With the advent of programmable switches, sketch-based measurements have become a powerful tool for traffic monitoring, offering high accuracy with minimal resource overhead. Conventional sketch designs provide uniform accuracy across all traffic classes, failing to address the diverse needs of different network applications. Recent efforts in priority-aware sketch-based measurements have enhanced accuracy for large flows. However, providing explicit guarantees for differentiated accuracy across multiple traffic categories under limited memory resources remains a challenge. To address this limitation, we introduce DiffSketch, a sketch-based measurement system that guarantees differential accuracy across traffic classes. DiffSketch employs a block-based biased hashing design, which dynamically adjusts block sizes and leverages biased hashing techniques to enable probabilistic block access. This design ensures that measurement accuracy aligns with operator-defined differentiation levels while optimizing memory usage. We implement DiffSketch on both bmv2 and Tofino switches, demonstrating that its block-based approach not only guarantees differential performance but also improves overall memory efficiency, reducing measurement errors compared to existing priority-aware solutions.

As Internet of Things (IoT) devices are widely deployed for remote data sensing, Low Earth Orbit (LEO) satellite networks are used in areas where traditional terrestrial ground stations are unavailable. To address the challenges of dynamic topologies and limited spectrum resources between satellites and IoT devices, recent research has focused on improving spectrum utilization through transmission scheduling. However, these efforts have often overlooked the timing requirements of IoT data collection. To address this limitation, our work introduces a deadline-aware data collection approach for satellite networks. We formulate an optimization model that explicitly accounts for the penalties associated with data expiration and aims to maximize on-time data collection. A greedy algorithm is proposed to solve this optimization problem by prioritizing data transmissions based on dynamic channel conditions and data urgency. Simulations conducted using NS3 show that deadline-aware scheduling effectively meets heterogeneous deadlines and, compared to deadline-oblivious scheduling, improves resource utilization across various scenarios.

Distributed Deep Learning (DDL) has become essential for accelerating model training to manage the growing size of models and datasets. However, the communication overhead required for synchronizing model weights across parallel workers would be a performance bottleneck of DDL training. Recently, in-network aggregation (INA) has been proposed to harness the computational capabilities of programmable switches to merge weights before forwarding, thereby reducing bandwidth consumption during synchronization. However, ensuring the reliability of INA incurs significant retransmission costs and coordination overhead. To fully optimize the benefits of INA, we introduce Coding-based INA (CINA), a system that integrates network coding with INA to facilitate efficient loss recovery. Moreover, we propose a priority-aware weight batching (grouping) scheme that leverages model sparsity to enhance bandwidth utilization in our coding-based INA. Emulation results demonstrate that our design effectively reduces feedback overhead by up to 94.87% and improves overall throughput by up to 2.64×. Compared to priority-oblivious batching, our priority-aware weight batching reduces the mean square error of model weights by around 15.27%.

While sketch-based network telemetry is attractive, realizing its potential benefits has been elusive in practice. Existing sketch solutions offer low-level interfaces and impose high effort on operators to satisfy telemetry intents with required accuracies. Extending these approaches to reduce effort results in inefficient deployments with poor accuracy-resource tradeoffs. We present SketchPlan, an abstraction layer for sketch-based telemetry to reduce effort and achieve high efficiency. SketchPlan takes an ensemble view across telemetry intents and sketches, instead of existing approaches that consider each intent-sketch pair in isolation. We show that SketchPlan improves accuracy-resource tradeoffs by up-to 12x and up-to 60x vs. baselines, in single-node and network-wide settings. SketchPlan is open-sourced at: https://github.com/milindsrivastava1997/SketchPlan.

Network measurement plays a crucial role in evaluating and monitoring the performance and reliability of a network. Among diverse methods, sketch-based monitoring has become more and more popular due to its high accuracy and resource efficiency. Recent studies have investigated how to enhance the performance of sketch-based monitoring by adaptively allocating the memory resources of a single switch or multiple cooperative switches. We, however, notice that those approaches assume that flows have been assigned the fixed routing paths and, thus, can hardly unleash the full potential of multi-switch cooperative monitoring. In this work, we present Joint Routing and Sketch configuration (JRS), a system that jointly solves the routing and monitoring switch assignment problem. The core idea of our design is to assign each flow a monitoring switch that provides high accuracy while avoiding a route detour. The trace-driven evaluation demonstrates that our proposed JRS reduces the worst relative error among all the flows by up to 39.59 x with an almost negligible increase in the routing cost.

Distributed deep learning (DDL) has recently been proposed to accelerate the training process of a deep learning model. The core idea is to have multiple workers collaboratively train a model in parallel. DDL, however, relies on synchronization among participating workers, which introduces significant communication overhead. To resolve this issue, recent research has demonstrated the effectiveness of in-network aggregation (INA), which reduces the bandwidth requirement of DDL training by allowing a programmable switch to combine the parameters of workers and only forward the aggregated parameter to the master. We, however, notice that existing approaches mainly focus on single-switch in-network aggregation and may overload a switch when the number of competing training jobs grows. In this work, we present Multi-Switch In-Network Aggregation (MS-INA), a system that efficiently offloads the aggregation load of DDL jobs across the switches of a network. To fully leverage the potential of all the available programmable switches for aggregation jobs, we assign each training job an aggregator switch that minimizes the end-to-end training latency. To this end, our MS-INA identifies the optimal switch that not only performs efficient aggregation but also introduces a short parameter forwarding latency. The trace-driven evaluation demonstrates that MS-INA effectively leverages the computational capability of all the switches and increases the number of successfully aggregated parameters by up to 5.3× as compared to conventional single-switch INA. The increasing aggregation capability contributes to bandwidth reduction by up to 64%.

This study developed an elastic wristband called Polyband for profiling the unique deformation of the skin and muscles around the wrist during hand gestures through resistance measurements. A proof-of-concept design was demonstrated to recognize a simple set of gestures with 92.9% accuracy.

5G New Radio (NR), a new radio access technology, has been proposed to enhance the flexibility, scalability, and efficiency of 5G networks. By leveraging phased-array antennas, a base station (BS) can adaptively form multiple directional beams to serve geo-distributed user equipments (UEs) concurrently. However, the imperfect beam pattern of a phased-array antenna may create side lobes, leading tointer-beam interference. While recent efforts have focused on beam selection that mitigates inter-beam interference and maximizes the sum-rate, we notice that the selected beams may not be fully utilized in an OFDMA-based system. The root cause is that only a fixed set of beams can be configured at a time to serve a wide frequency band but some resource blocks (i.e., subcarriers) may not be able to be allocated to any UEs due to limited traffic demands. To address the above resource underutilization problem, this paper presentstraffic-aware joint beam configuration and resource allocation, which explicitly considers user traffic demands and configures beams that can optimally utilize the spectrum resources. We derive an approximation model that produces a suboptimal solution solvable by open-source solvers and further develop a low-complexity greedy algorithm for large-scale networks. Our simulation results show that the proposed traffic-aware allocation and beam configuration scheme achieves better utilization, especially when users have heterogeneous traffic demands. The overall effective throughput can be significantly enhanced as compared to conventional sum-rate maximization allocation.

Recently, there has been a growing interest in coflow scheduling due to the rise of data-intensive applications. However, existing solutions rely on modifying hosts to obtain coflow information and cooperatively prioritize their packets. Such a host-assisted approach may not work for public data centers and could be problematic when the central controller becomes a bottleneck. In this work, we present PICO, an in-network coflow scheduling system allowing a programmable switch to prioritize coflows in a fully distributed way. In the absence of host cooperation, we develop a pairwise coflow detection scheme that clusters sequentially arrived flows. We further design a data plane pipeline that enables fast feature extraction and efficient coflow size tracking for real-time priority adaptation. The experiments show that our sequential coflow grouping achieves an accuracy of up to 99%. The coflows, on average, complete 1.28× faster than per-flow fair sharing, showing the effectiveness of PICO's distributed in-network scheduling even with no hardware modification and host cooperation.

Nowadays, with the emergence of software-defined networking, sketch-based network measurements have been widely used to balance the tradeoff between efficiency and reliability. The simplicity and generality of a sketch-based system allow it to track divergent performance metrics and deal with heterogeneous traffic characteristics. However, most of the existing proposals mainly consider priority-agnostic measurements, which introduce equal error probability to different classes of traffic. While network measurements are usually task-oriented, e.g., traffic engineering or intrusion detection, a system operator may be interested only in tracking specific types of traffic and expect various levels of tracking resolutions for different traffic classes. To achieve this goal, we propose MC-Sketch (Multi-Class Sketch), a priority-aware system that provides various classes of traffic with differential accuracy subject to the limited resources of a programmable switch. It privileges higher priority traffic in accessing the sketch over background traffic and naturally provides heterogeneous tracking resolutions. The experimental results and large-scale analysis show that MC-Sketch reduces the measurement errors of high priority flows by 56.92% without harming the overall accuracy much.

The emergence of Network Function Virtualization (NFV) and Service Function Chaining (SFC) together enable flexible and agile network management and traffic engineering. Recently, Network Function Parallelism (NFP) has been proposed to break the need for sequential services and, hence, significantly reduce the latency of SFC. While some studies have investigated how to identify parallel paths to enhance the efficiency of parallelism, less effort has been paid to efficient network function embedding with consideration of parallelism opportunities. Hence, this work aims at solving the instance deployment problem so as to prevent parallel functions from waiting for each other before continuing to the next stage. As it is extremely difficult to optimize VNF (Virtual Network Functions) embedding for uncertain parallelism opportunities, we propose a practical embedding scoring mechanism to enhance the likelihood of low-cost function parallelism. Our scoring design tends to cluster independent functions for efficient parallelism while ensuring load balancing. After instance deployment, we then assign function instances to parallel chains so as to minimize their end-to-end latency while balancing the workload of instances. Our evaluation results show that the proposed scoringbased embedding scheme ensures homogeneous delays of parallel functions and, hence, reduces the end-to-end latency for NFP by up to 22% as compared to the embedding algorithm without considering parallelism opportunities.

Software-defined network (SDN) allows traffic engineering and flow management by decoupling the control and data planes. SDN has attracted a lot of attention recently because of its centralized control, flow programmability and flexible resource management. However, to support centralized control, flow programmability and flexible resource management, most of the advanced SDN designs require per-flow management, which would easily make the flow table of a software switch overflow and incur undesired processing overhead when a network scales up. To overcome such huge traffic demands, we propose acluster-based macro-flow managementframework to balance the trade-off between customized services and management costs. The key idea of our design is to group similar flows into a cluster and replace per-flow management with per-cluster management. To achieve this goal, we cluster flows with consideration of the similarity of their traffic patterns. To adapt to varying traffic load, we further propose a hierarchical cluster merging scheme to dynamically merge clusters and make the best tradeoff between flow entry saving and routing performance guarantee. Our evaluation demonstrates that the proposed cluster-based flow management framework can significantly reduce the flow table usage, while producing minimum negative impact on routing performance.

Cloud radio access network (C-RAN) is a key technology of 5G cellular networks. While a C-RAN collaboratively manages a pool of collaborative Baseband units (BBUs), user equipment (UEs) locating in the interference range of remote radio units (RRUs) would interfere with each other. To enhance spectrum efficiency, Coordinated Multipoint (CoMP) has been proposed to enable network MIMO and suppress inter-cell interference. However, channel estimation from collisions requires perfect alignment of resource allocation for CoMP and can never be perfect, thereby lowering the rate of MIMO equalization. To resolve the above issues, we propose to opportunistically enable CoMP based on dynamic traffic demands and explicitly allocate resources with consideration of the alignment constraint introduced by channel estimation from collisions. Our simulations show that adaptive resource allocation for opportunistic CoMP improves the overall throughput by up to 33.1% and 12.6% as compared to naive muting and CoMP allocation.

The emergence of Network Function Virtualization (NFV) and Service Function Chaining (SFC) together enable flexible and agile network management and traffic engineering. Due to the sequential execution nature of SFC, the latency would grow linearly with the number of functions. To resolve this issue,function parallelizationhas recently been proposed to enable independent functions to work simultaneously. Existing solutions, however, assume all the function instances are installed in the same physical machine and, thus, can be parallelized with only a little overhead. Nowadays, most of the networks deploy function instances in distributed servers for load balancing, parallelization across different servers would, in fact, introduce a non-negligible cost of duplicating or merging packets. Hence, in this work, we propose PPC (Partial Parallel Chaining), which only parallelizes functions if parallelization can indeed reduce the latency after considering function placement and the required additional parallelization cost. To this end, we design two schemes,partial parallelism enumerationandinstance assignmentto identify the optimal partial parallelism that minimizes the latency. Our simulation results show that PPC effectively adapts the degree of parallelism and, hence, outperforms both sequential chaining and full parallelism in any general scenario. Overall, the latency reduction can be up to 47.2% and 35.2%, respectively, as compared to sequential chaining and full parallelism.

Cross-Technology Interference (CTI) is one of the major issues that hinder WiFi networks from achieving full spectrum utilization. Interference from nearby ZigBee devices, LTE-U UEs or even microwave ovens could emit RF signals over the frequency partially overlapping with the WiFi band. To combat such CTI, existing solutions have proposed several signal processing algorithms for error recovery or interference cancellation. However, most of those approaches need knowledge about the physical layer structure of CTI, which cannot be applied to denoise the unstructured interference from unknown electronics, e.g., microwave ovens. To overcome this deficiency, we present a CTI suppression framework based on Denoising AutoEncoder (DAE). The DAE is developed to learn the patterns of interference with unknown structures and passively suppress CTI with the zero cost. To avoid the expansive human cost of data collection, we propose a systematic way to synthesize corrupted WiFi signals for model training. Our experiments verify that the model trained with synthesized data can effectively reconstruct real corrupted WiFi signals and improve the decoding success probability.

The emergence of voice over LTE enables voice traffic transmissions over 4G packet-switched networks. Since voice traffic is characterized by its small payload and frequent transmissions, the corresponding control channel overhead would be high. Semi-persistent scheduling (SPS) is hence proposed in LTE-A to reduce such overhead. However, as wireless channels typically fluctuate, tremendous retransmissions due to poor channel conditions, which are still scheduled dynamically, would lead to a large overhead. To reduce the control message overhead caused by SPS retransmissions, we propose a new SPS retransmission protocol. Different from traditional SPS, which removes the downlink control indicators (DCI) directly, we compress some key fields of all retransmissions' DCIs in the same subframe as a fixed-length hint. Thus, the base station does not need to send this information to different users individually but just announces the hint as a broadcast message. In this way, we reduce the signaling overhead and at the same time, preserve the flexibility of dynamic scheduling. Our simulation results show that, by enabling DCI compression, our design improves signaling efficiency by 2.16×, and the spectral utilization can be increased by up to 60%.

3GPP has been defining 5G New Radio (NR), a new radio access technology, to enhance flexibility, scalability, and efficiency of 5G networks. An increasing data rate can be achieved by leveraging antenna arrays to adaptively form multiple directional beams and serve geo-distributed user equipments (UEs) concurrently. However, the imperfect beam pattern of an antenna array may create side lobes, leading to inter-user interference. While most recent research focuses on beam selection that mitigates inter-user interference and maximizes the sum rate, we, however, notice that the selected beams may not be fully utilized. The root cause is that only a fixed set of beams can be configured at a time to serve a wide frequency band but some resource blocks (i.e., subcarriers) may not be able to be allocated to any UEs due to limited traffic demands. To address such inefficiency, this paper presents traffic-aware joint beam configuration and resource allocation, which explicitly considers UEs' traffic demands and configures beams that can be optimally utilized in all the RBs (i.e., the operational frequency band). Our simulation results show that our traffic-aware allocation configures beams with better utilization and achieve an effective throughput much higher than conventional maximal capacity configuration.

Software Defined Networking (SDN) has been emerged to solve the problem of traditional network architectures. The ability of programmable switches renders us an opportunity to have computational tasks done in the switches. With this nice property, in this work, we investigate the potential of enabling machine learning inside a network. To this end, we propose a new architecture, Intra-Network Inference (INI), which equips each switch with a recently released component, called neural compute stick (NCS), to enable intra-switch neural network inference. Unlike conventional SDN architectures, which relay backend servers to enable inference, our INI performs inference locally at switches and, thereby, reduces the data forwarding overhead and inference latency.

Advanced machine learning and deep learning techniques have increasingly improved accuracy of image classification. Most existing studies have investigated the data imbalance problem among classes to further enhance classification accuracy. However, less attention has been paid to data imbalance within every single class. In this work, we present AC-GAN (Actor-Critic Generative Adversarial Network), a data augmentation framework that explicitly considers heterogeneity of intra-class data. AC-GAN exploits a novel loss function to weigh the impacts of different subclasses of data in a class on GAN training. It hence can effectively generate fake data of both majority and minority subclasses, which help train a more accurate classifier. We use defect detection as an example application to evaluate our design. The results demonstrate that the intra-class distribution of fake data generated by our AC-GAN can be more similar to that of raw data. With balanced training for various subclasses, AC-GAN enhances classification accuracy for no matter uniformly or non-uniformly distributed intra-class data.

Advanced Internet of Things (IoT) techniques have made human-environment interaction much easier. Existing solutions usually enable such interactions without knowing the identities of action performers. However, identifying users who are interacting with environments is a key to enable personalized service. To provide such add-on service, we propose WTW (who takes what), a system that identifies which user takes what object. Unlike traditional vision-based approaches, which are typically vulnerable to blockage, our WTW combines the feature information of three types of data, i.e., images, skeletons and IMU data, to enable reliable user-object matching and identification. By correlating the moving trajectory of a user monitored by inertial sensors with the movement of an object recorded in the video, our WTW reliably identifies a user and matches him/her with the object on action. Our prototype evaluation shows that WTW achieves a recognition rate of over 90% even in a crowd. The system is reliable even when users locate close by and take objects roughly at the same time.

Person identification and tracking (PIT) is an essential issue in computer vision and robotic applications. It has long been studied and achieved by technologies such as RFID or face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition accuracy highly depends on use scenarios. In this work, we propose RCU (Robot Catch yoU), an accompanyist robot system that provides follow-me or guide-me services. Such robots are capable of distinguishing users' profiles in front of them and keep tracking a specific target person. We study a more challenging scenario where the target person may be under occlusion from time to time. To enable robust PIT, we develop a data fusion technique that integrates two types of sensors, an RGB-D camera and wearable inertial sensors. Since the data generated by these sensors share common features, we are able to fuse them to achieve identity-aware tracking. Practical issues, such as time synchronization and coordinate calibration, are also addressed. We implement our design on a robotic platform and show that it can track a target person even when no biological feature is captured by the RGB-D camera. Our experimental evaluation shows a recognition rate of 95% and a following rate of 88%.

In recent years, it clearly emerged an increasing importance of experimental validation of innovative wireless solutions and applications, due to the increasing complexity of the wireless ecosystem. The WiNTECH workshop explicitly deals with methodological and technical issues that have to be faced for defining, running, controlling and benchmarking experiments on wireless solutions. The workshop, that this year reach the 13th edition, has a consolidated tradition for bringing together an important number of researchers and industry players working in different aspects of experimental wireless communications. The workshop will serve as a forum for sharing experiences and results with real testbeds, experimental evaluation, prototyping and empirical characterization of wireless technologies.

Emerging applications require the location information of clients to enable human-environment interactions or personalized services. With an increasing number of antennas equipped in today's wireless devices, recent research has shown possibility of sub-meter level localization based only on the angle of arrival (AoA) of WiFi sig- nals. While most existing work provides promising median accu- racy, their tail performance however is usually far worse. We ob- serve from measurements that the root cause is due to unequal AoA estimation reliability. In some critical areas, a small variation in the channel state information of signals could introduce an extremely large AoA estimation error. With this observation, we propose UAT (Unequal Angle Tracking), a confidence-aware AoA-based localiza- tion system. We show that unequal reliability of AoA measures can be mathematically quantified, allowing a system to weigh the de- cisions of different APs according to their confidence. Our testbed evaluation shows that UAT's confidence-aware design provides reli- able decimeter level localization for around 90% of locations. UAT is especially effective for risky areas and can reduce their localiza- tion errors by 27.5%, as compared to reliability-oblivious designs.

Traditional IEEE 802.11 network is designed for the use of small scale local wireless networks. However, the emergence of the Internet of Things (IoT) has changed the scene of wireless communications. Thus, recently, the IEEE task group ah (TGah) has been dedicated to the standardization of a new protocol, called IEEE 802.11ah, which is customized for this type of large-scale networks. IEEE 802.11ah adopts a grouping-based MAC protocol to reduce the contention overhead for each group of devices. However, most existing designs simply randomly partition devices into groups, and less attention has been paid to the problem of forming efficient groups. Therefore, in this paper, we argue that the performance of grouping is closely related to heterogeneity in traffic demands of devices, and propose a traffic-aware grouping algorithm to improve channel utilization. Since channel utilization of a group closely depends on the collision probability, we further derive a regression-based analytical model to estimate the contention success probability with consideration of sensors' heterogeneous traffic demands. The evaluation via NS-3 simulations shows that the proposed regression-based model is quite accurate even when clients have diverse traffic demands, and our traffic-aware grouping outperforms other baseline approaches, especially when the network is nearly saturated.

Recent studies have experimentally shown the gains of full-duplex radios. However, due to its relatively higher cost and complexity, we can envision a more practical step in the network evolution is to have a full-duplex access point (AP) but keep the clients half-duplex. Unfortunately, the full-duplex gains can hardly be extracted in practice as the uplink transmission from a half-duplex client introduces inter-client interference to another downlink client. To address this issue, we present the design and implementation of IC2 (Inter-Client Interference Cancellation), the first physical layer solution that exploits the AP's full-duplex capability to actively cancel the interference at the downlink client. Such active cancellation not only improves the achievable capacity, but also better tolerates imperfect user pairing, simplifying the MAC design as a result. We build a prototype of IC2 on USRP-N200 and evaluate its performance via both testbed experiments and large-scale trace-driven simulations. The results show that, without IC2, about 60% of client pairs produce no gain from full-duplex transmissions, while, with IC2 the median gain of the achievable rate over conventional half-duplex networks can be $1.65\times $ and $1.47\times $ for 1- and 2-antenna scenarios, respectively, even when clients are simply paired randomly.

Traffic management is known to be important to effectively utilize the high bandwidth provided by datacenters. Recent works have focused on identifying elephant flows and rerouting them to improve network utilization. These approaches however require either a significant monitoring overhead or hardware/end-host modifications. In this paper, we propose FlowSeer, a fast, low-overhead elephant flow detection and scheduling system using data stream mining. Our key idea is that the features from flows' first few packets allow us to train the streaming classification models that can accurately and quickly predict the rate and duration of any initiated flow. With these predicted information, FlowSeercan adapt routing polices of elephant flows to their demands and dynamic network conditions. Another nice property of FlowSeeris its capability of enabling the controller and switches to perform cooperative prediction. Most of decisions can be made by switches locally, thereby reducing both detection latency and signaling overhead. FlowSeerrequires less than 100 flow table entries at each switch to enable cooperative prediction, and hence can be implemented on off-the-shelf switches. The evaluation via both experiments in realistic virtual networks and trace-driven simulations shows that FlowSeerimproves the throughput by multiple times over Hedera, which pulls flow statistics, and performs comparably to Mahout, which needs end-host modification.

The surge of M2M devices imposes new challenges for the current cellular network architecture, especially in radio access networks. One of the key issues is that the M2M traffic, characterized by small data and massive connection requests, makes significant collisions and congestion during network access via the random access (RA) procedure. To resolve this problem, in this paper, we propose a paging cycle-based protocol to facilitate the random access procedure in LTE-A. The high-level idea of our design is to leverage a UE's paging cycle as a hint to preassign RA preambles so that UEs can avoid preamble collisions at the first place. Our rpHint has two modes: (1) collision-free paging, which completely prevents cross-collision between paged user equipment (UEs) and random access UEs, and (2) collision-avoidance paging, which alleviates cross-collision. Moreover, we formulate a mathematical model to derive the optimal paging ratio that maximizes the expected number of successful UEs. This analysis also allows us to adapt dynamically to the better one between the two modes. We show via extensive simulations that our design increases the number of successful UEs in an RA procedure by more than 3× as compared to the legacy RA scheme of the LTE.

Person identification (PID) is a key issue in many IoT applications. It has long been studied and achieved by technologies such as RFID and face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition rates highly depend on use scenarios. To enable reliable and remote PID, in this work, we present EOY (Eye On You)1, a data fusion approach that combines two kinds of sensors, a 3D depth camera and wearable sensors embedded with inertial measurement unit (IMU). Since these two kinds of data share common features, we are able to fuse them to conduct PID. Further, the result can be transferred to a mobile platform (such as robot) since we have less constraints on devices. To realize EOY, we develop fusion algorithms to address practical challenges, such as asynchronous timing and coordinate calibration. The experimental evaluation shows that EOY can achieve the recognition rate of 95% and is very robust even in crowded areas.

The emergence of Network Function Virtualization (NFV) enables flexible and agile service function chaining in a Software Defined Network (SDN). While this virtualization technology efficiently offers customization capability, it however comes with a cost of consuming precious TCAM resources. Due to this, the number of service chains that an SDN can support is limited by the flowtable size of a switch. To break this limitation, this paper presents CRT-Chain, a service chain forwarding protocol that requires only constant flowtable entries, regardless of the number of service chain requests. The core of CRT-Chain is an encoding mechanism that leverages Chinese Remainder Theorem (CRT) to compress the forwarding information into small labels. A switch does not need to insert forwarding rules for every service chain request, but only needs to conduct very simple modular arithmetic to extract the forwarding rules directly from CRT-Chain's labels attached in the header. We further incorporate prime reuse and path segmentation in CRT-Chain to reduce the header size and, hence, save bandwidth consumption. Our evaluation results show that, when a chain consists of no more than 5 functions, CRT-Chain actually generates a header smaller than the legacy 32-bit header defined in IETF. By enabling prime reuse and segmentation, CRT-Chain further reduces the total signaling overhead to a level lower than the conventional scheme, showing that CRT-Chain not only enables scalable flowtable-free chaining but also improves network efficiency.

With the increasing popularity of machine-type communication (MTC) devices, several new challenges are encountered by the legacy long term evolution (LTE) system. One critical issue is that a massive number of MTC devices trying to conduct random access procedures may cause significant collisions and long delays. In this work, we present a new random access mechanism by splitting the contention-based preambles in LTE into two logically disjoint parts, one for the user equipment (UE) being paged and the other for the UEs not being paged. Since the IDs of paged UEs are known by the base station, a novel hash-based random access, which we call hint, is possible. The main idea is to pre-allocate preambles to paged UEs in a contention-free manner and confines non-paged UEs to contend in a separate region. We further build a mathematical model to find the optimal ratio of pre-allocated preambles. Extensive simulations are conducted to validate our results.

Inertial measurement unit (IMU) has long suffered from the problem of integration drift, where sensor noises accumulate quickly and cause fast-growing tracking errors. Existing methods for calibrating IMU tracking either require human in the loop, or need energy-consuming cameras, or suffer from coarse tracking granularity. We propose to augment indoor inertial tracking by reusing existing indoor luminaries to project a static light polarization pattern in the space. This pattern is imperceptible to human eyes and yet through a polarizer, it becomes detectable by a color sensor, and thus can serve as fine-grained optical landmarks that constrain and correct IMU's integration drift and boost tracking accuracy. Exploiting the birefringence optical property of transparent tapes -- a low-cost and easily-accessible material -- we realize the polarization pattern by simply adding to existing light cover a thin polarizer film with transparent tape stripes glued atop. When fusing with IMU sensor signals, the light pattern enables robust, accurate and low-power motion tracking. Meanwhile, our approach entails low deployment overhead by reusing existing lighting infrastructure without needing an active modulation unit. We build a prototype of our light cover and the sensing unit using off-the-shelf components. Experiments show 4.3 cm median error for 2D tracking and 10 cm for 3D tracking, as well as its robustness in diverse settings.

Multi-user multiple input multiple output (MU-MIMO) enables a multi-antenna access point to serve multiple users simultaneously, and has been adopted as the IEEE 802.11ac standard. While several PHY-MAC designs have recently been proposed to improve the throughput performance of a MU-MIMO WLAN, they, however, usually assume that all the concurrent streams are of roughly equal length. In reality, users usually have frames with heterogeneous lengths even after aggregation, leading to different lengths of a transmission time. Hence, the concurrent transmission opportunities might not always be fully utilized when some streams finish earlier than the others in a transmission opportunity. To resolve this inefficiency, this paper presents full degree-of-freedom (FDoF), a PHY-MAC design that exploits a novel power allocation scheme to reduce the idle channel time and further leverages frame padding to better utilize the spatial multiplexing gain. Unlike traditional MIMO power allocation, which aims at maximizing the theoretical sum-rate, FDoF's power allocation explicitly considers heterogeneous frame lengths and minimizes the channel time required to finish concurrent frames, as a result improving the effective throughput. FDoF's padding protocol then identifies proper users to reuse the remaining idle channel time, while preventing this padding from harming all the ongoing streams. Our evaluation via large-scale trace-driven simulations demonstrates that FDoF's improves the throughput by up to 2.83×, or by 1.36× on average, as compared to the conventional 802.11ac. By combining FDoF's power allocation with frame padding, the average throughput gain can be further increased to 1.75×.

The subsystem in a smartphone means its hardware components, such as the CPU, GPU, and screen. Accurately estimating subsystem power consumption of commercial smartphones is necessary for applicable to wide research areas. Current subsystem power estimation techniques are mostly based on power models, resulting in considerable errors for various types of power consumption behaviors. These include (1) asynchrony between the measured power consumption and the corresponding workload statistics, and (2) nonlinearity concerning CPU idle states, pixels colors of AMOLED screen, and GPU workload statistics. In this study, we propose a novel utilization-based, subsystem power estimation method for a smartphone, namely Clustering and Symbolic Regression (CSR) that takes these power consumption behaviors into account so as to increase power estimation accuracy. To address asynchrony, we cluster the subsystem workload statistics into synchronous and asynchronous groups by employing affinity propagation clustering. To address nonlinearity, we employ symbolic regression for fitting measured power consumptions with respect to subsystem workload statistics. We compare our approach with various power estimation methods, Linear Regression Model (LM), Genetic Programming (GP), and Support Vector Regression (SVR). The results show Mean Absolute Percentage Error (MAPE) reduction between 23.61 and 42.55 percent on the estimated power consumption of a simple (Nexus S) and complex (Galaxy S4) smartphone subsystems.

Recent studies have experimentally shown the gains of full-duplex radios. However, due to its relatively higher cost and complexity, we can envision a more practical step in the network evolution is to have a full-duplex access point (AP) but keep the clients half-duplex. Unfortunately, the full-duplex gains can hardly be extracted in practice as the uplink transmission from a half-duplex client introduces inter-client interference to another downlink client. To address this issue, we present the design and implementation of IC2 (Inter-Client Interference Cancellation), the first physical layer solution that exploits the AP's full-duplex capability to actively cancel the interference at the downlink client. Such active cancellation not only improves the achievable capacity, but also better tolerates imperfect user pairing, simplifying the MAC design as a result. We build a prototype of IC2 on USRP-N200 and evaluate its performance via both testbed experiments and large-scale trace-driven simulations. The results show that, without IC2, about 60% of client pairs produce no gain from full-duplex transmissions, while, with IC2 the median throughput gain over conventional half-duplex networks can be 1.65× even when clients are simply paired randomly.

Multi-User Multiple Input Multiple Output (MU-MIMO) enables a multi-antenna access point (AP) to serve multiple users simultaneously, and has been adopted as the IEEE 802.11ac standard. While several PHY-MAC designs have recently been proposed to improve the throughput performance of a MU-MIMO WLAN, they, however, usually assume that all the concurrent streams are of roughly equal length. In reality, users usually have frames with heterogeneous lengths even after aggregation, leading to different lengths of transmission time. Hence, the concurrent transmission opportunities might not always be fully utilized when some streams finish earlier than the others in a transmission opportunity (TXOP). To resolve this inefficiency, this paper presents acPad, a PHY-MAC design that adds additional frames to fill up the idle channel time and better utilize the spatial multiplexing gain. Our acPad identifies proper users as the padding so as to improve the padding gain, while preventing this padding from harming all the ongoing streams. Our evaluation via large-scale trace-driven simulations demonstrates that acPad improves the throughput by up to 2.83×, or by 1.36× on average, as compared to the conventional 802.11ac.

Accurate orientation information is the key in many applications, ranging from map reconstruction with crowdsourcing data, location data analytics, to accurate indoor localization. Many existing solutions rely on noisy magnetic and inertial sensor data, leading to limited accuracy, while others leverage multiple, dense anchor points to improve the accuracy, requiring significant deployment efforts. This paper presents LiCompass, the first system that enables a commodity camera to accurately estimate the object orientation using just a single optical anchor. Our key idea is to allow a camera to observe varying intensity level of polarized light when it is in different orientations and, hence, perform estimation directly from image pixel intensity. As the estimation relies only on pixel intensity, instead of the location of the anchor in an image, the system performs reliably at long distance, with low resolution images, and with large perspective distortion. LiCompass' core designs include an elaborate optical anchor design and a series of signal processing techniques based on trigonometric properties, which extend the range of orientation estimation to full 360 degrees. Our prototype evaluation shows that LiCompass produces very accurate estimates with median errors of merely 2.5 degrees at 5 meters and 7.4 degrees at 2.5 meters with an irradiance angle of 55 degrees.

Recent studies have demonstrated the potential of light-to-camera communications for realizing applications such as localization, augmented reality and vehicle-to-vehicle communications. However, the fundamental requirement of visible light communications, flicker-free illumination, becomes a key limitation hindering existing technologies from serving a camera at larger distances. To break this limitation, this paper presents POLI, a light-to-camera communication system that exploits a novel POlarized Light Intensity modulation scheme to provide reliable communications for a wide range of distances. The key idea of POLI is to hide the information with the polarization direction of the light, to which human eyes are insensitive. Taking advantage of this, POLI can change the intensity of the polarized light as slowly as possible, at a rate determined by the range the system would support, but does not generate flickers. By using an optical component to "transform polarization directions to colors", POLI allows a camera to leverage its received RGB values as the three spatial dimensions to recover the information carried in different data streams. POLI further incorporates a number of designs to tackle the non-linearity effect of a camera, which is especially critical for an intensity-based modulation scheme. We implemented a prototype using the USRP N200 combined with off-the-shelf optical components. The experimental results show that POLI delivers to its camera receiver a throughput proportional to the dynamic channel conditions. The achievable throughput can be up to 71 bytes per second at short distances, while the service range can be up to 40 meters.

Existing visible light positioning (VLP) systems leverage the high image resolution of a receiving camera to support high positioning accuracy. However, power-hungry cameras might not always be applicable for many scenarios, such as smart factories, in which small objects require to be accurately localized and tracked. In this paper, we introduce CELLI, an indoor VLP system that only uses a single luminary as the transmitter and requires only a simple light sensor to achieve an extremely high accuracy with centimeter-level error. The key idea is to provide the spatial resolution capability from the transmitter instead of the receiver, so that the complexity of the receiver can be minimized. In particular, a small LCD is installed at the transmitter to project a large number of narrow and interference-free polarized light beams to different spatial cells. A receiving light sensor identifies its located cell by detecting the unique polarization-modulated signals projected to that cell. CELLI further incorporates a number of novel designs to overcome the technical challenges such as reducing the positioning latency, which is typically limited by the long optical response time of an LCD, and transforming a cell coordinate to the global 3D position using only a single light. We have prototyped our design using off-the-shelf optical and electrical components, and experimentally shown that CELLI achieves a median 3D positioning error less than 11.8 cm and a median 2D positioning error to less than 2.7 cm.

Indoor positioning enables location-based services for a wide range of commercial applications [4]. Existing visible light positioning (VLP) systems [5, 7] leverage the high image resolution of a receiving camera to support high positioning accuracy. However, power-hungry cameras are not desirable in many scenarios, e.g., smart factories, where small objects need to be accurately located and tracked. In this demo, we introduce CELLI, an indoor VLP system that only uses a single luminary as the transmitter and requires only a simple light sensor to achieve high accuracy with centimeter-level error. The key idea is to provide the spatial resolution capability from the transmitter instead of the receiver, so that the complexity of the receiver can be minimized. In particular, a small Liquid Crystal Display (LCD) is installed at the transmitter to project a large number of narrow and interference-free polarized light beams to different spatial cells. A receiving light sensor identifies its located cell by detecting the unique polarization-modulated signals projected to that cell, as shown in Fig. 1. CELLI further incorporates several novel designs to overcome the technical challenges such as reducing the positioning latency, which is typically limited by the long optical response time of an LCD, and transforming a cell coordinate to the global 3D position using only a single light. We have prototyped our design using off-the-shelf optical and electronic components, and experimentally shown that CELLI achieves a median 3D positioning error less than 12 cm and a median 2D error less than 2.7 cm.

Massive Machine Type Communication (mMTC) has attracted increasing attention due to the explosive growth of IoT devices. Random Access (RA) for a large number of mMTC devices is especially difficult since the high signaling overhead between User Equipments (UEs) and an eNB may overwhelm the available spectrum resources. To address this issue, we propose “respond by hint” (r-Hint), an ID-free handshaking protocol for contention-based RA in mMTC. The core idea of r-Hint is to avoid sequentially notifying contending UEs of their IDs by broadcasting a hint in the RA Response (RAR). To do so, we exploit the concept of prime factorization and hashing to encode the hint such that UEs can extract their required information accordingly. Our simulation results show that r-Hint reduces the RAR message size by 20%-40%. Such reduction can be translated to around 50% improvement of spectrum efficiency in LTE-M.

Device-to-device (D2D) communications is an emerging service model that is currently under standardization by 3GPP. While D2D offloading has a great potential to relieve increasingly congested cellular networks, its benefits, however, come at a cost, namely interference. Most of the prevailing D2D designs conservatively avoid interference via either spectrum resource allocation or power control. These designs, however, do not exploit spatial degrees of freedom (DoF), which are inherently supported by multi-antenna devices. In this work, we present$\sf {MD2D}$, a multiuser D2D system that embraces concurrent D2D transmissions, while leveraging MIMO techniques to actively eliminate interference across D2D pairs.$\sf{MD2D}$has a systematic methodology that checks whether the antenna combination in a D2D network is capable of eliminating cross-pair interference and, thereby, ensuring interference-free concurrent transmissions. If the interference can be eliminated, then$\sf{MD2D}$applies abucket-based DoF assignment algorithmto determine an effective antenna usage configuration that handles the interference. We evaluate our design via testbed experiments and large-scale simulations. The results show that, as compared to the traditional interference avoidance scheme,$\sf{MD2D}$improves the throughput by 87.39 and 218.84 percent in a three-pair testbed and in large-scale simulations, respectively.

Recent advances have demonstrated the potential of network MIMO (netMIMO), which combines a practical number of distributed antennas as a virtual netMIMO AP (nAP) to improve spatial multiplexing of an WLAN. Existing solutions, however, either simply cluster nearby antennas as static nAPs, or dynamically cluster antennas on a per-packet basis so as to maximize the sum rate of the scheduled clients. To strike the balance between the above two extremes, in this paper, we present the design, implementation and evaluation of FlexNEMO, a practical two-phase netMIMO clustering system. Unlike previous per-packet clustering approaches, FlexNEMO only clusters antennas when client distribution and traffic pattern change, as a result being more practical to be implemented. A medium access control protocol customized for uplink transmissions is then designed to allow the clients at the center of nAPs to have a higher probability to gain uplink access opportunities, but still ensure long-term fairness among clients. By combining on-demand clustering and priority-based access control, FlexNEMO not only improves antenna utilization, but also optimizes the channel condition for every individual client. We evaluated our design via both test bed experiments on USRPs and trace-driven emulations. The results demonstrate that FlexNEMO can deliver 94.7% and 93.7% throughput gains over static antenna clustering in a 4-antenna test bed and 16-antenna emulation, respectively.

The feasibility of practical in-band full-duplex radios has recently been demonstrated experimentally. One way to leverage full-duplex in a network setting is to enable three-node full-duplex, where a full-duplex access point (AP) transmits data to one node yet simultaneously receives data from another node. Such three-node full-duplex communication, however, introduces inter-client interference, directly impacting the full-duplex gain. It hence may not always be beneficial to enable three-node full-duplex transmissions. In this paper, we present a distributed full-duplex medium access control (MAC) protocol that allows an AP to adaptively switch between full-duplex and half-duplex modes. We formulate a model that determines the probabilities of full-duplex and half-duplex access so as to maximize the expected network throughput. A MAC protocol is further proposed to enable the AP and clients to contend for either full-duplex or half-duplex transmissions based on their assigned probabilities in a distributed way. Our evaluation shows that, by combining the advantages of centralized probabilistic scheduling and distributed random access, our design improves the overall throughput by 1.53 times, on average, as compared with the greedy downlink-uplink client pairing.

Action recognition via WiFi has caught intense attention recently because of its ubiquity, low cost, and privacy-preserving. Observing Channel State Information (CSI, a fine-grained information computed from the received WiFi signal) resemblance to texture, we transform the received CSI into images, extract features with vision-based methods and train SVM classifiers for action recognition. Our experiments show that regarding CSI as images achieves an accuracy above 85%. Our contributions include:, · To our best knowledge, we are the first to investigate the feasibility of processing CSI by vision-based methods with extendable learning-based framework. · We regard CSI of each Tx-Rx pair as a channel and investigate early and late fusion of multi-channels. · We could know where and what action user performs with location-awareness classification.

Recently, Network Function Virtualization (NFV) has been proposed to transform from network hardware appliances to software middleboxes. Normally, a demand needs to invoke several Virtual Network Functions (VNFs) in a particular order following the service chain along a routing path. In this paper, we study the joint problem of VNF placement and path selection to better utilize the network. We discover that the relation between the link and server usage plays a crucial role in the problem. We first propose a systematic way to elastically tune the proper link and server usage of each demand based on network conditions and demand properties. In particular, we compute a proper routing path length, and decide, for each VNF in the service chain, whether to use additional server resources or to reuse resources provided by existing servers. We then propose a chain deployment algorithm to follow the guidance of this link and server usage. Via simulations, we show that our design effectively adapts resource usage to network dynamics, and, hence, serves more demands than other heuristics.

Recent advances have demonstrated the potential of network MIMO (netMIMO), which combines a practical number of distributed antennas as a virtual netMIMO AP (nAP) to improve spatial multiplexing of an WLAN. Existing solutions, however, either simply cluster nearby antennas as static nAPs, or dynamically cluster antennas on a per-packet basis so as to maximize the sum rate of the scheduled clients. To strike the balance between the above two extremes, in this paper, we present the design, implementation and evaluation of FlexNEMO, a practical two-phase netMIMO clustering system. Unlike previous per-packet clustering approaches, FlexNEMO only clusters antennas when client distribution and traffic pattern change, as a result being more practical to be implemented. A medium access control protocol is then designed to allow the clients at the center of nAPs to have a higher probability to gain access opportunities, but still ensure long-term fairness among clients. By combining on-demand clustering and priority-based access control, FlexNEMO not only improves antenna utilization, but also optimizes the channel condition for every individual client. We evaluated our design via both testbed experiments on USRPs and trace-driven emulations. The results demonstrate that FlexNEMO can deliver 94.7% and 93.7% throughput gains over static antenna clustering in a 4-antenna testbed and 16-antenna emulation, respectively.

Due to the characteristics of ubiquity, non-occlusion, privacy preservation of WiFi, many researchers have devoted to human action recognition using WiFi signals. As demonstrated in [1], Channel State Information (CSI), a fine-grained information capturing the properties of WiFi signal propagation, could be transformed into images for achieving a promising accuracy on action recognition via vision-based methods. However, from the experimental results shown in [1], the CSI is usually location dependent, which affects the recognition performance if signals are recorded in different places.

A full-duplex radio can transmit and receive simultaneously, and, hence, is a natural fit for realizing an in-band relay system. Most of existing full-duplex relay designs, however, simply forward an amplified version of the received signal without decoding it, and, thereby, also amplify the noise at the relay, offsetting throughput gains of full-duplex relaying. To overcome this issue, we explore an alternative: demodulate-and-forward. This paper presents the design and implementation of DelayForward (DF), a practical system that fully extracts the relay gains of full-duplex demodulate-and-forward mechanism. DF allows a relay to remove its noise from the signal it receives via demodulation and forward the clean signal to destination with a small delay. While such delay-and-forward mechanism avoids forwarding the noise at the relay, the half-duplex destination, however, now receives the combination of the direct signal from a source and the delayed signal from a relay. Unlike previous theoretical work, which mainly focuses on deriving the capacity of demodulate-and-forward relaying, we observe that such combined signals have a structure similar to the convolutional code, and, hence, propose a novel viterbi-type decoder to recover data from those combined signals in practice. Another challenge is that the performance of full-duplex relay is inherently bounded by the minimum of the relay's SNR and the destination's SNR. To break this limitation, we further develop a power allocation scheme to optimize the capacity of DF. We have built a prototype of DF using USRP software radios. Experimental results show that our power-adaptive DF delivers the throughput gain of 1.25×, on average, over the state-of-the-art full-duplex relay design. The gain is as high as 2.03× for the more challenged clients.

In many applications, it is a basic operation for the sink to periodically collect reports from all sensors. Since the data gathering process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs for maintaining the routing tables of sensors. In this paper, we work on the problem of constructing a data aggregation tree that minimizes the total energy cost of data transmission in a wireless sensor network. In addition, we also address such a problem in the wireless sensor network where relay nodes exist and consider the cases where the link quality is not perfect. We show that these problems are NP-complete and propose$O(1)$-approximation algorithms for each of them. Simulations show that the proposed algorithms have good performance in terms of energy cost.

While MIMO technologies are rapidly adopted in 802.11, mobile devices increasingly have different numbers of antennas. Several multiuser MIMO (MU-MIMO) MAC protocols have recently been proposed to allow concurrent transmissions across different links. Though those protocols better utilize the available degrees of freedom, they however do not provide each data stream any receive diversity. This paper introduces multiplexing-diversity medium access (MDMA), a distributed MU-MIMO MAC protocol that achieves both the multiplexing and receive diversity gains at the same time. Instead of letting a node pair use its full degrees of freedom, MDMA allows as many contending node pairs as possible to transmit concurrently and share all the available degrees of freedom. By doing this, MDMA exploits more antennas equipped at different receiving nodes to provide concurrent streams more receive diversity, without sacrificing the multiplexing gain. We show via testbed experiments and simulations that MDMA achieves a similar multiplexing gain, but extracts more diversity gains as the number of antennas at any nodes increases. It hence improves the throughput by up to 48.8 percent as compared to the protocol enabling only spatial multiplexing.

Unlike conventional omnidirectional antennas, switched beam antennas exploit antenna arrays and signal processing techniques to focus energy in a specific beam-width and orientation. Recent research has shown that access points of a WLAN can exploit such switched beam antennas to increase the overall network capacity. The achievable sum rate of a WLAN with switch beam antennas is however mainly determined by how each AP selects its beam, including the orientation and width, and how each client associates with a proper AP. The goal of this paper is to solve the Joint Beam configuration and Client association (JBC) problem such that the sum rate of all clients in the network can be maximized. We formulate the JBC problem as a mixed integer linear programming model, and propose a 2-approximation algorithm to solve it. Our proposed algorithm has two distinctive properties: 1) it can be realized as a distributed protocol that allows the APs to configure their beams without the help of a central coordinator, and 2) it can be generally applied both in specific scenarios, where exact client locations are known, and in uncertain scenarios, where only geographic client distribution is given. Finally, we adjust the sum-rate maximization algorithm to the throughput maximization algorithm, which further takes medium sharing among clients into account. The simulation results show that the proposed algorithm outperforms both WLANs using omnidirectional antennas and other heuristics using switched beam antennas.

While recent works on multiuser MIMO (MU-MIMO) mainly focus on boosting the throughput by enabling concurrent transmissions, less attention is paid to recovering concurrent erroneous streams. We however notice that uplink MU-MIMO is especially vulnerable to errors because error in any stream corrupts most of the other concurrent streams. Even worse, carrier sense and rate adaptation become more challenging in MU-MIMO, which further decreases reliability. Existing systems however recover errors by retransmitting all the streams in a corrupted packet, thereby taking away the significant performance benefit of MU-MIMO. To effectively harness the ideal gain of MU-MIMO, we develop Concurrent Packet Recovery (CPR), a recovery protocol customized for MU-MIMO. It has two distinctive features: (i) It judiciously selects the minimum number of streams to be retransmitted to support successful decoding; (ii) during retransmission, it utilizes the full degrees of freedom by allowing new streams to be sent in parallel. Our evaluation via testbed experiments and trace-driven simulations shows that CPR can efficiently recover both normal losses and collisions. For three-antenna AP scenarios, the throughput gain is up to 31.1 percent when no hidden terminal exists, and is 3.16× when 20 percent pairs of contending clients are hidden terminals.

Abstract In this paper, we investigate the problem of joint sink deployment and association (JSDA) in multi‐sink wireless camera networks (WCNs). Each camera in a WCN requires a different streaming rate of delivering its stream back to an access point (sink) because of various surveillance requirements, for example, event detection or target tracking. In a WCN where multiple channels are supported, the sinks must be placed in suitable locations (sink deployment) to collect streams from cameras over nonoverlapping channels, and in addition, each camera must associate with the appropriate sink (sink association) so that its demand rate can be optimally satisfied. To achieve this goal, we first formulate the JSDA problem as an optimization model using mixed‐integer linear programming and prove its NP‐completeness. Two approaches, branch‐and‐bound and our heuristics, iterative sink deployment and association (ISDA), are then developed to solve JSDA. We evaluate the performance via simulations with the traces collected by real measurements and show that ISDA can effectively satisfy cameras' demands with a reasonable computational cost. Copyright © 2014 John Wiley & Sons, Ltd.

The traditional IEEE 802.11 network is designed for the use of small scale local wireless networks such as home or campus WLANs, where one access point serves a reasonably few number of devices like smartphones, laptops, tablets, and so on. However, the emergence of the Internet of Things (IoT) have changed the scene of wireless communications, while the number of devices rapidly increases and becomes far larger than before. Thus, recently, the IEEE task group ah (TGah) is dedicated to the standardization of a new protocol, called IEEE 802.11ah, which is customized for this type of large-scale networks. IEEE 802.11ah adopts the grouping-based MAC protocol to reduce the contention overhead of each group of devices. However, most existing designs simply randomly assign devices to groups, and less attention has been paid to the problem of forming efficient groups. Therefore, in this paper, we argue that the performance of grouping is closely related to the heterogeneous traffic demands of devices, and propose a load-balanced grouping algorithm to improve channel utilization of each group. Our evaluation shows that the proposed load-balanced grouping outperforms simple random grouping, especially when the network is almost saturated.

The feasibility of practical in-band full-duplex radios has recently been demonstrated experimentally. One way to leverage full-duplex in a network setting is to enable three-node full-duplex, where a full-duplex access point (AP) transmits data to one node yet simultaneously receives data from another node. Such three-node full-duplex communication however introduces inter-client interference, directly impacting the full-duplex gain. It hence may not always be beneficial to enable three-node full-duplex transmissions. In this paper, we present a distributed full-duplex medium access control (MAC) protocol that allows an AP to adaptively switch between full-duplex and half-duplex modes. We formulate a model that determines the probabilities of full-duplex and half-duplex access so as to maximize the expected network throughput. A MAC protocol is further proposed to enable the AP and clients to contend for either full-duplex or half-duplex transmissions based on their assigned probabilities in a distributed way. Our evaluation shows that, by combining the advantages of centralized probabilistic scheduling and distributed random access, our design improves the overall throughput by 3.16× and 1.44×, on average, as compared to half-duplex 802.11 and greedy downlink-uplink client pairing.

In a Wireless Local Area Network (WLAN), clients typically associate with the AP that offers the maximal signal strength. Later works on client-AP association then further take load balancing and fairness into consideration. Those schemes however are not directly applicable in a multiuser MIMO (MU-MIMO) WLAN since different combinations of clients result in different throughput of each individual client. Therefore, in this paper, we present a client-AP association algorithm customized for MU-MIMO WLANs. The proposed algorithm jointly solves the problems of client-AP association and MU-MIMO client grouping with consideration of channel correlation among clients. It hence allows a good group of clients, i.e., those with low channel correlation, to together associate with the same proper AP and achieve a high sum-rate. The simulation results show that our MU-MIMO AP association algorithm improves the aggregate throughput by about 11%-28% and 26%-45%, as compared to two common association approaches, i.e., RSSI-based and load-based schemes, respectively.

Dynamic Adaptive Streaming over HTTP (DASH) has become an emerging application nowadays. Video rate adaptation is a key to determine the video quality of HTTP-based media streaming. Recent works have proposed several algorithms that allow a DASH client to adapt its video encoding rate to network dynamics. While network conditions are typically affected by many different factors, these algorithms however usually consider only a few representative information, e.g., predicted available bandwidth or fullness of its playback buffer. In addition, the error in bandwidth estimation could significantly degrade their performance. Therefore, this paper presents Machine Learning-based Adaptive Streaming over HTTP (MLASH), an elastic framework that exploits a wide range of useful network-related features to train a rate classification model. The distinct properties of MLASH are that its machine learning-based framework can be incorporated with any existing adaptation algorithm and utilize big data characteristics to improve prediction accuracy. We show via trace-based simulations that machine learning-based adaptation can achieve a better performance than traditional adaptation algorithms in terms of their target quality of experience (QoE) metrics.

Transmission failures are common in wireless networks due to dynamic channel conditions and unpredictable interference. To efficiently recover from failures, we proposea smart retransmission scheme where the receiver combines information received from multiple failed transmissions associated with the same frame. The smart retransmission has two distinguishing features: (i) it can simultaneously supportpartial retransmission and combines bits with low confidence, and (ii) it has the first combining-aware rate adaptation scheme, which selects the data rates for all transmissions associated withthe same frame to maximize overall throughput. We find thatcombining-aware rate adaptation is essential to harnessing thecombining gain. Using trace-driven simulation and USRP testbedexperiments, we demonstrate the feasibility and effectiveness ofour approach, and show it significantly out-performs the existingschemes, such as WiFi, partial packet recovery (PPR), andSOFT in terms of both throughput and energy.

Multi-user multiple input and multiple output (MU-MIMO) is one predominate approach to improve the wireless capacity. However, since the aggregate capacity of MU-MIMO heavily depends on the channel correlations among the mobile users in a beamforming group, unwisely selecting beamforming groups may result in reduced overall capacity, instead of increasing it. How to select users into a beamforming group becomes the bottleneck of realizing the MU-MIMO gain. The fundamental challenge for user selection is the large searching space, and hence there exists a tradeoff between search complexity and achievable capacity. Previous works have proposed several low complexity heuristic algorithms, but they suffer a significant capacity loss. In this paper, we present a novel MU-MIMO MAC, called SIEVE. The core of SIEVE design is its scalable multi-user selection module that provides a knob to control the aggressiveness in searching the best beamforming group. SIEVE maintains a central database to track the channel and the coherence time for each mobile user, and largely avoids unnecessary computing with a progressive update strategy. Our evaluation, via both small-scale testbed experiments and large-scale trace-driven simulations, shows that SIEVE can achieve around 90% of the capacity compared to exhaustive search.

Multi-user MIMO (MU-MIMO) has recently been specified in wireless standards, e.g., LTE-Advance and 802.11ac, to allow an access point (AP) to transmit multiple unicast streams simultaneously to different clients. These protocols however have no specific mechanism for multicasting. Existing systems hence simply allow a single multicast transmission, as a result underutilizing the AP's multiple antennas. Even worse, in most of systems, multicast is by default sent at the base rate, wasting a considerable link margin available for delivering extra information. To address this inefficiency, we present the design and implementation of HybridCast, a MU-MIMO system that enables joint unicast and multicast. HybridCast efficiently leverages the unused MIMO capability and link margin to send unicast streams concurrently with a multicast session, while ensuring not to harm the achievable rate of multicasting. We evaluate the performance of HybridCast via both testbed experiments and simulations. The results show that HybridCast always outperforms single multicast transmission. The average throughput gain for 4-antenna AP scenarios is 6.22× and 1.54× when multicast is sent at the base rate and the best rate of the bottleneck receiver, respectively.

Recent literatures have demonstrated the feasibility and applicability of light-to-camera communications. They either use this new technology to realize specific applications, e.g., localization, by sending repetitive signal patterns, or consider non-line-of-sight scenarios. We however notice that line-of-sight light-to-camera communications has a great potential because it provides a natural way to enable visual association, i.e., visually associating the received information with the transmitter's identity. Such capability benefits broader applications, such as augmented reality, advertising, and driver assistance systems. Hence, this paper designs, implements, and evaluates RollingLight, a line-of-sight light-to-camera communication system that enables a light to talk to diverse off-the-shelf rolling shutter cameras. To boost the data rate and enhance reliability, RollingLight addresses the following practical challenges. First, its demodulation algorithm allows cameras with heterogeneous sampling rates to accurately decode high-order frequency modulation in real-time. Second, it incorporates a number of designs to resolve the issues caused by inherently unsynchronized light-to-camera channels. We have built a prototype of RollingLight with USRP-N200, and also implemented a real system with Arduino Mega 2560, both tested with a range of different camera receivers. We also implement a real iOS application to examine our real-time decoding capability. The experimental results show that, even to serve commodity cameras with a large variety of frame rates, RollingLight can still deliver a throughput of 11.32 bytes per second.

In this paper, we investigate the wireless network deployment problem, which seeks the best deployment of a given limited number of wireless routers. We find that many goals for network deployment, such as maximizing the number of covered users, the size of the coverage area, or the total throughput of the network, can be modeled with a submodular set function. Specifically, given a set of routers, the goal is to find a set of locations S, each of which is equipped with a router, such that S maximizes a predefined submodular set function. However, this deployment problem is more difficult than the traditional maximum submodular set function problem, e.g., the maximum coverage problem, because it requires all the deployed routers to form a connected network. In addition, deploying a router in different locations might consume different costs. To address these challenges, this paper introduces two approximation algorithms, one for homogeneous deployment cost scenarios and the other for heterogeneous deployment cost scenarios. Our simulations, using synthetic data and real traces of census in Taipei, Taiwan, show that the proposed algorithms achieve better performances than other heuristics.

As mobile devices become more ubiquitous, the amount of cellular traffic for multimedia content grows explosively. Therefore, data dissemination through proximity-based opportunistic communications attracts the attention of service providers who are eager for solutions of traffic offloading. In this paper, we propose PrefCast, a preference-aware opportunistic content dissemination protocol that uses as few cellular bandwidth as possible to maximally satisfy user preferences for content objects. The efficiency of PrefCast depends on 1) how does the base-station select initial sources, and 2) how does each user forward objects within a limited contact duration. Since mobile users typically form communities and have heterogeneous preferences, PrefCast's base-station selects sources that efficiently produce the maximal utility to their communities. We then derive a model to predict how much utility a forwarder can contribute to future contacts. PrefCast's users can hence use such prediction to find their optimal forwarding schedule, which maximizes the utility contribution, in a distributed way. Our trace-based evaluation shows that, without explicit source selection, PrefCast produces a 15.7 and 22.6 percent higher average utility than the protocols that only consider contact frequency or preference of local contacts, respectively. Enabling source selection in PrefCast further improves the utility by 49.3 percent.

Long Term Evolution-Advanced (LTE-A) networks exploit the Carrier Aggregation (CA) technique to achieve a higher data rate by allowing user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs). Moreover, MIMO technologies have become increasingly mature and been adopted as a default choice of the 4G standards. However, most of existing studies on resource allocation with carrier aggregation do not consider the MIMO capability of UEs. In this paper, we address the spectrum resource allocation problem with consideration of UEs' MIMO capability as well as modulation and coding schemes (MCSs) selection in carrier aggregation based LTE-A systems. We formulate the problem under both backlogged and finite queue traffic models as an optimization model, and prove its NP-hardness. As a result, a 1/2-approximation algorithm is proposed to find a suboptimal solution of resource allocation. Simulation results show that the proposed algorithm outperforms the existing schemes, and performs fairly close to the optimal solution under the small-scale scenarios.

How to effectively exchange information between parties in a disaster management system is one of the fundamental challenges to support timely and efficient disaster response and relief. Specifically, the timeliness, scalability, and availability are three desirable features for information exchange. We call the framework to support information exchange with the three features an Open Information Gateway (OIGY). In this paper, we present and experiment the mechanisms to recover communicaiton service during and after disasters for Open Information Gateway. The designed mechanisms adopt long distance radio to support tele-communication and computer networks, dynamic power adjustment for radio stations to maximize the radio recovery with minimal interference, and dynamic MESH routers to connect devices located in different networks. We experimented the proposed mechanims on a physical communication testbeds to measure the delay for service recovery and reliability of the proposed mechanism.

Mobile ad hoc networks (MANETs) are ideal for situations where a fixed infrastructure is unavailable or infeasible. Today's MANETs, however, may suffer from network partitioning. This limitation makes MANETs unsuitable for applications such as crisis management and battlefield communications, in which team members might need to work in groups scattered in the application terrain. In such applications, intergroup communication is crucial to the team collaboration. To address this weakness, we introduce in this paper a new class of ad-hoc network called Autonomous Mobile Mesh Network (AMMNET). Unlike conventional mesh networks, the mobile mesh nodes of an AMMNET are capable of following the mesh clients in the application terrain, and organizing themselves into a suitable network topology to ensure good connectivity for both intra- and intergroup communications. We propose a distributed client tracking solution to deal with the dynamic nature of client mobility, and present techniques for dynamic topology adaptation in accordance with the mobility pattern of the clients. Our simulation results indicate that AMMNET is robust against network partitioning and capable of providing high relay throughput for the mobile clients.

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in an MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: Its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7× and 2.3×, for 2-antenna and 3-antenna APs, respectively.

In multiuser MIMO (MU-MIMO) LANs, the achievable throughput of a client depends on who is transmitting concurrently with it. Existing MU-MIMO MAC protocols, however, enable clients to use the traditional 802.11 contention to contend for concurrent transmission opportunities on the uplink. Such a contention-based protocol not only wastes lots of channel time on multiple rounds of contention but also fails to maximally deliver the gain of MU-MIMO because users randomly join concurrent transmissions without considering their channel characteristics. To address such inefficiency, this paper introduces MIMOMate, a leader-contention-based MU-MIMO MAC protocol that matches clients as concurrent transmitters according to their channel characteristics to maximally deliver the MU-MIMO gain while ensuring all users fairly share concurrent transmission opportunities. Furthermore, MIMOMate elects the leader of the matched users to contend for transmission opportunities using traditional 802.11 CSMA/CA. It hence requires only a single contention overhead for concurrent streams and can be compatible with legacy 802.11 devices. A prototype implementation in USRP N200 shows that MIMOMate achieves an average throughput gain of 1.42× and $1.52× over the traditional contention-based protocol for two- and three-antenna AP scenarios, respectively, and also provides fairness for clients.

In this paper, we investigate the wireless network deployment problem, which seeks the best deployment of a given limited number of wireless routers. We found that many goals for network deployment, such as maximizing the number of covered users or areas, or the total throughput of the network, can be modelled with the submodular set function. Specifically, given a set of routers, the goal is to find a set of locations S, each of which is equipped with a router, such that S maximizes a predefined submodular set function. However, this deployment problem is more difficult than the traditional maximum submodular set function problem, e.g., the maximum coverage problem, because it requires all the deployed routers to form a connected network. In addition, deploying a router in different locations might consume different costs. To address these challenges, this paper introduces two approximation algorithms, one for homogeneous deployment cost scenarios and the other for heterogeneous deployment cost scenarios. Our simulations, using synthetic data and real traces of census in Taipei, show that the proposed algorithms achieve a better performance than other heuristics.

In existing multiuser MIMO (MU-MIMO) MAC protocols, a multi-antenna node sends as many concurrent streams as possible once it wins the contention. Though such a scheme allows nodes to utilize the multiplex gain of a MIMO system, it however fails to leverage receive diversity gains provided by multiple receive antennas across nodes. We introduce Multiplex-Diversity Medium Access (MDMA), a MU-MIMO MAC protocol that achieves both the multiplex gain and the receive diversity gain at the same time. Instead of letting a node pair use all the available degrees of freedom, MDMA allows as many contending node pairs to communicate concurrently as possible and share all the degrees of freedom. It hence can exploit the antennas equipped on different receivers to further provide some of concurrent streams more receive diversity, without losing the achievable multiplex gain. We implement a prototype on software radios to demonstrate the throughput gain of MDMA.

Exchanging the channel state information (CSI) in a multiuser WLAN is considered an extremely expensive overhead. A possible solution to reduce the overhead is to notify the analog value of the CSI, which is also known as analog channel feedback. It however only allows nodes to overhear an imperfect channel information. While some previous studies have theoretically analyzed the performance of analog channel feedback, this work aims at addressing issues of realizing it in practice and empirically demonstrating its effectiveness. Our prototype implementation using USRP-N200 shows that analog channel feedback produces a small error comparable to that of estimating CSI using reciprocity, but however can be applied to more general scenarios.

Flexible channels enable Access Points (AP) and clients to determine channel widths and their center frequencies. The key challenge in flexible channels is how to determine proper channel width and center frequency for each link demands such that the overall system throughput could be improved. In this work, we introduce a distributed flexible channel assignment algorithm, DFCA. Different from prior researches, DFCA does link-based channel assignment instead of AP-based or packet-based. This is because that the packet-based method could incur heavy overhead, while the AP-based method is not able to determine the interfering relation between links precisely. In addition, the transmission weight of a link is proposed; with the help of the transmission weight, channel width, center frequency and interference relation to neighboring nodes, DFCA is able to accurately predict that link throughput prior to the channel assignment. Results show that DFCA could outperform traditional fixed channel assignment approaches and the AP-based flexible channel assignment algorithm. Even compared with FLUID[1], which is a packet-based channel assignment approach with assist of a central controller, DFCA is able to achieve comparable (at least 93%) throughput by using only local information rather than global knowledge.

Video multicast exploits the wireless broadcast nature to transmit a video stream to multiple clients with a minimum bandwidth requirement. Assigning a suitable transmission bit-rate to each scalable coded block in a video stream is however a challenging problem because clients in a wireless network have heterogeneous channel quality and experience different packet loss probability. Prior work attempts to transmit the base-layer stream at a low transmission bit-rate to ensure a high reception probability, and hence the basic visual quality. Such methods however are over-simplified for a video stream that supports multiple quality levels in a video frame and needs explicit rate assignment for each block. We propose in this paper a dependency-aware rate scheduling scheme that assigns each block a rate according to dependency between blocks. With consideration of block dependency, we can better utilize limited wireless bandwidth to deliver important blocks, and minimize the number of undecodable blocks due to the loss of their reference blocks at the receivers. The simulation results show that since our scheme reduces the number of undecodable blocks, it achieves a higher overall video quality for a multicast group than the existing schemes under different client distributions.

Adaptation of modulation and transmission bit-rates for video multicast in a multirate wireless network is a challenging problem because of network dynamics, variable video bit-rates, and heterogeneous clients who may expect differentiated video qualities. Prior work on the leader-based schemes selects the transmission bit-rate that provides reliable transmission for the node that experiences the worst channel condition. However, this may penalize other nodes that can achieve a higher throughput by receiving at a higher rate. In this work, we investigate a rate-adaptive video multicast scheme that can provide heterogeneous clients differentiated visual qualities matching their channel conditions. We first propose a rate scheduling model that selects the optimal transmission bit-rate for each video frame to maximize the total visual quality for a multicast group subject to the minimum-visual-quality-guaranteed constraint. We then present a practical and easy-to-implement protocol, called QDM, which constructs a cluster-based structure to characterize node heterogeneity and adapts the transmission bit-rate to network dynamics based on video quality perceived by the representative cluster heads. Since QDM selects the rate by a sample-based technique, it is suitable for real-time streaming even without any preprocess. We show that QDM can adapt to network dynamics and variable video-bit rates efficiently, and produce a gain of 2-5 dB in terms of the average video quality as compared to the leader-based approach.

Since data in underwater sensor networks (UWSNs) is transmitted by acoustic signals, the characteristics of a UWSN are different from those of a terrestrial sensor network. Specifically, due to the high propagation delay of acoustic signals in UWSNs, referred as spatial-temporal uncertainty, current terrestrial MAC schemes do not work well in UWSNs. Hence, we consider spatial-temporal uncertainty in the design of an energy-efficient TDMA-based MAC protocol for UWSNs. We first translate the TDMA-based scheduling problem in UWSNs into a special vertex-coloring problem in the context of a spatial-temporal conflict graph (ST-CG) that describes explicitly the conflict delays among transmission links. With the help of the ST-CG, we propose two novel heuristic approaches: 1) the traffic-based one-step trial approach (TOTA) to solve the coloring problem in a centralized fashion; and for scalability, 2) the distributed traffic-based one-step trial approach (DTOTA) to assign the data schedule for tree-based routing structures in a distributed manner. In addition, a mixed integer linear programming (MILP) model is derived to obtain a theoretical bound for the TDMA-based scheduling problem in UWSNs. Finally, a comprehensive performance study is presented, showing that both TOTA and DTOTA guarantee collision-free transmission. They thus outperform existing MAC schemes such as S-MAC, ECDiG, and T-Lohi in terms of network throughput and energy consumption.

ABSTRACT The utilization of limited energy in wireless sensor networks (WSNs) is the critical concern, whereas the effectiveness of routing mechanisms substantially influence energy usage. We notice that two common issues in existing specific routing schemes for WSNs are that (i) a path may traverse through a specific set of sensors, draining out their energy quickly and (ii) packet retransmissions over unreliable links may consume energy significantly. In this paper, we develop an energy‐efficient routing scheme (called EFFORT) to maximize the amount of data gathered in WSNs before the end of network lifetime. By exploiting two natural advantages of opportunistic routing, that is, the path diversity and the improvement of transmission reliability, we propose a new metric that enables each sensor to determine a suitable set of forwarders as well as their relay priorities. We then present EFFORT, a routing protocol that utilizes energy efficiently and prolongs network lifetime based on the proposed routing metric. Simulation results show that EFFORT significantly outperforms other routing protocols. Copyright © 2011 John Wiley & Sons, Ltd.

How to exchange information between parties in a mega-scale disaster management system is one of the fundamental challenges to support timely and efficient disaster response and relief. Specifically, the timeliness, scalability, and availability are three desirable features for information exchange. We call the framework to support information exchange with the three features an open information gateway, OIGY in short. In this paper, we present the challenges of information gateway, and the design of the communication protocols and the fundamental components and algorithms to support the aforementioned features. The efforts of this work will be divided into two major components: one is the distributed Truthful Real-time Information Publishing and Subscribing (TRIPS), and the other one is Heterogeneous And Plug-n-PlaY networks (HAPPY). The two components in OIGY collaborate to provide reliable and timely information publish and subscription service. TRIPS is responsible for logical information exchange management. Compared to modern real-time publish and subscription services, TRIPS is aimed at information responsiveness and distributed content-based filtering in an un-reliable network. To achieve better responsiveness, TRIPS will take advantage of the run-time service composition of SOA framework to select information sources. To enhance the success rate, TRIPS relies the information routing information provided by HAPPY. HAPPY will integrate heterogeneous communication networks including 3G/WiMAX telecommunication network and mesh mobile network into a coherent communication network and discovers the routes with probabilistic bandwidth guarantee.

As mobile devices have become more ubiquitous, mobile users increasingly expect to utilize proximity-based connectivity, e.g., WiFi and Bluetooth, to opportunistically share multimedia content based on their personal preferences. However, many previous studies investigate content dissemination protocols that distribute a single object to as many users in an opportunistic mobile social network as possible without considering user preference. In this paper, we propose PrefCast, a preference-aware content dissemination protocol that targets on maximally satisfying user preference for content objects. Due to non-persistent connectivity between users in a mobile social network, when a user meets neighboring users for a limited contact duration, it needs to efficiently disseminate a suitable set of objects that can bring possible future contacts a high utility (the quantitative metric of preference satisfaction). We formulate such a problem as a maximum-utility forwarding model, and propose an algorithm that enables each user to predict how much utility it can contribute to future contacts and solve its optimal forwarding schedule in a distributed manner. Our trace-based evaluation shows that PrefCast can produce a 18.5% and 25.2% higher average utility than the protocols that only consider contact frequency or preference of local contacts, respectively.

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in a MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7x and 2.3x, for 2-antenna and 3-antenna APs respectively.

With the growing demands for accessing mobile applications, the cellular network is currently overloaded. Recent work has proposed to exploit opportunistic networks to offload cellular traffic for mobile content dissemination services. The basic idea is to distribute the content object to only part of subscribers (called initial sources) via the cellular network, and allow initial sources to propagate the object through opportunistic communications. The preliminary study focused on selecting a given number of initial sources only based on the probability of encounters between users. However, without consideration of social relationships between users, the selected sources might not be able to propagate the object across different social communities opportunistically. In addition, there exists a dilemma of selecting a suitable number of sources to take the trade-off between offloading cellular traffic and reducing the latency. Hence, in this paper, we propose community-based opportunistic dissemination, which automatically selects a sufficient number of initial sources to propagate the object across disjointed communities in parallel. The trace-based evaluation shows that, compared to encounter-based dissemination, our community-based scheme improves the amount of offloaded cellular traffic up to 29%. In addition, users experience a significantly shorter latency.

Real traffic replay is one of the solutions used to test network devices over complicated scenarios. Packet traces captured in a real environment hold more details than any mathematical models. However, the lack of packet-replay control and environment emulation might highly affect traffic behaviors, especially in wireless networks. Real traffic replay in wireless networks requires packet-replay control to manage the interactions with the device under test (DUT), and coordinately reproduces environment effects, such as fading, noise, and interference. In this work, we propose a method, called Event-driven Automata-synchronized Replay (EAR), to address real traffic replay over WLAN. EAR transforms the captured packet trace into a sequence of events that follow the IEEE 802.11 protocol. The three-level automata are applied to achieve packet-replay control and synchronize the environment effects in traffic replay with the packets and signals captured in a real environment. We propose a quantitative metric, called the event reproduction ratio (ERR), to evaluate the effectiveness of traffic replay. Our software implementation on the Linux-based system demonstrates that EAR achieves the ERR of 95.9% and 92.45% over the DUT-dependent traffic and fading environments, respectively. Under the same condition, the straight-forward replay can only produce the ERR of 20.6% and 0%, respectively.

As mobile devices, such as smart phones and tablet PCs, become more and more ubiquitous, it is increasingly popular for users to watch online video streaming on their mobile devices. Because of the nature of wireless broadcast, a wireless router (e.g., an access point or cellular tower) can retrieve video streams from the video server and then efficiently multicast them via wireless medium to multiple clients who are interested in those videos. However, clients could locate in different positions and therefore experience various wireless channel conditions. In addition, clients might be interested in different video content so that different video clips could have various popularity. The heterogeneity in wireless conditions and user interests makes bandwidth allocation in a wireless router for video multicast a challenging problem. In this paper, we propose a marginal-utility-based video multicast scheme that takes variant channel qualities and heterogeneous user interests into account. Our evaluation shows that, compared to interest-oblivious multicast, the proposed interest-aware approach can improve the average visual quality by up to 9 dB.

The wireless broadcast nature enables a video source to multicast a stream to a group of mobile clients efficiently. However, since different clients might experience heterogeneous wireless channel conditions, the throughput of single-hop multicast schemes is bounded by the clients that experience a poor channel condition. This paper presents a relay-based video multicast scheme that allows the clients experiencing a better channel condition to select a suitable transmission bit-rate and relay data for neighboring poorly-connected clients. We first formulate a joint time-allocation and rate-selection model that allocates bandwidth resources to each relay node, and then propose a network-coding-based forwarding scheme to determine which video frames should be forwarded by each relay. Our evaluation results show that the proposed relay-based scheme can utilize channel bandwidth more efficiently, and, therefore, improve the average visual quality by up to 5dB as compared to single-hop multicast.

Peer-to-Peer (P2P) storage systems are cost-effective and reliable platforms that enable users to share their storage to support variant emerging applications, such as peer-to-peer social networks and distributed backup systems. Because different users have heterogeneous online characteristics and bandwidth capabilities, how to replicate data at suitable peers has become an important issue to ensure that users can access any replica with a high probability. Previous work on data replication in a P2P storage system only considers online characteristic of each user, and aims at increasing data availability. However, we notice that, without considering the bandwidth capability of each user, a system might replicate popular data at a user who has a long online duration but does not have enough bandwidth capability to support all the requests. Therefore, in this work, we propose a swap-based replication scheme that jointly considers online characteristic, data popularity and bandwidth capability to improve not only data availability, but also access probability for each data item.

Because of marketing demand of user-centric video dissemination, promoting video content via interactive distribution platforms has received considerable attention recently. In addition, the preferences of clients are heterogenous and dynamic, effectively utilizing the network bandwidth to distribute user-centric content becomes a challenging problem, especially when the number of users in the systems scale up. Therefore, we propose a P2P-based distribution platform, called Bulldozer, which enable clients to cooperatively propagate user-centric video content in a large scale environment, and better utilizes peers upload bandwidth to share the server traffic load. Bulldozer consists of two components: 1) Clip Scheduling phase schedules the playing order of videos for each individual user, and 2) Bandwidth Allocation phase allocates users upload bandwidths to distribute videos among clients. Finally, simulation results show that Bulldozer can effectively reduce the peak traffic load of the server.

Hybrid coordination function (HCF) is proposed in IEEE 802.11e standard to support quality of service (QoS). HCF defines two channel access mechanisms; one of them is HCF controlled channel access (HCCA) designed for contention free transmission. In HCCA, each QoS-enhanced station (QSTA) obtains polled transmission opportunity (TXOP) from the hybrid coordinator (HC), which is in charge of scheduling the channel access and responding the poll messages to each QSTA. However, forwarding polling messages causes a significant overhead and degrades the channel utilization of data transmission. The goal of this work is to develop an efficient multipolling scheme that can reduce the overhead of polling messages. The simulation results show that the proposed algorithm can reduce the control overhead and, thus, improve the channel utilization significantly.

This paper presents the design and implementation of 802.11n+, a fully distributed random access protocol for MIMO networks. 802.11n+ allows nodes that differ in the number of antennas to contend not just for time, but also for the degrees of freedom provided by multiple antennas. We show that even when the medium is already occupied by some nodes, nodes with more antennas can transmit concurrently without harming the ongoing transmissions. Furthermore, such nodes can contend for the medium in a fully distributed way. Our testbed evaluation shows that even for a small network with three competing node pairs, the resulting system about doubles the average network throughput. It also maintains the random access nature of today's 802.11n networks.

In this paper, we investigate the Access Point (AP) deployment problem in Wireless Surveillance Systems (WSSs). Cameras in a WSS could have various demanding upload streaming rates to the AP (usually deemed as a sink or data collector) according to their surveillance requirements, e.g. object detection frequency, video resolution, or the successful ratio of object tracking. Therefore, a suitable AP location must be determined in order to satisfy each camera's demand. We first provide a mathematical formulation that defines the best location of the AP, and then propose a novel mechanism, called Spring-Cam, to exploit the concept of mass-spring systems in Physics to locate the suitable AP location with consideration of camera demands as well as transmission quality of the link between each camera and the AP. Simulation results show that Spring-Cam can use only a limited measurement overhead to find an AP location that achieves a performance comparable with exhausted searching the best location.

Lifetime-maximization is the critical concern for wireless sensor networks (WSNs). We notice that two common issues in existing routing schemes for WSNs are that (1) a path may traverse through a fixed set of sensors, draining out their energy quickly, and (2) packet retransmissions over an unreliable link of any fixed-path may consume energy significantly. In this paper, we exploit two natural advantages of opportunistic routing, i.e., path diversity and the improvement of transmission reliability, to develop a distributed routing scheme (called EFFORT) for prolonging the network-lifetime of a WSN. Specifically, a new metric is proposed to assist each sensor in determining a suitable set of forwarders as well as their priorities and, thus, enables EFFORT to extend the network-lifetime. Simulation results show that EFFORT effectively achieves network-lifetime extension compared to other routing protocols.

The pervasive use of Peer-to-Peer (P2P) systems and the growing demand on personalization for consumers has made future business focus on the niche market instead of the mass market. The recommender system, which is able to timely select data of the interest to each individual user, has become the key to any successful business. However, currently most recommendation systems are based on a centralized architecture; nonetheless, they are not suitable for P2P environments. In this article, we propose a distributed semantic P2P overlay, which can provide music search and recommendation services by considering both of user preference and diversity of interests. We then propose a 3C hybrid recommendation procedure that adapts three traditional filtering techniques to fitting the requirements in a distributed semantic overlay. First, we choose a set of proper meta-data to represent a music object and use them to construct the characteristic-vector-based content filter. Second, a dominant attribute, which is one of the attributes in the characteristic vector of a music object, is used to build the profile of a peer. With the idea of the social network, a P2P profile-based collaborative filter is proposed. Finally, we explore the item-to-item relationship to construct a history-based cooperative filter. We use simulations and a real database called Audio Scrobbler, which tracks users' listening habits, to evaluate the performance of the recommendation system. The results demonstrate the effectiveness of the proposed approach compared with existing recommendation systems.

Recently, much attention has been paid to the problem of how to efficiently utilize multiple orthogonal channels and communication radios to enhance the aggregate throughput of a Wireless Mesh Network (WMN). Most of prior works on the channel assignment problem assume that all transmission links can only transmit at the base rate and do not consider the presence of multiple bit- rates. However, in a multi-rate environment, the achievable throughput of a high-rate link is critically affected by neighboring low-rate links when they share the same channel. This problem is called performance anomaly. To cope with the problem, we propose a Rate-Loss function, which is a quantitative metric used to evaluate how critical the performance anomaly problem occurred in each orthogonal channel. In addition, we present a distributed Rate- Loss based Channel Assignment (RL-CA), which enables each router to select channels suffering from the slightest performance anomaly problem in a distributed manner. The simulation results show that RL-CA outperforms existing schemes and can adapt to network dynamics efficiently.

Increasingly, peer-to-peer (P2P) network users expect to be able to search objects by semantic attributes based on their preferences for multimedia content. Partial match search (i.e., search through the use of multimedia content semantic information) has become an essential service in P2P systems. In this paper, we propose SocioNet, a social-based overlay that clusters peers based on their preference relationships as a small-world network. In SocioNet, peers mimic how people form a social network and how they query, by preference, their friends or acquaintances. Hence, SocioNet benefits from two desirable features of a social network: interest-based clustering and small-world properties (i.e., high cluster coefficient among all peers yet short path lengths between any two peers). To realize an interest-based small-world SocioNet, we also investigate the following practical design issues: 1) similarity estimation: we define a quantifiable similarity measure that enables clustering of similar peers in SocioNet; 2) distributed small-world overlay adaptation: peers maintain a small-world overlay under network dynamics; and 3) query strategy under the small-world overlay: we analyze appropriate settings for the Time-to-Live (TTL) value, for TTL-limited flooding, that provides a satisfactory success ratio and avoids redundant message overhead. We use simulations and a real database called AudioScrobbler [CHECK END OF SENTENCE], which tracks users' listening habits, to evaluate the performance of SocioNet. The results show that SocioNet assists peers in locating content at peers with similar interests through short path lengths, and hence, achieves a higher success ratio (than nonsmall-world interest-based overlays and noninterest-based small-world overlays) while reducing message overhead significantly.

Underwater sensor networks (UWSNs) have attracted a lot of attention recently. Since data in UWSNs are transmitted by acoustic signals, the characteristics of a UWSN are different from those of a terrestrial sensor network. In other words, the high propagation delay of acoustic signals in UWSNs causes spatial-temporal uncertainty, and makes transmission scheduling in UWSNs a challenging problem. Hence, in this paper, we propose a spatial-temporal MAC scheduling protocol, called ST-MAC, which is designed to overcome spatial-temporal uncertainty based on TDMA-based MAC scheduling for energy saving and throughput improvement. We construct thespatial-temporalconflictgraph(ST-CG) to describe the conflict delays among transmission links explicitly, and model ST-MAC as a new vertex coloring problem of ST-CG. We then propose a novel heuristic, called thetraffic-basedone-steptrialapproach(TOTA), to solve the coloring problem. In order to obtain the optimal solution of the scheduling problem, we also derive a mixed integer linear programming (MILP) model. Finally, we present a comprehensive performance study via simulations. The results show that ST-MAC can perform better than existing MAC schemes (such as S-MAC, ECDiG, and T-Lohi) in terms of the network throughput and energy cost.

Since the effectiveness of the routing scheme directly affects resource (e.g. energy, bandwidth, and memory) usage in wireless sensor networks, research works on routing protocols have received considerable attention recently. In this paper, we propose a location-free point-to-point routing scheme, called virtual domain and coordinate routing (VDCR), in wireless sensor networks. VDCR applies the concepts of the virtual domain and the virtual coordinate to design its distributed assignment protocol and the location-free routing protocol. That is, the assignment protocol assigns each sensor node the virtual coordinate to represent the location in a network and the virtual domain to maintain the information about the existing shortest path that has been established during the coordinate assignment. Our VDCR is able to efficiently discover the routing path based on the assigned domains and coordinates. The simulations show that the routing path of VDCR is shorter than ABVCap by 62%-70%. Besides, VDCR require 63%-89% preprocess overhead of ABVCap. For realistic network topologies with obstacles, VDCR can improve the routing path by 64% and up to 96%, compared with GPSR that requires physical location information.

Current works on the joint routing and channel assignment (JRC) problem in wireless mesh networks (WMNs) assume that all links operate at the base rate. In other words, they do not consider the presence of multiple bit-rates. However, in multi-rate WMNs, the achievable throughput of a link operating at higher rates could be much less than its bit-rate when it has to contend with low-rate links. This is also known as the multirate sharing problem. To address the problem, we first present a numerical formulation for estimating the dynamic link capacity, which can indicate the degree of the multi-rate sharing problem. Next, we propose an optimization model called MR2C, which solves the JRC problem by considering the dynamic link capacity in order to maximize the throughput in multi-rate, multi-radio, multi-channel WMNs. We then reformulate the primal problem as a Lagrangian dual problem, and apply the subgradient method to approach the dual. Finally, since each subproblem in the dual problem is also a non-linear problem, we design a polynomialtime algorithm to solve it. Our experiment results demonstrate that the proposed capacity formulation can approximate the actual link throughput. We compare MR2C with the base-rate JRC model via simulations. The results show that MR2C achieves throughput gains of 3-4 times and recover from network failure efficiently.

Current wireless protocols retransmit any packet that fails the checksum test, even when most of the bits are correctly received. Prior work has recognized this inefficiency, however the proposed solutions (e.g., PPR, HARQ and SOFT) require changes to the hardware and physical layer, and hence are not usable in today's WLANs and mesh networks. They are further tested in channels with fixed modulation and coding, whereas production 802.11 networks adapt their modulation and codes to maximize their ability to correct erroneous bits.

In multi-channel wireless mesh networks (WMNs), the routing and channel assignment can interdependently determine network capacity. However, joint routing and channel assignment (JRC) is a challenging problem since it must satisfy theRadioConstraint,theChannelConstraint,andtheCo-channelInterferenceConstraint. Recently, many heuristic or suboptimization algorithms have been proposed to address the JRC problem. The aim of this paper is to preform a comprehensive performance study to fairly compare the different approaches. To achieve this goal, a proper benchmark, a mixed integer linear programming (MILP) model that can optimally configure the routing and channel assignment to get maximum achievable network capacity, is provided in this work as well. The proposed MILP model can help system designers quantitatively evaluate existing heuristic methods and make a better decision by considering the tradeoff between the characteristics of various schemes according to the system requirements.

Recently, peer-to-peer systems have become one of the most popular distributed applications. Many previous works have investigated identifier-based indexing systems that support a query-by-identifier service. However, clients usually have only partial information about an object, and prefer to query by keywords. In this paper, we propose a small-world-based keyword search system (SW-KSS) that provides keyword search and similarity search services simultaneously. The proposed SW-KSS applies the concept of the "small world theory" to the construction of an indexing structure. Such structures mirror the way humans keep track of their friends and acquaintances; hence, they can cluster peers who share common interests. The method enables a peer to And objects of interest from similar neighboring peers efficiently. We evaluate the performance of SW-KSS via simulations. The results show that SW-KSS can achieve both scalability and partial-match look-up capability.

In this paper, we analyze the limitation of ρ-domain based rate-quantization (R-Q) model. We find out that a characteristic-based R-Q model can be derived from ρ-domain to q-domain. Experimental data show that such a characteristic-based R-Q model can provide a more accurate estimation of the actual bitrate than existing models for both frame-level and macroblock(MB)-level. In addition, a simple analysis of computational complexity of our quantization-free characteristics extraction framework shows that our model is faster than existing variance and ρ-domain based R-Q models.

Multi-channel wireless mesh networks (WMNs) aim to perform ubiquitous wireless broadband network access. In WMNs, much attention has been paid to the problem of resource allocation, i.e., how to utilize multiple orthogonal channels and multiple communication radios to enhance the aggregate throughput efficiently. Routing and resource allocation correlatively determine the performance of WMNs. Thus, this paper proposes a route-aware resource allocation algorithm to distribute total traffic load over diverse radios and channels based on route information. The simulation results show that route-aware resource allocation can balance the workload among all available resources (radios and channels) and achieve the higher aggregate throughput and fairness.

Since interests in multimedia services have been growing with the proliferation of wireless networks, there is a need to provide an efficient loss-differential scheme for the wired-cum-wireless networks. This is important because with the aid of the differential scheme, the congestion control protocol can tell the packet loss due to the error-prone wireless link or the network congestion. Then, the congestion control protocol will avoid reducing the sending rate blindly in the present of the packet loss. In this work, we propose an end-to-end loss-differential approach, called Trend-and-Loss-Density-based (TD) scheme. Since the congestion losses are highly co-related to each other, the TD scheme considers (a) the trend to indicate where the packet loss happens, and (b) the loss density to examine how often the packet loss occurs. Specifically, we use the autocorrelation function to measure the dense degree of a packet loss in a given sequence. Moreover, the threshold of each trend in the TD scheme is decided according to the characteristics of the wireless channel so it is easily adapted to different wireless network situations. We evaluate the TD scheme via extensive simulations in ns2. The results show that compared with other differential schemes, the TD scheme can substantially improve wireless and overall misclassification rates while maintaining a comparable throughput in all experiments.

Finding the routing path for disseminating data to mobile sinks in the wireless sensor networks is a challenging problem due to the random mobility of sinks and the limited resources of sensors, such as energy, storage capacity, and computing capability. Although flooding the location information of mobile sinks seems to be a naive method to find the path between the data source and the mobile sink, it drains much power of the sensor nodes. Thus, we propose a Hierarchical Cluster-based Data Dissemination scheme, named HCDD, to disseminate data to the mobile sink with light control overhead. In HCDD, the sensor nodes are self-organized to find the route without the knowledge of node's location information. That is, unlike other works, the HCDD can operate without any expensive and power-consuming GPS device used for estimating the location information. The simulation results show that our HCDD scheme can greatly alleviate the control overhead, and, at the same time, achieve longer network lifetime and comparable number of received data with previous works, such as the TTDD-like methods.