the complexity of bilaterationbased schemes. In this paper, we tackle the challenge by first characterizing a broad subclass of the set of critical sub-networks within which the number of possible estimated positions grows exponentially with the size of these sub-networks. Then using mathematical techniques from percolation theory, we prove that, in random geometric networks, with very high probability the size of these critical sub-networks, which constitute the worst case for bilateration-based localization, is bounded. Therefore the complexity of bilateration-based localization technique does not grow exponentially with the size of the entire network. The significance of this result is to analytically demonstrate that bilateration-based techniques not only localize a higher fraction of nodes than their trilateration counterpart, but also they can be implemented in a very efficient (low computational cost) manner.

Cooperative communication technologies can improve the system throughput energy efficiency and reliability in dynamic wireless networks. For practical multi-cell multi-antenna mobile cellular networks, co-channel interference is a critical issue affecting cooperative transmission (Co-Tx) performance. In this paper, we first derive a cooperative outage probability model and a cooperative block error rate (BLER) model incorporating a binary differential phase shift keying modulation for performance analysis in such cooperative cellular networks. Based on them, a cooperative energy efficiency model is proposed and analyzed under different Co-Tx scenarios, interference levels and wireless channel conditions. As demonstrated by numerical results, our analytical models show that Co-Tx is an effective approach to mitigate co-channel interference and improve the energy efficiency, BLER and overall outage probability performance in multi-cell multi-antenna cooperative cellular networks.

In this paper, we propose a distributed network coding (DNC) scheme based on the Raptor codes for wireless sensor networks (WSNs), where a group of sensor nodes, acting as source nodes, communicate with a single sink through some other sensor nodes, serving as relay nodes, in a multi-hop fashion. At the sink, a graph-based Raptor code is formed on the fly. After receiving a sufficient number of encoded packets, the sink begins to decode. The main contributions of this paper are the derivation of a bit error rate (BER) lower bound for the LT-based DNC scheme over Rayleigh fading channels under maximum-likelihood (ML) decoding, and the derivations of upper and lower BER bounds for the proposed Raptor-based DNC scheme on the basis of the derived BER bound of LT codes.

This paper studies networks where all nodes are distributed on a unit squareA=Δ[- [1/2], [1/2]]2following a Poisson distribution with known density ρ and a pair of nodes separated by an Euclidean distancexare directly connected with probabilitygrρ(x)=Δg(x/rρ), independent of the event that any other pair of nodes are directly connected. Here,g:[0,∞)→ [0,1] satisfies the conditions of rotational invariance, nonincreasing monotonicity, integral boundedness, andg(x)=o(1/(x2log2x)) ; further,rρ=√{(logρ+b)/(Cρ)} whereC=∫ℜ2g(||x||)dxandbis a constant. Denote the aforementioned network byG(Xρ,grρ,A). We show that as ρ→ ∞, 1) the distribution of the number of isolated nodes inG(Xρ,grρ,A) converges to a Poisson distribution with meane-b; 2) asymptotically almost surely (a.a.s.) there is no component inG(Xρ,grρ,A) of fixed and finite orderk>; 1; c) a.a.s. the number of components with an unbounded order is one. Therefore, as ρ→ ∞, the network a.a.s. contains a unique unbounded component and isolated nodes only; a sufficient and necessary condition forG(Xρ,grρ,A) to be a.a.s. connected is that there is no isolated node in the network, which occurs whenb→ ∞ as ρ→ ∞. These results expand recent results obtained for connectivity of random geometric graphs from the unit disk model and the fewer results from the log-normal model to the more general and more practical random connection model.

Extensive research has been done on studying the capacity of wireless multi-hop networks. These efforts have led to many sophisticated and customized analytical studies on the capacity of particular networks. While most of the analyses are intellectually challenging, they lack universal properties that can be extended to study the capacity of a different network. In this paper, we sift through various capacity-impacting parameters and present a simple relationship that can be used to estimate the capacity of both static and mobile networks. Specifically, we show that the network capacity is determined by the average number of simultaneous transmissions, the link capacity and the average number of transmissions required to deliver a packet to its destination. Our result is valid for both finite networks and asymptotically infinite networks. We then use this result to explain and better understand the insights of some existing results on the capacity of static networks, mobile networks and hybrid networks and the multicast capacity. The capacity analysis using the aforementioned relationship often becomes simpler. The relationship can be used as a powerful tool to estimate the capacity of different networks. Our work makes important contributions towards developing a generic methodology for network capacity analysis that is applicable to a variety of different scenarios.

In this paper we investigate the critical node density required to ensure that an arbitrary node in a large-scale wireless multi-hop network is connected (via multi-hop path) to infinitely many other nodes with a positive probability. Specifically we consider a wireless multi-hop network where nodes are distributed in ℝ2(d = 2, 3) following a homogeneous Poisson point process. The establishment of a direct connection between any two nodes is independent of connections between other pairs of nodes and its probability satisfies some intuitively reasonable conditions, viz. rotational and translational invariance, nonincreasing monotonicity, and integral boundedness. Under the above random connection model we first obtain analytically the upper and lower bounds for the critical density. Then we compare the new bounds with other existing bounds in the literature under the unit disk model and the log-normal model which are special cases of the random connection model. The comparison shows that our bounds are either close to or tighter than the known ones. To the best of our knowledge, this is the first result for the random connection model in both 2D and 3D networks. The result is of practical use for designing large-scale wireless multihop networks such as wireless sensor networks.

Despite intensive research in the area of network connectivity, there is an important category of problems that remain unsolved: how to measure the quality of connectivity of a wireless multi-hop network which has a realistic number of nodes, not necessarily large enough to warrant the use of asymptotic analysis, and has unreliable connections, reflecting the inherent unreliable characteristics of wireless communications? The quality of connectivity measures how easily and reliably a packet sent by a node can reach another node. It complements the use of capacity to measure the quality of a network in saturated traffic scenarios and provides a native measure of the quality of (end-to-end) network connections. In this paper, we explore the use of probabilistic connectivity matrix as a tool to measure the quality of network connectivity. Some interesting properties of the probabilistic connectivity matrix and their connections to the quality of connectivity are demonstrated. We show that the largest eigenvalue of the probabilistic connectivity matrix can serve as a good measure of the quality of network connectivity.

Outage probability and transmission capacity are two metrics that are often used together to quantify the achievable capacity of decentralized wireless networks, where the outage probability measures the probability that a direct transmission fails and the transmission capacity measures the maximum spatial density of successful concurrent transmissions, subject to a constraint on the outage probability. In CSMA networks, spatial correlations between concurrent transmitters makes the analysis of the outage probability and the transmission capacity a challenging task. In this paper, we analyze the transmission capacity of CSMA networks subject to a designated outage probability constraint by first deriving an upper bound on the outage probability in CSMA networks subject to Rayleigh fading, which is applicable for any node distribution. On that basis, we provide a sufficient condition on the transmission power required to meet a designated outage probability constraint. Finally, we obtain an upper bound on the transmission capacity in CSMA networks satisfying a pre-determined outage probability constraint.

Greedy forwarding is an efficient and scalable geographic routing algorithm for wireless networks. To guarantee the success of greedy forwarding, many research efforts assign virtual coordinates to nodes to obtain a greedy embedding of the network. Different from these existing efforts, this paper presents an approach that enables greedy forwarding to succeed in directional wireless networks by selecting links in the network instead of assigning virtual coordinates to the nodes. Specifically, this paper studies the following problem: given a set of nodes on the Euclidean plane, how can we add a minimum number of point-to-point links, such that the greedy forwarding algorithm succeeds on the resulting network. The motivation for studying this problem is that each point-to-point link in directional wireless networks is realized by a pair of directional antennas, so minimizing the number of links will reduce the network installation cost. This paper first presents the properties of the graphs supporting greedy forwarding, and then solves the above problem optimally by Integer Linear Programming and also sub-optimally by a polynomial-time 3-approximation algorithm. Finally, this paper compares the polynomial-time algorithm with the optimal solution, showing that the polynomial-time algorithm can actually generate within 1.1 times the number of links found by the optimal solution in most cases.

In this paper, we study the throughput of interference-limited three dimensional (3D) CSMA networks. Specifically, we consider a network with a total of n nodes uniformly i.i.d. in a cube of edge length n1/3. Further, CSMA random access scheme is employed and the SINR model is used to simulate a successful transmission. We first give a sufficient condition on the transmit power required for the CSMA network to be asymptotically almost surely (a.a.s.) connected as n → ∞ under the SINR model. Then, we demonstrate constructively that a throughput of Θ(1/(n log2n)1/3) is obtainable by each node for an arbitrarily chosen destination.

In this paper, we propose two uncoordinated cooperative truncated ARQ schemes based on node position or local SNR information. If the original transmission between a source and a destination fails, the source and all the potential relays that have correctly received the data packet will contend for the channel to retransmit the packet without coordination. The competition for the channel access is governed by the retransmission probability which is computed in a distributed fashion using the stochastic geometry theory. Theoretical system success probabilities of both schemes are derived. Compared with the scheme in which only the source retransmits, the uncoordinated schemes are shown to achieve better performance, particularly when the distance between the source and the destination is large.

This paper studies the information propagation process in a 1D mobile ad-hoc network formed by vehicles traveling on a highway. We consider that vehicles can be divided into traffic streams; vehicles in the same traffic stream have the same speed distribution, while the speed distributions of vehicles in different traffic streams are different. Analytical formulas are derived for the fundamental properties of the information propagation process as well as the information propagation speed. Using the formulas, one can straightforwardly study the impact on the information propagation speed of various parameters such as radio range, vehicular traffic density, vehicular speed distribution and the time variation of vehicular speed.

Connectivity is one of the most fundamental properties of wireless multi-hop networks. In a wireless network with many concurrent transmissions, signals transmitted at the same time may mutually interfere with each other. In this paper we consider the impact of interference on the connectivity of CSMA networks using the SINR model. On the basis of our earlier work in which we give a sufficient condition, i.e. an upper bound, on the critical transmission power required for a CSMA network with a total of n nodes i.i.d. on a √n × √n square following a uniform distribution to be a.a.s. connected as n → ∞ under the SINR model, in this paper we continue to study the necessary condition for the above CSMA network to be a.a.s. connected. A lower bound is obtained on the critical transmission power required for the above CSMA network to be a.a.s. connected under any scheduling scheme satisfying the carrier-sensing constraint. The lower bound differs from the upper bound by a constant factor only. Compared with previous literature assuming a unit disk model, it is shown that the critical transmission power for a CSMA network under the SINR model to be a.a.s. connected is within a constant factor of that required for a network under the unit disk model, which does not consider the impact of interference, to be a.a.s. connected. That is, transmission power only needs to be increased by a constant factor to combat interference and maintain connectivity. This result is also in sharp contrast with previous results considering the connectivity of ALOHA networks under the SINR model.

In this paper, we consider 2D wireless multi-hop networks with mobile nodes randomly distributed on a torus, and a small number of base stations (infrastructure nodes) deterministically placed in the same area. Mobile nodes move following a random walk mobility model. A piece of information is broadcast from the base stations at the same time in a multi-hop manner using a Susceptible-Infectious-Recovered (SIR) epidemic routing algorithm. A distinguishing feature of the SIR algorithm, which leverages the mobility of mobile users, is that a relay node carries a piece of information for a predetermined amount of time and forwards it at any available opportunity during that time. We provide analytical results for the percolation probability and for the expected fraction of nodes that receive the information when the information dissemination process stops. Further, we study the time delay of the information dissemination process. The accuracy of the analytical results is verified using simulations.

Cooperative communication techniques offer significant performance benefits over traditional methods that do not exploit the broadcast nature of wireless transmissions. Such techniques generally require advance coordination among the participating nodes to discover available neighbors and negotiate the cooperation strategy. However, the associated discovery and negotiation overheads may negate much of the cooperation benefit in mobile networks with highly dynamic or unstable topologies (e.g. vehicular networks). This paper discusses uncoordinated cooperation strategies, where each node overhearing a packet decides independently whether to retransmit it, without any coordination with the transmitter, intended receiver, or other neighbors in the vicinity. We formulate and solve the problem of finding the optimal uncoordinated retransmission probability at every location as a function of only a priori statistical information about the local environment, namely the node density and radio propagation model. We show that the solution consists of an optimal cooperation region which we provide a constructive method to compute explicitly. Our numerical evaluation demonstrates that uncoordinated cooperation offers a low-overhead viable alternative, especially in high-noise (or low-power) and high node density scenarios.

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

Connectivity and capacity are two fundamental properties of wireless multihop networks. The scalability of these properties has been a primary concern for which asymptotic analysis is a useful tool. Three related but logically distinct network models are often considered in asymptotic analyses, viz. the dense network model, the extended network model, and the infinite network model, which consider respectively a network deployed in a fixed finite area with a sufficiently large node density, a network deployed in a sufficiently large area with a fixed node density, and a network deployed in with a sufficiently large node density. The infinite network model originated from continuum percolation theory and asymptotic results obtained from the infinite network model have often been applied to the dense and extended networks. In this paper, through two case studies related to network connectivity on the expected number of isolated nodes and on the vanishing of components of finite order respectively, we demonstrate some subtle but important differences between the infinite network model and the dense and extended network models. Therefore, extra scrutiny has to be used in order for the results obtained from the infinite network model to be applicable to the dense and extended network models. Asymptotic results are also obtained on the expected number of isolated nodes, the vanishingly small impact of the boundary effect on the number of isolated nodes, and the vanishing of components of finite order in the dense and extended network models using a generic random connection model.

Consider a wireless multihop network where nodes are randomly distributed in a given area following a homogeneous Poisson process. The hop count statistics, viz. the probabilities related to the number of hops between two nodes, are important for performance analysis of the multihop networks. In this paper, we provide analytical results on the probability that two nodes separated by a known euclidean distance are k hops apart in networks subject to both shadowing and small-scale fading. Some interesting results are derived which have generic significance. For example, it is shown that the locations of nodes three or more hops away provide little information in determining the relationship of a node with other nodes in the network. This observation is useful for the design of distributed routing, localization, and network security algorithms. As an illustration of the application of our results, we derive the effective energy consumption per successfully transmitted packet in end-to-end packet transmissions. We show that there exists an optimum transmission range which minimizes the effective energy consumption. The results provide useful guidelines on the design of a randomly deployed network in a more realistic radio environment.

Many wireless multi-hop networks are deployed with some infrastructure support. Existing results on ad-hoc networks are inadequate to fully understand the properties of those networks. In this paper, we study the properties of 1-D infrastructure-based multi-hop networks. Specifically, we consider networks with two types of nodes, i.e. ordinary nodes and powerful nodes. Ordinary nodes are i.i.d. and Poissonly distributed in a unit interval. Powerful nodes are arbitrarily distributed within the same unit interval. These powerful nodes are inter-connected via some backbone infrastructure. The network is said to be connected if each ordinary node is connected (possibly through a multi-hop path) to at least one of the powerful nodes. We obtain analytical results for the connectivity probability and the average number of clusters in the network. We also prove for the first time that the optimum powerful node distribution that minimizes the average number of clusters, and maximizes the asymptotic connectivity probability, is to deploy these powerful nodes in an equi-distant fashion. These results are important for the design and deployment of 1-D infrastructure-based networks.

Wireless multi-hop networks are being increasingly used in military and civilian applications. Connectivity is a prerequisite in wireless multi-hop networks for providing many network functions. In a wireless network with many concurrent transmissions, signals transmitted at the same time will mutually interfere with each other. In this paper we consider the impact of interference on the connectivity of CSMA networks. Specifically, consider a network with n nodes uniformly and i.i.d. on a square [-\frac{\sqrt{n}}{2},\frac{\sqrt{n}}{2}]^{2} where a node can only transmit if the sensed power from any other active transmitter is below a threshold, i.e. subject to the carrier-sensing constraint, and the transmission is successful if and only if the SINR is greater than or equal to a predefined threshold. We provide a sufficient condition and a necessary condition, i.e. an upper bound and a lower bound on the transmission power, required for the above network to be asymptotically almost surely (a.a.s.)connected as n → ∞. The two bounds differ by a constant factor only as n → ∞. It is shown that the transmission power only needs to be increased by a constant factor to combat interference and maintain connectivity compared with that considering a unit disk model (UDM) without interference. This result is also in stark contrast with previous results considering the connectivity of ALOHA networks under the SINR model.

In traditional cooperative communications, coordination is required among relay nodes to help data transmission through distributed signal transmission or coding techniques. For large cooperative networks, the overhead for coordination is huge and the synchronization among relays is very difficult. In this paper, we propose uncoordinated cooperative communication schemes in a large wireless network that do not need the coordination among relays while realizing cooperative diversity for the source-destination link. Without a central controller, all relays that are spatially randomly placed contend for the channel to relay the packet from the source to the destination in a distributed fashion. The competition for the channel access is governed by the retransmission probability that is independently calculated by the relays according to the location or channel quality information. Three schemes of uncoordinated cooperative communications are proposed to determine the retransmission probabilities of the potential relays based on the local distance, direction, and channel quality, referred to as distance based, sectorized, and local SNR based schemes, respectively. Success probabilities for the proposed uncoordinated schemes are analyzed. Numerical and simulation results show that the local SNR based scheme has the best performance and the distance based scheme outperforms the sectorized scheme.

In this paper we develop analytical bounds on the critical density for percolation in wireless multi- hop networks, but in contrast to other studies, under a random connection model and with nodes Poissonly distributed in the plane R2. The establishment of a direct connection between any two nodes follows a random connection model satisfying some intuitively reasonable conditions, i.e. rotational and translational invariance, non- increasing monotonicity and integral boundedness. It is well known that under the above network model and connection model there exists a critical density below which almost surely a fixed but arbitrary node is connected (via single or multi-hop path) to finite number of other nodes only, and above which the node is connected to an infinite number of other nodes with a positive probability. In this paper we investigate the bounds on the critical density. The result is compared with the existing results under a specific connection model, i.e. the unit disk communication model, and it is shown that our method generates bounds close to the known ones. The result provides valuable insight into the design of large- scale wireless multi-hop networks.

In this paper, we study information propagation in a 2D mobile ad-hoc network, where mobile nodes are randomly and independently distributed on a torus following a homogeneous Poisson process with a given density. Nodes in the network move following a random direction mobility model. A piece of information is broadcast from a source node to all other nodes in the network, using a Susceptible-Infectious-Recovered (SIR) epidemic routing protocol. A distinguishing feature of the SIR algorithm, which leverages the mobility of mobile users, is that a relay node carries and forwards a piece of information for a specified amount of time. We first propose a metric fundamentally characterizing the information propagation in mobile ad-hoc networks. Then analytical results are derived for the probability that a non-zero fraction of nodes receive the information in the limit of large network size and for the expected fraction of nodes that receive the information. The analytical results are verified using simulations. The research provides useful insights on the design of mobile ad-hoc networks.

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We consider wireless multi-hop networks with a finite number of (ordinary) nodes randomly deployed in a given 2D area. A finite number of gateways (infrastructure nodes) are deterministically placed in the same area. We study the connectivity between the ordinary nodes and the gateways. In real applications, it is often desirable to limit the maximum number of hops between the ordinary nodes and the gateways in order to provide reliable services. On the other hand, requiring every ordinary node to be connected to at least one gateway imposes strong requirement on transmission range/power or the number of gateways. Therefore it is beneficial to allow a small fraction of ordinary nodes to be disconnected from the gateways so that the network is only partially connected. Based on the above two considerations, we provide analytical results on the k hop partial connectivity, which is the fraction of ordinary nodes that are connected to at least one gateway in at most k hops. The research provides useful guidelines on the design of wireless multi-hop networks.

Consider a network where all nodes are distributed on a unit square following a Poisson distribution with known density ρ and a pair of nodes separated by an Euclidean distance x are directly connected with probability g(x/rρ), where g : [0,∞) → [0,1] satisfies three conditions: rotational invariance, non-increasing monotonicity and integral boundedness, √(log ρ+b)/Cρ, C = ∫ℜ2g (||x||) dx and b is a constant, independent of the event that another pair of nodes are directly connected. In this paper, we analyze the asymptotic distribution of the number of isolated nodes in the above network using the Chen-Stein technique and the impact of the boundary effect on the number of isolated nodes as ρ → ∞. On that basis we derive a necessary condition for the above network to be asymptotically almost surely connected. These results form an important link in expanding recent results on the connectivity of the random geometric graphs from the commonly used unit disk model to the more generic and more practical random connection model.

IEEE 802.11p and 1609 standards are currently under development to support Vehicle-to-Vehicle and Vehicle-to-Infrastructure communications in vehicular networks. For infrastructure-based vehicular relay networks, access probability is an important measure which indicates how well an arbitrary vehicle can access the infrastructure, i.e. a base station (BS). On the other hand, connectivity probability, i.e. the probability that all the vehicles are connected to the infrastructure, indicates the service coverage performance of a vehicular relay network. In this paper, we develop an analytical model with a generic radio channel model to fully characterize the access probability and connectivity probability performance in a vehicular relay network considering both one-hop (direct access) and two-hop (via a relay) communications between a vehicle and the infrastructure. Specifically, we derive close-form equations for calculating these two probabilities. Our analytical results, validated by simulations, reveal the tradeoffs between key system parameters, such as inter-BS distance, vehicle density, transmission ranges of a BS and a vehicle, and their collective impact on access probability and connectivity probability under different communication channel models. These results and new knowledge about vehicular relay networks will enable network designers and operators to effectively improve network planning, deployment and resource management.

Distance estimation is of great importance for localization and a variety of applications in wireless sensor networks. In this paper, we develop a simple and efficient method for estimating distances between any pairs of neighboring nodes in static wireless sensor networks based on their local connectivity information, namely the numbers of their common one-hop neighbors and non-common one-hop neighbors. The proposed method involves two steps: estimating an intermediate parameter through a Maximum-Likelihood Estimator (MLE) and then mapping this estimate to the associated distance estimate. In the first instance, we present the method by assuming that signal transmission satisfies the ideal unit disk model but then we expand it to the more realistic log-normal shadowing model. Finally, simulation results show that localization algorithms using the distance estimates produced by this method can deliver superior performances in most cases in comparison with the corresponding connectivity-based localization algorithms.

Wireless multi-hop networks with infrastructure support have been actively studied to solve the scalability problem in large scale vehicular and sensor networks that the end-to-end throughput and other performance metrics decrease sharply with the increase in the number of nodes in the network. In the infrastructure-based networks, wireless nodes are allowed to access the base stations either directly or via a multi-hop path. In order to provide meaningful services, it is often desirable to limit the number of hops in the wireless multi-hop path. In this paper, we study a 2-D wireless network where users are Poissonly distributed in a square area and base stations are placed at the four corners of the square area as a typical component of a larger network where users are randomly distributed and base stations are regularly deployed. We obtain analytically the exact and approximate k-hop connectivity probability for k = 2, i.e. the probability that all users can access to at least one base station in at most two hops, under a generic channel model. The results are verified by simulations and can be used in network planning, design and resource management.

In this paper, we study the information propagation speed in a 1D mobile ad hoc network formed by vehicles Poissonly distributed on a highway and travelling in the same direction but with random Gaussianly-distributed speeds, independent between vehicles. Assume that time is divided into time slots of equal length and that each vehicle changes its speed at the beginning of each time slot, independent of its speed in other time slots. We derive analytical formulas for the IPS in the above network under the unit disk model. Using the formula, we can straightforwardly study the impact on the information propagation speed of various parameters such as vehicle density, speed and radio range. The accuracy of the formula is validated using simulations. The research provides useful guidelines on the design of vehicular ad hoc networks.

Many real wireless multi-hop networks are deployed with some infrastructure support, where the results on ad-hoc networks cannot be readily extended to understand the properties of those networks. In this paper, we study those networks in 1-D. Specifically, we consider two types of nodes in the networks: ordinary nodes and powerful nodes, where ordinary nodes are i.i.d and Poissonly distributed in a unit interval and powerful nodes are arbitrarily distributed within the same unit interval. These powerful nodes are inter-connected via some backbone infrastructure. The network is said to be connected, i.e. any two nodes can communicate with each other, if each ordinary node is connected to at least one of the powerful nodes. We call this type of connectivity type-II connectivity. Exact and simplified asymptotic formulas for type-II connectivity probability and the average hop count between two arbitrary nodes are obtained. Further we prove that equi- distant powerful nodes deployment delivers the optimum performance which maximizes the type-II connectivity probability. These results are important for the design and deployment of 1-D infrastructure-based networks and provide useful insights into the analysis of higher dimensional networks.

Coverage is an important problem in wireless networks. Together with the access probability, which measures how well an arbitrary user can access a wireless network, in particular VANET, they are often used as major indicators of the quality of the network. In this paper, we investigate the coverage and access probability of the vehicular networks with roadside infrastructure, i.e. base stations. Specifically, we analyze the relation between these key parameters, i.e. the coverage range of base stations, coverage range of vehicles, vehicle density and distance between adjacent base stations, and how these parameters interact with each other to collectively determine the coverage and the access probability. We use the connectivity probability, the probability that all nodes in the network are connected to at least one base station within a designated number of hops, as a measure of the coverage. We derived close-form formulas for the connectivity probability and the access probability for a 1D vehicular network bounded by two adjacent base stations. The analytical results have been validated by simulations. The results in the paper can be used by network operators to design networks with specific service coverage guarantees.

We analytically characterize the energy consumption per successfully transmitted packet in end-to-end packet transmissions in a wireless sensor network where nodes are identically and independently distributed in a square area following a homogeneous Poisson process. It is assumed that a greedy forwarding protocol is used for routing. We obtain analytical results on the average number of hops between any two nodes if the transmission is successful and the average number of hops traversed by packets before being dropped if the transmission is unsuccessful. A transmission is unsuccessful if the packet sent from a source to a destination has to be dropped at an intermediate node because the greedy forwarding routing protocol is unable to find a next-hop node that is closer to the destination. Based on the above analysis, we derive the effective energy consumption per successfully transmitted packet. We show that there exists an optimum transmission range which minimizes the effective energy consumption and such optimum transmission range can be computed based on our analysis.

Graph theory has been used to characterize the solvability of the sensor network localization problem. If sensors correspond to vertices and edges correspond to sensor pairs between which the distance is known, a significant result in the theory of range-based sensor network localization is that if the graph underlying the sensor network is generically globally rigid and there is a suitable set of anchors at known positions, then the network can be localized, i.e., a unique set of sensor positions can be determined that is consistent with the data. In particular, for planar problems, provided the sensor network has three or more noncollinear anchors at known points, all sensors are located at generic points, and the intersensor distances corresponding to the graph edges are precisely known rather than being subject to measurement noise, generic global rigidity of the graph is necessary and sufficient for the network to be localizable (in the absence of any further information). In practice, however, distance measurements will never be exact, and the equations whose solutions deliver sensor positions in the noiseless case in general no longer have a solution. This paper then argues that if the distance measurement errors are not too great and otherwise the associated graph is generically globally rigid and there are three or more noncollinear anchors, the network will be approximately localizable, in the sense that estimates can be found for the sensor positions which are near the correct values; in particular, a bound on the position errors can be found in terms of a bound on the distance errors. The sensor positions in this case can be found by minimizing a cost function which, although nonconvex, does have a global minimum.

In this paper, we study the well-known phase transition behavior of connectivity in a wireless multi-hop network, but, in contrast to other studies, in a shadowing environment. We consider that a total of n nodes are randomly, independently and uniformly distributed on a unit square in R2, each node has a uniform transmission power and any two nodes are directly connected if and only if the power received by one node from the other node, as determined by the log-normal shadowing model, is larger than or equal to a given threshold. We extend the results obtained under the unit disk communication model in previous work to the more realistic log-normal shadowing model, and derive an analytical formula for the phase transition width of connectivity for large n. We also demonstrate how our results can be extended to higher dimensional networks and to other channel models.

In this paper, we study the giant component, the largest component containing a non-vanishing fraction of nodes, in wireless multi-hop networks in Rd(d = 1, 2). We assume that n nodes are randomly, independently and uniformly distributed in [0, 1]d, and each node has a uniform transmission range of r = r(n) and any two nodes can communicate directly with each other iff their Euclidean distance is at most r. For d = 1, we derive a closed-form analytical formula for calculating the probability of having a giant component of order above pn with any fixed 0.5 < p les 1. The asymptotic behavior of one dimensional network having a giant component is investigated based on the derived result, which is distinctly different from its two dimensional counterpart. For d = 2, we derive an asymptotic analytical upper bound on the minimum transmission range at which the probability of having a giant component of order above qn for any fixed 0 < q < 1 tends to one as n rarr infin. Based on the result, we show that significant energy savings can be achieved if we only require a large percentage of nodes (e.g. 95%) to be connected rather than requiring all nodes to be connected. The results of this paper are of practical significance in the design and analysis of wireless ad hoc networks and sensor networks.

We consider cooperative retransmission strategies in wireless networks, where the retransmission of a failed frame is handled not by the original source but rather by common neighbors overhearing the transmission. The majority of existing literature in this space focuses on opportunistic mechanisms for choosing a single ¿best¿ neighbor, with a goal of minimizing the number of required (re-)transmissions. However, the coordination overhead of such mechanisms renders them unsuitable in general for scenarios involving delay-critical control or sensing applications, where the delivery latency, rather than number of retransmissions, is the dominant performance criterion. Accordingly, we study a distributed uncoordinated setting, where each neighbor that successfully overhears a frame decides independently whether to retransmit it in subsequent time slots, considering that multiple simultaneous such retransmissions will cause a collision. We employ a Bayesian approach to analyze the evolution of the system state view from the perspective of each cooperative neighbor, and derive a strategy of finding a sequence of retransmission probabilities for every neighbor in each time slot to minimize the expected delivery latency. We demonstrate for a wide variety of scenarios that this strategy achieves a significantly lower expected latency than either traditional retransmission or two-hop routing to the destination.

Cooperative communication methods in wireless networks, ranging from relaying by a common neighbor over a single wireless hop to opportunistic routing at the network layer, have been shown in recent years to offer significant performance gains over traditional approaches that ignore the broadcast nature of the wireless medium, and are particularly valuable in environments prone to channel shadowing and fading, such as mobile ad-hoc networks. A common feature of various cooperative methods proposed in the literature is the coordination required to discover the available neighbors and determine the optimal one(s) to be involved in the cooperation. However, the overhead cost of such coordination may be prohibitive in networks with highly dynamic topology (e.g. due to high mobility), as the discovery and negotiation overheads may negate much of the cooperation gains. Accordingly, we present the case for uncoordinated cooperative retransmission, where a node overhearing a frame may retransmit it ¿blindly¿ without any prior coordination with the transmitter, intended receiver, or any other neighbors in the vicinity. We pose and solve the problem of finding the optimal retransmission probability as a function of location, and characterize the optimal uncoordinated cooperation region through the solution of an integral equation that depends only on the a priori node density and wireless propagation model. Through numerical evaluation, we demonstrate that uncoordinated cooperation provides a low-overhead viable alternative, with a frame delivery probability that can even exceed that of coordinated cooperation methods in situations with high noise (or low transmission power) and high node density.

Erroneous local geometric realizations in some parts of the network due to their sensitivity to certain distance measurement errors is a major problem in wireless sensor network localization. This may in turn affect the localization of either the entire network or a large portion of it. This phenomenon is well-described using the notion of "flip ambiguity" in rigid graph theory. In this paper we analytically derive an expression for the flip ambiguity probabilities of arbitrary neighborhoods in two dimensional sensor networks. This probability can be used to mitigate flip ambiguities in two ways: 1) If an unknown sensor finds the probability of flip ambiguity on its location estimate larger than a predefined threshold, it may choose not to localize itself 2) Every known neighbor can be assigned with a confidence factor to its estimated location, reflecting the probability of flip ambiguity; a sensor with an initially unknown location can then choose only those known neighbors with a confidence factor greater than a predefined threshold. A recent study by co-authors have shown that the performance of sequential and cluster based localization schemes in the literature can be significantly improved by correctly identifying and removing neighborhoods with possible flip ambiguities from the localization process. One motivation of this paper is to enhance the performance of the robustness criterion presented in that study by accurately identifying the flip ambiguity probabilities of arbitrary neighborhoods. The various simulations done in this study show that our analytical calculations of the probability of flip ambiguity matches with the simulated detection of the probability very accurately.

Wireless multihop networks are being increasingly used in military and civilian applications. Advanced applications of wireless multihop networks demand better understanding on their properties. Existing research on wireless multihop networks has largely focused on static networks, where the network topology is time-invariant; and there is comparatively a lack of understanding on the properties of dynamic networks with dynamically changing topology. In this paper, we use and extend a recently proposed graph theoretic model, i.e. evolving graphs, to capture the characteristics of such networks. We extend and develop the concepts of route matrix, connectivity matrix and probabilistic connectivity matrix as convenient tools to characterize and investigate the properties of evolving graphs and the associated dynamic networks. The properties of these matrices are established and their relevance to the properties of dynamic wireless multihop networks are introduced.

Energy saving is an important design consideration in wireless sensor networks. In this paper, we analyze the energy savings that can be achieved in a sensor network where each sensor is capable of reducing its transmission power from a maximum power pm, compared with that in a sensor network where each sensor can only transmit at a constant power level pm. To achieve a fair comparison, we assume sensors in both types of sensor networks are connected to the same set of neighbors, i.e. no connection is lost as a result of a sensor reducing its transmission power. We further assume that sensors are distributed in a given area following a Poisson distribution with known node density and the radio propagation is described by a log-normal model. Ignoring boundary effect, we establish analytically the probability for a sensor to achieve an energy saving of at least h dB. We also obtain the expected percentage of energy savings which can be substantial. The research reported in the paper helps to answer questions such as whether the energy savings achieved by using a sensor with a variable-transmission-power (and the consequent extension of its lifetime) justify the additional cost involved in manufacturing it.

In this paper, we study the giant component, the largest component containing a non-vanishing fraction of nodes, in a wireless multi-hop network where n nodes are randomly and uniformly distributed in [0, 1]d(d = 1, 2) and any two nodes can communicate directly with each other if their Euclidean distance is not larger than the transmission range r. We investigate the probability that the size of the giant component is at least a given threshold p with 0.5 < p les 1. For d = 1, we derive a closed-form analytical formula for this probability. For d = 2, we propose an empirical formula for this probability using simulations. In addition, we compare the transmission range required for having a connected network with the transmission range required for having a certain size giant component for d = 2. The comparison shows that a significant energy saving can be achieved if we only require most nodes (e.g. 95%) to be connected to the giant component rather than require all nodes to be connected. The results of this paper are of practical value in the design and analysis of wireless ad hoc networks and sensor networks.

In this paper, we study the phase transition behavior of k-connectivity (k=1,2,...) in wireless multihop networks where a total of n nodes are randomly and independently distributed following a uniform distribution in the unit cube [0,1]d(d = 1,2,3), and each node has a uniform transmission range r(n). It has been shown that the phase transition of k-connectivity becomes sharper as the total number of nodes n increases. In this paper, we investigate how fast such phase transition happens and derive a generic analytical formula for the phase transition width of k-connectivity for large enough n and for any fixed positive integer k in d-dimensional space by resorting to a Poisson approximation for the node placement. This result also applies to mobile networks where nodes always move randomly and independently. Our simulations show that to achieve a good accuracy, n should be larger than 200 when k = 1 and d = 1; and n should be larger than 600 when k les 3 and d = 2, 3. The results in this paper are important for understanding the phase transition phenomenon; and it also provides valuable insight into the design of wireless multihop networks and the understanding of its characteristics.

A major problem in wireless sensor network localization is erroneous local geometric realizations in some parts of the network due to the sensitivity to certain distance measurement errors, which may in turn affect the reliability of the localization of the whole or a major portion of the sensor network. This phenomenon is well-described using the notion of "flip ambiguity" in rigid graph theory. In a recent study by the coauthors, an initial formal geometric analysis of flip ambiguity problems has been provided. The ultimate aim of that study was to quantify the likelihood of flip ambiguities in arbitrary sensor neighborhood geometries. In this paper we propose a more general robustness criterion to detect flip ambiguities in arbitrary sensor neighborhood geometries in planar sensor networks. This criterion enhances the recent study by the coauthors by removing the assumptions of accurately knowing some inter- sensor distances. The established robustness criterion is found to be useful in two aspects: (a) Analyzing the effects of flip ambiguity and (b) Enhancing the reliability of the location estimates of the prevailing localization algorithms by incorporating this robustness criterion to eliminate neighborhoods with flip ambiguity from being included in the localization process.

Consider a wireless multi-hop network formed by distributing a total of n nodes randomly and uniformly in the unit cube [0, 1]d(d = 1, 2, 3) and connecting any two distinct nodes directly iff (if and only if) their Euclidean distance is not greater than a given threshold r(n). We study the phase transition phenomenon of a k-connected (k isin Nopf) multi-hop network in this paper. We show that the phase transition of k-connectivity becomes sharper as n increases. We derive a generic analytical formula for the phase transition width for large n and for any fixed k isin Nopf in d-dimensional space. The result in this paper is important for understanding phase transition behavior, and it provides valuable insight into the design and implementation of wireless multi-hop networks.

The concept of cooperative retransmission in wireless networks has attracted considerable research attention. The basic idea is that when a receiver cannot decode a frame, the retransmission is handled not by its original source but rather by a neighbour that overheard the transmission successfully, and may have a better channel to the destination. However, the majority of existing literature tackles the issue from the physical layer perspective, with either a single cooperating neighbour, or a multiple-neighbour setting where the receiver is capable of combining and decoding the signal from several simultaneous retransmissions. In this paper, we consider the case of multiple cooperating neighbours from a MAC-layer perspective. Thus, we assume a receiver that can only decode one transmission at a time, while multiple simultaneous retransmissions (by several neighbours that had overheard the frame successfully) will cause a collision. As a result, each neighbour with a successfully overheard copy of the frame faces a tradeoff between helping with a cooperative retransmission and possibly causing a collision. Accordingly, we pose the optimization problem of finding a distributed randomized strategy for the cooperating neighbours, which assigns a certain retransmission probability to every neighbour in each time slot, so as to minimize the expected latency until successful reception. We analyse the performance achieved by two approaches: one where the original source is silent while the neighbours conduct their cooperative retransmissions, and another where both the source and the neighbours may have a nonzero retransmission probability simultaneously. We show that the latter approach offers a significant performance improvement over the former one, as well as either traditional retransmission or two-hop routing to the destination.

Given a wireless sensor network (WSN) whose sensors are randomly and independently distributed in a bounded area following a homogeneous Poisson process with density rho and each sensor has a uniform transmission radius of r0, we investigate the probability that two random sensors separated by a known distance x are fc-hop neighbors for some positive integer k in this paper. We give a closed-form equation for computing this probability for k = 2; and also give a recursive equation for evaluating this probability for k > 2 by using some approximations. The accuracy of the approximate analytical solution is validated by simulations. Furthermore, we present an empirical method to correct the discrepancies between the analytical results and the simulation results caused by the approximation. The result of this paper can be useful in a number of sensor network problems, e.g., estimating the transmission delay between two sensors and energy consumed in the transmission, and WSN routing problems.

IEEE 802.11e standard has been published in 2005. In the next few years, we may expect a proliferation of 802.11e capable stations. In the mean time, the legacy 802.11 stations will exist Therefore, it is of practical significance to study the network performance when 802.11 stations and 802.11e stations coexist. In this paper, a novel Markov chain based analytical model is proposed to investigate the coexistence of DCF and EDCA, which are the fundamental access mechanisms for 802.11 and 802.11e respectively. The performance impact of the differences between DCF and EDCA is analyzed, including the contention window (CW) size, the interframe space (IFS), the backoff counter decrement rule, and the transmission timing when the backoff counter reaches zero. Based on the proposed model, the saturated throughput is analyzed. Simulation study is carried out to evaluate the accuracy of the proposed model.

The path loss exponent (PLE) is a parameter indicating the rate at which the received signal strength (RSS) decreases with distance, and its value depends on the specific propagation environment. Path loss exponent estimation plays an important role in distance-based wireless sensor network localization, where distance is estimated from the RSS measurements. Path loss exponent estimation is also useful for other purposes like sensor network dimensioning. Existing techniques on PLE estimation rely on both RSS measurements and distance measurements in the same environment to calibrate the PLE. However distance measurements can be difficult and expensive to obtain in some environments. In this paper we propose a novel technique for online calibration of the path loss exponent in wireless sensor networks without using distance measurements. The major contribution of this paper is to demonstrate that it is possible to estimate the PLE using only power measurements and the geometric constraints associated with planarity in a sensor network. This may have a significant impact on wireless sensor network localization.

IEEE 802.11e standard has been recently published to introduce quality of service (QoS) support to the conventional IEEE 802.11 wireless local area network (WLAN). Enhanced Distribution Channel Access (EDCA) is used as the fundamental access mechanism for the medium access control (MAC) layer in IEEE 802.11e. In this paper, a novel Markov chain model with a simple architecture for EDCA performance analysis under the saturated traffic load is proposed. Compared with existing analytical models, the proposed model considers more features of EDCA. Firstly, the effect of using different arbitration interframe spaces (AIFSs) is analyzed. Secondly, the possibility that a station's backoff procedure may be suspended due to transmission from other stations is considered. We consider that the contention zone specific transmission probability caused by using different AIFSs can affect the occurrence of the backoff suspension state. Based on the proposed model, saturated throughput of EDCA is obtained. Simulation study is performed, which demonstrates that the proposed model has better accuracy than other models.

Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase simulated annealing based localization (SAL) algorithm to address the issue. Simulated annealing (SA) is a technique for combinatorial optimization problems and it is robust against being trapped into local minima. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed scheme gives accurate and consistent location estimates of the nodes and mitigate errors due to flip ambiguities.

In sensor networks, the information obtained from sensors are meaningless without the location information. In this paper, we propose a simulated annealing based localization (SAL) scheme for wireless sensor networks. Simulated annealing (SA) is used to estimate the approximate solution to combinatorial optimization problems. The SAL scheme can bring the convergence out of the local minima in a controlled fashion. Simulation results show that this scheme gives accurate and consistent location estimates of the nodes.

The bufferless fluid flow model (BFFM) is often used in the literature for loss performance analysis. We propose an efficient and effective means of investigating cell loss using the BFFM. We define the cell loss rate function (CLRF) and use it to characterize the loss performance of traffic sources in the BFFM. Stochastic ordering theory is used to study the CLRF. The introduction of the stochastic ordering theory not only simplifies the theoretical analysis but also makes it possible to extend the scope of applications and theoretical analysis presented. A cell loss upper bound for heterogeneous on-off sources is proposed. The proposed cell loss upper bound is tighter than those previously proposed in the literature. A connection admission control (CAC) scheme using online measurements is designed based on the cell loss upper bound. Extensive simulation is carried out to study the performance of the CAC scheme. Particular attention is paid to the impact of inaccuracies in user-declared traffic parameters on the performance of the CAC scheme. Simulation results indicate that the proposed CAC scheme can ensure QoS guarantee, is robust to inaccuracies in declared traffic parameters, and is capable of achieving high link utilization.

On-off traffic source models are extensively used in connection admission control (CAC) schemes because of their simplicity and their ability to model real traffic sources. However, the calculation of the probability mass function of heterogeneous on-off sources entails a convolution operation which cannot be carried out in real time. Based on a bufferless fluid flow model this paper proposes the cell loss rate function (CLRF) and uses it for studying cell loss in bufferless systems. The properties of heterogeneous on-off sources in bufferless systems are extensively discussed using CLRF. A tight cell loss upper bound for heterogeneous on-off sources is proposed which results in great computational savings. The upper bound is applied in the CAC scheme. Simulation studies are presented which indicate that the upper bound is tight and the CAC scheme has a very good performance.