Power consumption continues to pose a fundamental challenge for large-scale IoT tag deployment. While conventional active communication strategies struggle to mitigate peak power demands due to energy-intensive components, passive alternatives achieve microwatt-level consumption but suffer from limited operational range. This paper introducesTiga, an innovative hybrid active-passive communication system for Ambient IoT that autonomously selects optimal transmission modes. Recognizing the ambiguity inherent in the distance-based mode switching boundary, we instead adopt energy level as our boundary criterion.Tigaemploys passive communication when channel energy suffices and seamlessly transitions to active transmission when required. Our design features a novel microwatt-scale dual-function detector that performs concurrent downlink signal demodulation and RSSI computation. We further propose an adaptive four-phase blind boundary search algorithm capable of efficient execution on any low-power controller. We prototypeTigaon a four-layer PCB and evaluate its performance across various environments. Experimental results demonstrate that compared to duty-cycling active communication,Tigareduces dynamic power consumption by 1.85× to 9.42× across various distances from the gateway.

This paper presents μTag, an ultra-low-power backscatter sensor that supports high-rate sensing of a large number of targets simultaneously. The core of μTag is an RF “gene editing” technique that embeds both the identity of the sensor and the real-time motion or vibration state of the attached target intensively in the transient features of the sensor’s RF signal, in a collision-resilient manner. We provide practical techniques which i) generate such “genetic signal” with purely analog and extremely simple circuits; and ii) separate the signals from a large scale of sensors reliably. Our experimental results show that our design can support concurrent tracking of 150 targets with a 12 kHz per-tag sampling rate. We also demonstrate with multiple sensing applications that μTag can achieve high-speed and large-scale motion tracking, rotation frequency sensing, and contactless sensing. The PCB power consumption of μTag is 38∼107 μW, according to the operating frequency of the tag. Our ASIC simulation based on the 40 nm CMOS process shows that the power consumption can be further reduced to 0.13∼0.52 μW.

This paper presents mmDFRBC, a symbol-level millimeter-wave (mmWave) backscatter communication system that reuses commercial mmWave FMCW radar infrastructure as the dual-function access point (AP) without hardware modification. We propose M-ary Frequency Shift Modulation (MFSM), a lightweight encoding scheme that enables the tag to modulate information using orthogonal frequency blocks over adjacent chirps, achieving symbol-level modulation with data rates exceeding kbps. To robustly extract the modulation signals from strong radar sensing clutter, we introduce a Coherent Cancellation Demodulation (CCD) method that exploits the coherence difference between sensing signals and modulated reflections. We develop a soft synchronization strategy for operating asynchronously and requiring no synchronization or downconversion circuits at the tag, supporting kbps-level data rates with low power and cost. We implement mmDFRBC using a commercial mmWave radar and verify its performance across static and mobile scenarios, achieving BERs below$10^{-3}$over 4 meters, with strong resilience to radar clutter interference. mmDFRBC supports data rates up to 50 kbps, highlighting DFRBC’s potential to enable high-performance DFRC systems using existing infrastructure.

Efficient processing and sharing of geological environmental data are crucial for sustainable development and informed decision-making. However, current analysis methods struggle with low efficiency and resource utilization, especially in complex computational tasks. This paper proposes a parallel processing framework based on container orchestration that systematically improves the efficiency of geological environment data analysis by integrating container technology and complex task processing optimization strategies. Leveraging containerized processing, we established a standardized packaging and deployment mechanism for geological environmental data analysis algorithms, enabling flexible encapsulating and management of multiple models. In addition, we proposed a complex task decomposition method for pipeline parallelism and realized multicontainer collaborative geological environment data processing in a distributed environment based on container orchestration. For enhancing the efficiency of complex task processing purposes, this paper proposed a task scheduling optimization strategy based on the dynamic merging of directed acyclic graph, which improves resource utilization and processing speed through task merging. Experimental results demonstrate that the proposed framework enhances processing efficiency by over 50% in typical geological environmental data analysis scenarios, while improving resource utilization by$\mathbf{4 8 \% - 6 9 \%}$. It exhibits strong reliability and scalability, offering technical support for intelligent analysis and service sharing of geological environmental data.

This paper presents Serafin, the first full-stack, sub-mW, and versatile Bluetooth-enabled RF sensor that brings transparent RF sensing to any mobile and IoT device: it independently conducts the whole RF sensing process from RF signal reception to sensing result computation in a wide variety of sensing tasks with only negligible power consumption. At the core of Serafin are our two designs that address the challenge posed by the stringent sub-mW power constraint to jointly achieving versatility and full stackness. Specifically, (i) we utilize the ambient Bluetooth advertising signal as the signal for sensing, and extract the phase difference of the sensing signals received by each antenna pair from the amplitude of their sum signal, which avoids power-hungry hardware components and intensive computation, and (ii) we employ low-power MCU as the computation hardware, and suppress its power consumption by activating it adaptively only when necessary and customizing a light-weight neural network model that still ensures satisfactory inference accuracy. Our extensive experiments on 6 representative sensing tasks show that Serafin achieves competitive sensing performance, but consumes only around 500–900μW power, which is 3–4 orders of magnitude lower than those of existing full-stack and versatile counterparts.

This paper demonstrates Serafin, the first full-stack, sub-mW, and versatile Bluetooth-enabled RF sensor that brings transparent RF sensing to mobile and IoT device: it independently conducts the whole RF sensing process from RF signal reception to sensing result computation in a wide variety of sensing tasks with only negligible power consumption. At the core of Serafin are our two designs that address the challenge posed by the stringent sub-mW power constraint to jointly achieving versatility and full stackness. Specifically, (i) we utilize the ambient Bluetooth advertising signal as the signal for sensing, and extract the phase difference of the sensing signals received by each antenna pair from the amplitude of their sum signal, which avoids power-hungry hardware components and intensive computation, and (ii) we employ low-power MCU as the computation hardware, and suppress its power consumption by activating it only when necessary and customizing a light-weight yet versatile neural network model.

The Wi-Fi-enabled ultra-low power communication system exhibits high asymmetry between uplink and downlink speeds. The uplink can reach up to 1 Mbps, while the downlink throughput is around 100 Kbps. In this paper, we present Wook, a novel high throughput downlink system to empower Commercial Off-The-Shelf (COTS) Wi-Fi devices to transmit high-speed OOK messages. The key innovation underpinning Wook is its ability to achieve sub-symbol level modulation, allowing a single OFDM symbol to carry multiple OOK bits. This is done by profoundly exploring the Wi-Fi PHY layer and identifying optimal input payload to achieve fine-grained Wi-Fi waveform manipulation. We fabricate a PCB prototype and employ the COTS Wi-Fi router to implement the entire system. Experimental results show that with a simulated IC power consumption 76.6μW, Wook achieves a data rate of up to 1.1 Mbps, an 8.9X improvement over state-of-the-art systems. Moreover, even at a communication distance of 95 m, Wook maintains a throughput of 82.9 Kbps.

Ambient IoT (A-IoT) aims to connect hundreds of billions of ultra-low-power and battery-free devices, which has been included in the agenda for 6G standardization by 3GPP. Backscatter communication is considered the mainstream enabling technique for A-IoT; however, current state-of-the-art can hardly meet A-IoT's main technical requirements simultaneously: power consumption below 100 μW, communication ranges up to 100 m, and 100+ concurrency. This paper presents NanoScatter, the first backscatter network with each tag implemented using our customized backscatter communication ASIC. We propose a nanowatt wake-up receiver design and a sensitivity-driven downlink/uplink modulation mechanism to carry out the ASIC, which enables minimizing the tag's power consumption and long-range communication. NanoScatter supports concurrent communication of 6 IC-based tags with a subcarrier capacity of 512, achieving communication distances of 66 m indoors and 100 m outdoors. The tag consumes 1 μW in idle listening, with the core circuit using 58 nW and 43 μW during communication.

Ambient IoT (A-IoT) targets battery-free, ultra-low-power connectivity for massive devices, which has been a key focus in 6G standardization by 3GPP. While backscatter communication enables A-IoT, existing solutions struggle to meet its core demands simultaneously: power consumption below 100 μW, communication ranges up to 100 m, and 100+ concurrency. In this demo, we present NanoScatter, the first backscatter network with each tag implemented using our customized backscatter communication ASIC. We propose a nanowatt wake-up receiver design and a sensitivity-driven downlink/uplink modulation mechanism to carry out the ASIC, which enables minimizing the tag's power consumption and long-range communication. NanoScatter supports concurrent communication of 6 IC-based tags with a subcarrier capacity of 512, achieving communication distances of 66 m indoors and 100 m outdoors. The tag consumes 1 μW in idle listening, with the core circuit using 58 nW and 43 μW during communication.

With the rapid development of Internet of Vehicles technology, communication and data exchange between vehicles have become an important part of modern traffic management. A content placement and edge collaborative caching solution based on deep reinforcement learning is proposed in this paper, aiming to address the data processing and storage challenges faced by Internet of Vehicles systems. Utilizing the collaborative caching between smart vehicles and roadside units employs deep reinforcement learning methods to find and design a collaborative caching solution for the Internet of Vehicles edge. It uses content segmentation technology to divide and cache content fragments in advance to reduce the central server load and network pressure, thereby adapting to the randomness of vehicle mobility and communication duration. The experimental results show that the proposed scheme can effectively reduce the load on the central server, reduce network latency, and improve cache hit rate, providing a flexible and efficient solution for real-time communication and data exchange in the Internet of Vehicles system.

This paper presents FaB, a frequency-agile backscatter system that can optionally leverage OFDM signals on different bands as carriers for backscatter communication. Compared with existing backscatter systems that are tailored to a specific frequency band, a frequency-agile backscatter yields two critical benefits: i) it can leverage the increased availability of “free rides” across a broad range of frequency band to improve its transmission efficiency; and ii) it becomes compatible with mainstream wireless communication standards, making it applicable to heterogeneous wireless networks. Based on these two features, FaB’s circuits can be migrated to various types of backscatter communication nodes without any modification, significantly reducing design and deployment costs. To show the efficacy of our design, we implement a PCB prototype of FaB and showcase its capability of leveraging OFDM Wi-Fi and LTE signals as carrier waves. Our extensive field studies show that FaB’s multi-band modulator can produce an error vector magnitude of under -15dB in any band below 6GHz with a precision of 10mV.

LoRa backscatter achieves long-range communication at the cost of only tens of micro-watts of power when implemented in integrated circuits (ICs), which makes it a potential enabler for massive IoT. However, despite the above advantages, we find that the current tag design wastes approximately 72% of the chip functional area due to the use of large-size inductors in the impedance loads array. This inefficiency significantly increases the cost per chip during mass production. To address this die area issue, we propose OsTAG, a novel LoRa backscatter design that eliminates all inductors in the impedance loads array while maintaining the same quantization resolution. The key innovation lies in the creation of virtual impedance using oversampling. Realizing such design requires overcoming the technical challenges of mitigating approximation error and managing oversampling clock imperfections. To validate our design, we develop prototype and an IC, demonstrating promising results in terms of both performance and efficiency. OsTAG is expected to cost only 28% of the existing chip area while achieving comparable hundred-meter level communication range.

This paper presents μTag, an ultra-low-power backscatter sensor that supports high-frequency sensing of a large number of targets simultaneously. The core of μTag is an RF "gene editing" technique that embeds both the identity of the sensor and the real-time motion state of the attached target intensively in the transient features of the sensor's RF signal, in a collision-resilient manner. We provide practical techniques which i) generate such "genetic signal" with purely analog and extremely simple circuits; and ii) separate the signals from a large scale of sensors reliably. Our experimental results show that our design can support concurrent tracking of 150 targets with a 12kHz per-tag sampling rate. We also demonstrate with multiple sensing applications that μTag can achieve high-speed and large-scale motion tracking and rotation frequency sensing. The PCB power consumption of μTag is 38~107μW, according to the operating frequency of the tag. Our ASIC simulation based on the 40nm CMOS process shows that the power consumption can be further reduced to 0.13~0.52μW.

This paper presents FaB, a frequency-agile backscatter system that can optionally leverage OFDM signals on different bands as carriers for backscatter communication. Compared with existing backscatter systems that are tailored to a specific frequency band, a frequency-agile backscatter yields two critical benefits: i) it can leverage the increased availability of "free rides" across a broad range of frequency band to improve its transmission efficiency; and ii) it becomes compatible with mainstream wireless communication standards, making it applicable to heterogeneous wireless networks. Based on these two features, FaB's circuits can be migrated to various types of backscatter communication nodes without any modification, significantly reducing design and deployment costs. To show the efficacy of our design, we implement a PCB prototype of FaB and showcase its capability of leveraging OFDM Wi-Fi and LTE signals as carrier waves. Our extensive field studies show that FaB's multi-band modulator can produce an error vector magnitude of under -15dB in any band below 6GHz with a precision of 10mV.

This paper presents the design, implementation, and evaluation of TaGroup, a fast, fine-grained, and robust grouping technique for RFIDs. It can achieve a nearly 100% accuracy in distinguishing multiple groups of closely located RFIDs, within only a few seconds. It would benefit many inventory tracking applications, such as self-checkout in retails and packaging quality control in logistics. We make two technical innovations. First, we propose a novel method which can measure the channels between multiple pairs of commercial RFID tags simultaneously, and then estimate the proximity relations between them based on the channel information. Second, we introduce a spatio-temporal graph model which captures a full picture of proximity relations among all the tags, based on which TaGroup can perform a robust grouping of the tags. These two designs together boost the grouping speed and accuracy of TaGroup. Our experiments show that in grouping 120 tags into 4 closely located groups, TaGroup can achieve a nearly 100% accuracy, at the cost of only 2 seconds.

This paper presents dual-band Wi-Fi backscatter (DBscatter), which is the first system supporting 2.4GHz and 5GHz Wi-Fi backscatter simultaneously in a single tag. Our key insight is that most existing Wi-Fi devices communicate in the clean 5GHz band. The 5GHz band provides more chances for ”free riding” with less interference, while the 2.4GHz band presents better NLoS performance. DBscatter combines the strengths of the existing 2.4GHz band with the unexplored 5GHz backscatter in a unified design, developing a robust and high-throughput ambient Wi-Fi backscatter system. We make the following technical contributions: (1) We design a dual-band RF frontend to support dual-band Wi-Fi signals. (2) We propose a tag data demodulation algorithm, which merges the common phase error in multi-antenna received signals, improving the tag transmission reliability while reducing the number of required receivers. (3) We build a prototype of DBscatter system using COTS FPGAs and SDRs. Compared to TiScatter and FreeRider, DBscatter boosts Wi-Fi backscatter throughput by 3.74X and 7.35X, and energy efficiency by 1.78X and 1.38X respectively.

We in this paper present TiSee, an RFID-based sensing system that supports miniature robots to perform agile tasks in everyday environments. TiSee’s unique capability is that it uses a single arbitrarily-deployed antenna to locate a target with sub-cm-level accuracy and identify its orientation to within few degrees. Compared with existing solutions which rely on either antenna arrays or multiple RFID readers, TiSee is cheap, compact, and applicable to miniature robots. The idea of TiSee is to stick an RFID tag on the robot (or its gripper) and use it as a moving “antenna” to locate the tags on the target. The core of this design is a novel technique which can build a “channel” between two commercial RFID tags. Such an inter-tag channel is proved to be highly sensitive to the change in inter-tag distance and is resistant to multipath. By leveraging this channel and the mobility of the robot, we emulate an antenna array and use it for fine-grained localization and orientation estimation. Our experiments show that TiSee achieves a median accuracy of 9.5mm and 3.1° in 3D localization and orientation estimation. TiSee brings an eye-in-hand “camera” to miniature robots, supporting them to perform agile tasks in dark, cluttered, and occluded settings.

This paper presents MobiScatter, which enhances capacity of CSS based backscatter networks for accommodating drone-carried access points (APs). CSS based backscatter design has favorable features including long range and high concurrency. However, the concurrency of the network can be reduced by 34% when the drone-carried AP is introduced due to the mobility and fast fading. In order to maintain high concurrency, MobiScatter presents a series of new designs. In particular, we propose to enhance concurrency of the CSS based backscatter network with symmetric upchirps and downchirps, which neutralizes the impact of imperfect frequency orthogonality. Then, to mitigate the impact of fast fading on decoding, we present a novel half-period chirp modulation scheme for crossed chirps. Finally, we provide a power management method for tags by controlling transmitting time of chirps. We construct a MobiScatter prototype, which contains a drone-carried AP implemented with a mobile USRP and 200 tags. We deploy those tags in an area of$200 m\times 180 m$on a meadow. Experimental results show that MobiScatter can support 160 concurrent backscatter transmissions when the AP moves at$15 m/s$.

This paper for the first time demonstrates how to enable OFDMA in Wi-Fi backscatter for capacity and concurrency enhancement. With our design, the excitation signal is reflected, modulated and shifted to lie in the frequency band of the OFDM subcarrier by the tag; OFDMA is realized by coordinating tags to convey information to the receiver with orthogonal subcarriers concurrently through backscatter. The crux of the design is to achieve strict synchronization among communication components, which is more challenging than in regular OFDMA systems due to the more prominent hardware diversity and uncertainty for backscattering. We reveal how the subtle synchronization scenarios particularly for backscattering can incur system offsets, and present a series of novel designs for the excitation signal transmitter, tag, and receiver to address the issue. We build a prototype in 802.11g OFDM framework to validate our design. Experimental results show that our system can achieve 5.2-$16Mbps$aggregate throughput by allowing 48 tags to transmit concurrently, which is 1.45-$5\times $capacity and$48\times $concurrency compared with the existing design respectively. We also design an OFDMA tag IC, with the corresponding simulation and numerical analysis results show that the tag’s power consumption is in tens of$\mu W$.

This paper presents the design, implementation, and evaluation of TaGroup, a fast, fine-grained, and robust grouping technique for RFIDs. It can achieve a nearly 100% accuracy in distinguishing multiple groups of closely located RFIDs, within only a few seconds. It would benefit many inventory tracking applications, such as self-checkout in retails and packaging quality control in logistics.

Reconfigurable reflective arrays can be used to program the radio propagation environment in order to form favorable wireless channel conditions. Previous designs have used large-scale arrays containing hundreds to thousands of reflecting elements located external to the receiving node, with the reflection coefficients of all array elements managed by a controller. However, these designs can be costly to deploy and are challenging to quickly adapt to the time-varying nature of wireless channels caused by mobility.

This paper presents EmoRI, a novel single-chip mmWave radar-based egomotion estimation approach that works in challenging scenarios where moving objects exist in radar's Field of View (FoV). Essentially, estimating a mobile platform's egomotion using an on-board mmWave radar requires inferring the relative motion between radar and the points of the stationary objects (PSOs) in the radar point cloud. However, in practice, there could be no PSOs because of the blockage of moving objects. Even if PSOs exist, precisely identifying them is still challenging due to (i) the large quantity of points generated by the moving objects, and (ii) the huge angle estimation errors of the conventional point cloud generation algorithm. We empower EmoRI to overcome the above challenges incurred by moving objects with three core techniques, which include (i) a hybrid FFT-MUSIC algorithm that improves the angle estimation accuracy of single-chip mmWave radar, (ii) a multiple stationary target consensus algorithm that precisely selects the PSOs from the radar point cloud, and (iii) a simultaneous fusion and calibration mechanism that introduces an IMU as the auxiliary sensor, meticulously calibrates IMU accelerations with radar measurements, and complimentarily fuses these two modalities to obtain the 6-DoF egomotion. Our extensive experiments validate that EmoRI pushes the limit of single-chip mmWave radar-based egomotion estimation with moving objects in radar's FoV by reducing the per-meter destination error from decimeter to centimeter level.

This paper presents BatchQR, a mobile APP that can batch read densely arranged QR codes attached to caps of the tubes and vials in clinical and biological labs. BatchQR could work in two modes: photo mode and preview mode, which are respectively suitable for scenarios that favor one-time batch processing capacity and real-time tracking performance. For the photo mode, we first propose an IFFT based lightweight code detection mechanism, which can adaptively adjust operating parameters to identify densely arranged QR codes in practice. We propose an image refocus mechanism to deal with blurs/distorts that may appear in the photo, and a lightweight learning based classifier to filter out falsely detected QR codes. We further optimize BatchQR by enabling batch reading in the preview mode of the camera, which is more in line with common usage habits. To this end, we develop a QR code tracking algorithm based on Kalman filtering, which keeps track of each code image dynamically. We also design a parallel acceleration mechanism based on multi-core CPU and GPU, which significantly mitigates the end-to-end delay. Comprehensive experimental results show that BatchQR can read up to 160 Version 1-H QR codes in one shot, with 95 percent accuracy and 100-400ms delay, which is only 0.1 percent of the time consumed by the regular QR code reader in the same situation.

Magnetic sensing is emerging as an enabling technology for various engaging applications. Representative use cases include high-accuracy posture tracking, human-machine interaction, and haptic sensing. This technology uses multiple MEMS magnetometers to capture the changing magnetic field at a close distance. However, magnetometers are susceptible to real-world disturbances, such as hard- and soft-iron effects. As a result, users need to perform a cumbersome and lengthy calibration process frequently, severely limiting the usability of magnetic tracking.

In this paper, we for the first time show how to enable software-defined PHY (SD-PHY) to achieve agile reprogrammability in wireless backscatter networks. This can facilitate innovations in this field by relieving researchers from unnecessary engineering work. With SD-PHY, the tag's PHY-layer behavior can be neatly defined by configuring a set of parameters, which allows the common hardware to generate backscattered signals complying with various wireless protocols. The SD-PHY architecture is based on the key insight that the tag's PHY-layer behavior is essentially determined by reflection coefficient sequence.

We in this paper present TiSee, an RFID-based sensing system that supports miniature robots to perform agile tasks in everyday environments. TiSee's unique capability is that it uses a single arbitrarily-deployed antenna to locate a target with sub-cm-level accuracy and identify its orientation to within few degrees. Compared with existing solutions which rely on either antenna arrays or multiple RFID readers, TiSee is cheap, compact, and applicable to miniature robots.

OFDMA downlink design allowing parallel processing OFDM subcarriers is adopted by a number of commercial wireless standards such as LTE, 5G, and 802.11ax. However, the widespread adoption of OFDMA downlink on low-end IoT devices is stymied due to the existing digital receiver framework's ≈100mW power consumption, which is mainly incurred by LO+mixer, ADC, and complex digital processing. In this paper, we present an ultra-low-power OFDMA downlink demodulation design, which achieves ≈100 μW receiving power. Our basic idea is to transform the current digital demodulation approach into the analog one based on filtering, which avoids those power-hungry components. We achieve this by proposing a series of novel RF front-end hardware designs: 1) a μW-level two-stage mixing scheme that enables adjustable and precise subcarrier filtering, 2) a quartz crystal-based filter circuit incurring negligible insertion loss, and 3) a passive phase-to-envelope conversion technique enabling low-power non-coherent phase demodulation. We build a prototype to verify the proposed schemes. Experimental and IC simulation results show that: our new design can achieve 130 -- 1500 times power savings depending on the number of subcarriers that need to be processed in parallel, compared with the traditional all-digital design.

Emerging crowdsensing paradigm enables a large number of sensing applications, where much attention is drawn to the fundamental problems of data collection and truth inference. Existing works have devised manifold techniques to discover truth from collected noisy data, but they frequently ignore various expertise of workers and dynamic information of crowdsensing system, thus leading to error-prone estimated truth and unqualified sensing data. In this paper, we design an online location-aware crowdsensing system to accurately estimate truth and efficiently assign tasks. Specifically, we unify diverse types of numerical and categorical tasks based on probabilistic graphical model, and then propose unsupervised learning methods which can dynamically infer ground truth and various worker expertise at the same time. Furthermore, we develop online task allocation schemes to gradually gather high quality data considering location awareness and inferred worker expertise. In particular, we convert the complicated task allocation into the additive form of probability improvement and entropy reduction, thereby solving the allocation problem via linearly selecting worker-task pair with low computation complexity. We finally carry out extensive evaluations using two datasets collected by our smartphones, where results demonstrate the superiority of our algorithms over the state-of-the-art approaches.

Channel state information (CSI) based Wi-Fi localization can achieve admirable decimeter-level accuracy; however, such systems require labor-intensive site survey to calibrate the AP position and the antenna array direction, which hinders practical large-scale deployment. In this article, we reveal an interesting finding that the calibration efforts for deploying the CSI localization system can be significantly reduced by simply replacing the ordinary linear antenna layout of the AP with the non-linear layout. In particular, we first present an autonomous self-calibrating method to significantly facilitate site survey for deploying CSI localization systems. Then we propose a systematical evaluation mechanism to show the fundamental reason why linear antenna layout usually leads to serious errors and why non-linear antenna layout is better off. Finally, we build a testbed with COTS devices and conduct comprehensive experiments. Results show that triangular antenna layout can achieve 80% angle of arrival (AoA) measurement error within 9° for any direction in contrast to 16° based on linear antenna layout. Moreover, we can realize promising localization accuracy as previous works even without labor-intensive site survey, where 80% localization error is within$0.60m$.

Fingerprinting indoor localization systems exploit wireless signal propagation features to estimate the location of wireless devices, where the major challenge in practice is the all-consuming training process: it requires site survey to establish the mapping between the signal feature and the location where the feature is observed. In this paper, we present a Wi-Fi localization scheme based on channel state information (CSI) of wireless signals, which manages to relieve time-consuming site survey. In particular, we first propose how to automatically generate the theoretical fingerprints database based on the signal propagation model and geometric methods. Localization with the theoretical fingerprints database yields accuracy close to existing methods. Second, we improve localization accuracy by parsing the user's trajectory instead of restricting to the single spot, where human movement features introduce more information for localization. Third, we present an automatic update scheme for the theoretical fingerprints database to improve time efficiency for localization, which can save 94 - 98% processing time for utilizing the CSI fingerprints database. We implement a prototype with COTS devices and conduct comprehensive experiments to verify proposed mechanisms. Results show that our design achieves 80% localization errors within 0.3m, which is 3× accuracy compared with the state-of-the-art design leveraging CSI.

In this paper, we for the first time demonstrate how to realize multi-frequency backscatter communication for low power IoT devices. Our key innovation is an encoding rule that works from O(100) MHz to 2.4 GHz to cover most commercial communication bands such as BLE, LoRa and Wi-Fi. Based on the proposed method, backscatter devices can adaptively select one appropriate wireless protocol according to application scenarios. To this end, we reveal the fundamental reason why the existing backscatter tags only work in a specific frequency range and propose a universal multi-frequency communication method. We build hardware prototype and conduct experiments at several frequencies from 150 MHz to 2.4 GHz. Experimental results show that UniTag significantly improves communication performance at different frequencies in contrast to the existing methods, where the average bit error rate is only 13% to 50% of existing methods.

Recently proposed OFDMA backscatter could improve both concurrency and spectrum allocation flexibility for backscatter systems based on OFDM. However, we find that it is remarkably inefficient for the existing design to scale up in prototyping: it requires one-by-one offline computation to obtain tags' operating parameters, in order to ensure orthogonality among subcarriers in the system; moreover, the tag hardware has to be dedicatedly modified offline before being assigned multiple subcarriers. The inefficiency is caused by the current analog frequency synthesis design for the tag. This paper proposes DigiScatter, an OFDMA backscatter system realizing digital frequency synthesis, which provides an efficient prototyping approach for large-scale OFDMA backscatter networks. In DigiScatter, we for the first time integrate IDFT into the tag design; such a simple but effective improvement enables the system to support high concurrency and flexible spectrum resource allocation through pure software configurations in an online manner. We build a prototype and conduct comprehensive experiments to validate our design. DigiScatter physically realizes 100 and 300 concurrent OFDMA backscatter transmissions in 2.4GHz and 900MHz respectively, and provides frequency synthesis capability for supporting 1019 concurrent transmissions.

Cellular network positioning is a mandatory requirement for localizing emergency callers, such as E911 in North America. Although smartphones are normally equipped with GPS modules, there are still a large number of users with cell phones only as basic devices, and GPS could be ineffective in urban canyon environments. To this end, the RF fingerprints based positioning mechanism is incorporated into LTE architecture by 3GPP, where the major challenge is to collect geo-tagged RF fingerprints in vast areas. This paper proposes to utilize the subspace identification approach for large-scale RF fingerprints prediction. We formulate the problem into the problem of finding the optimal subspace over Stiefel manifold, and redesign the Stiefel-manifold optimization method with fast convergence rate. Moreover, we propose a sliding window mechanism for the practical large-scale fingerprints prediction scenario, where recorded fingerprints are unevenly distributed in the vast area. Combining the two proposed mechanisms enables an efficient method of large-scale fingerprints prediction in the city level. Further, we validate our theoretical analysis and proposed mechanisms by conducting experiments with real mobile data, which shows that the resulted localization accuracy and reliability with our predicted fingerprints exceed the requirement of E911.

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues of matching workers to their assigned tasks and processing the collected data. In this paper, we reexamine these issues by considering the spatio-temporal worker mobility and task arrivals, which more fit the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the non-linear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby, we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Finally, we propose an optimization framework to aggregate the sensing data which can estimate worker expertise and task truth simultaneously. Performance evaluations on real and synthetic data set validate the proposed algorithm, where 80 percent gain of fairness is achieved at the expense of 12 percent loss of sensing value on average.

This paper presents BatchQR, a mobile APP that can batch read the densely arranged QR codes attached to caps of the tubes and vials in clinical and biological labs. The basic idea of BatchQR is to detect each code in the image and then decode in an one-by-one manner. However, the unique characteristics of the QR code and the application scenario bring technical challenges: First, off-the-shelf lightweight object detection mechanisms are unable to distinguish those densely arranged codes that are highly similar to each other; second, the focus area of the camera is limited, which blurs or distorts parts of the image. To this end, we propose a lightweight code detection mechanism, which can adaptively adjust operating parameters to identify densely arranged QR codes in practice. We also propose a simple but effective image refocus mechanism, which takes an auto-focused image and multiple refocused ones, and then replaces the blurred or distorted code parts with the high-quality counterparts in the refocused images. Comprehensive experimental results show that BatchQR can read 160-180 Version 1-L QR codes in batch with 90%-95% accuracy in 10-14s, which is only 4% of the time consumed by the regular QR code reader in the same situation.

In this paper, we propose a tree modeling-based data mining method to detect anomalies from crowdsourced network data. We design an algorithm to extract potential network anomalies from decision trees. Moreover, we propose a criteria to evaluate the severity of anomaly in terms of three factors: standard deviation, weight sum and impurity decrease. To enhance generalization performance, we randomly generate sample subspace of the original dataset as the input for each subtree and compact detected anomalies from all subtrees. We carry out experiments based on the crowdsourced network measurement dataset containing five million samples, which contains round trip time (RTT) from more than 5,000 users. Experiments show that the proposed method can effectively detect high-latency network anomalies. Moreover, the random forest-based approach can achieve an improvement of approximately 25% of generalization performance compared to the single decision tree approach.

Recent studies show that the voice control system (VCS) is subject to the inaudible voice command attack, which can not be heard by human ears but can be recorded by the microphone. An adversary could leverage the attack to disable the VCS user's home security system, leak the victim's privacy or download malware stealthily. Efforts have been dedicated to developing forensics based defense mechanisms, which target at detecting traces of the attack signal; however, we find that existing approaches of the kind still leave loopholes. Moreover, a complete defense mechanism should be able to not only detect the attack but also cancel out the attack signal, and meanwhile ensure the legitimate voice commands unaffected, which however is still unavailable to the best of our knowledge. This paper is an attempt to fill the gap. We first systematically analyze existing forensics based defense mechanisms and reveal the root cause of their loopholes. Then we present an active inaudible-voice-command cancellation (AIC) design, which can reliably detect and capture the attack signal facilitated by our custom-designed "guard'' signal transmitter. AIC can create a special spectrum in the passband of the VCS microphone, based on which we are able to neutralize the attack signal in software means. We implement a prototype of our defense system and conduct comprehensive experiments to validate our design.

In this paper, we for the first time demonstrate how to enable OFDMA in Wi-Fi backscatter for capacity and concurrency enhancement. With our approach, the excitation signal is reflected, modulated and shifted to lie in the frequency band of the OFDM subcarrier by the tag; OFDMA is realized by coordinating tags to convey information to the receiver with orthogonal subcarriers concurrently through backscatter. The crux of the design is to achieve strict synchronization among communication components, which is more challenging than in regular OFDMA systems due to the more prominent hardware diversity and uncertainty for backscattering. We reveal how the subtle asychnronization scenarios particularly for backscattering can incur system offsets, and present a series of novel designs for the excitation signal transmitter, tag, and receiver to address the issue. We build a prototype in 802.11g OFDM framework to validate our design. Experimental results show that our system can achieve 5.2-16Mbps aggregate throughput by allowing 48 tags to transmit concurrently, which is 1.45-5x capacity and 48x concurrency compared with the existing design respectively. We also design an OFDMA tag IC, and the simulation and numerical analysis results show that the tag's power consumption is in tens of μW.

Channel state information (CSI) based Wi-Fi localization can achieve admirable decimeter-level accuracy; however, such systems require labor-intensive site survey to calibrate the AP position and the antenna array orientation, which hinders practical large-scale deployment. In this paper, we reveal an interesting finding that the calibration efforts for deploying the CSI localization system can be significantly reduced by simply replacing the ordinary linear antenna layout of the AP with the non-linear layout. In particular, we first present an autonomous self-calibrating method to significantly facilitate site survey for deploying CSI localization systems. Then we propose a systematical evaluation mechanism to show the fundamental reason why linear antenna layout usually leads to serious errors and why non-linear antenna layout is better off. Finally, we build a testbed with COTS devices and conduct comprehensive experiments. Results show that triangular antenna layout can achieve 80% angle of arrival (AoA) measurement error within 9◦for any direction in contrast to 16◦based on linear antenna layout. Moreover, we can realize promising localization accuracy as previous works even without labor-intensive site survey, where 80% localization error is within 0.60m.

Future cities are to be not only an intelligent and green living environment but also provide human-centered public services at a lower cost. The Internet of Things (IoT) and artificial intelligence (AI) are two cornerstone technologies enabling the smart city concept, which are fusing into an organic whole in recent years. Some particular joint points where IoT meets AI are intelligent IoT devices, smart sensing boosted by AI, and IoT big data mining with AI. Such “IoT meets AI” trend is already casting significant impact to enable a smart city. Some examples are: smartphones are able to learn the touching pattern of users; home Wi-Fi router can intelligently detect and locate an intruder; vehicles locations and movement information can be exploited for traffic control; video cameras installed in the street can perform face recognition locally.

Network functions virtualization (NFV) architecture enables quick and cost-effective response of mobile network operators to various Internet-of-Things (IoT) applications, where the crux is to effectively and efficiently allocate resources to virtual network functions (VNFs). However, a systematical approach for resource allocation in virtualized mobile core network is still unavailable. In this paper, we propose a synthetic approach based on analysis of both network processing procedures and users' behaviors. Inspired by the static user behavior model adopted by equipment manufacturers' load test procedures, we construct a more practical user behavior model by analyzing over 20TB real data from an operator. With the model, we propose a matrix mapping-based dynamic resource allocation mechanism for the virtualized mobile core networks. To demonstrate the effectiveness of our approach, we conduct experiments using application and signaling records of millions of real users. Results show that the new approach significantly increases the resource utilization and system capacity of the mobile core networks.

Facial paralysis makes patients lose their facial movements, which can incur eye damage even blindness since patients are incapable of blinking. We design and implement a pair of smart glasses iBlink to assist facial paralysis patients to blink. The basic idea is to monitor the normal side of the face with a camera and stimulate the paralyzed side, so that the blink of the both eyes become symmetric. Our contributions are: First, we propose an eye-blink detection mechanism based on support vector machine (SVM), which can detect asymmetric blinks of patients under various illumination conditions with an accuracy above 99 percent. Our eye-image library for training the model is published online for further related studies, which contains more than 30,000 eye images. Second, we design and implement an automatic stimulation circuits to generate electrical impulse for stimulating the patient's facial nerve branches, which can configure operational parameters in a self-adaptive manner for different patients. Third, we implement the entire iBlink system, which integrates the two functions above and a communication function module for tele-medicine applications. We conduct experiments in a hospital to obtain the design basis and verify effectiveness of our device.

Recently proposed Wi-Fi indoor localization systems utilizing channel state information (CSI) derived from the received signal achieve admirable accuracy. This paper presents an information-theoretical analysis in the received waveform level to explore the fundamental limits of the approach. In particular, we perform frequency domain Cramer-Rao bound (CRB) analysis for location estimation with CSI. Our analysis resolves the high-rank Fisher information matrix challenge, and establishes intrinsic connection between parameters to be estimated for localization and the received waveform information observable with the CSI retrieving toolkit. We also analyze the influence of the asynchronization between the transmitter and the receiver to the performance bound of the CSI approach. Moreover, we shed light on the insight into the design of the CSI localization systems. In particular, we show that the CSI approach presents varying performance for localizing targets in different distances and directions with respect to the CSI retrieving anchor point (AP), and the geometric distribution of AP antennas could fundamentally influence the localization performance. Comprehensive experimental results are demonstrated to validate our theoretical analysis.

Self-calibrating wireless indoor localization systems construct the radio map even the indoor floor plan automatically, which avoids the labor-intensive site survey process; however, existing systems utilizing the feature of Wi-Fi signals can only provide coarse-grained indoor maps, which hinders improvement of localization accuracy. In this paper, we present FineLoc, a fine-grained self-calibrating localization system based on the freely-deployed Bluetooth low energy (BLE) nodes and crowd-sourced data, which can profile more detailed layout information of the indoor space. We first reveal that existing systems can only generate inaccurate floor plans owning to the coarse-grained Wi-Fi reference information. Then, we utilize the increasingly popular BLE beacon nodes as the source of reference information, with which a series of dead-reckoning optimization and new schemes particularly for finer-grained indoor map construction are presented. We implement a prototype FineLoc system, which is deployed in around 11,000 m2areas. Our experimental results with the prototype show that FineLoc can achieve 80 percent localization errors within 1.6 m, 1.4 m, and 1.1 m in the library, classroom building, and office building, respectively, with an average density of deployed BLE nodes less than 2.6/100 m2.

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issue of matching workers to their assigned tasks. In this paper, we reexamine this issue by considering the spatiotemporal worker mobility and task arrivals, which more fits the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the nonlinear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Performance evaluation on real data set validates the proposed algorithm, where 116% gain of fairness is achieved at the expense of 12% loss of sensing value on average.

Cellular network positioning is a mandatory requirement for localizing emergency callers, such as E911 in North America. Although smartphones are normally with GPS modules, there are still a large number of users with cell phones only as basic devices, and GPS could be ineffective in urban canyon environments. To this end, the fingerprinting positioning mechanism is incorporated into LTE architecture by 3GPP, where the major challenge is to collect geo-tagged wireless fingerprints in vast areas. This paper proposes to utilize the subspace identification approach for large-scale wireless fingerprints prediction. We formulate the problem into the problem of finding the optimal subspace over Stiefel manifold, and redesign the Stiefel-manifold optimization method with fast convergence rate. Moreover, we propose a sliding window mechanism for the practical large-scale fingerprints prediction scenario, where fingerprints are unevenly distributed in the vast area. Combining the two proposed mechanisms enables an efficient method of large-scale fingerprints prediction in the city level. Further, we validate our theoretical analysis and proposed mechanisms by conducting experiments with real mobile data, which shows that the resulted localization accuracy and reliability with our predicted fingerprints exceed the requirement of E911.

Fingerprinting indoor localization systems have been studied from different perspectives in the past decades; however, a vitally important piece in the puzzle is still missing: how does the system scale with the number of users? In this paper, we study the issue from a theoretical perspective, where the upper and lower bound of the system's localization reliability with respect to the number of users are derived. Our theoretical results can be verified by experiments thus can provide meaningful guidance for practical system design, which is in contrast to the traditional scaling-law work utilizing asymptotical analysis that is valid only under unverifiable extreme conditions. The theoretical and experimental results of our work reveal two interesting observations that shed light on the insight into the scalability of the fingerprinting localization system: First, the localization reliability drops dramatically before the number of users increases to a critical point and then decreases smoothly, where the critical point tends to appear when the number of users equals the number of access points (APs) deployed in the service region; second, even if the number of users approaches to infinity, the fingerprinting localization system still retains certain level of reliability.

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, we establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. We also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.

This paper investigates how to optimize the fingerprints reporting strategy to improve localization accuracy, and how the optimal strategy theory can be utilized to streamline the design of WLAN fingerprinting localization systems. In particular, we first reveal that the fingerprints reporting problem is essentially an NP-Hard size-constrained supermodular maximization problem, and then show the inapplicability of the state-of-the-art approximation algorithms to the problem. We then propose a new algorithm and show that if the number of fingerprints measurements is large enough, then the localization accuracy is at most 1 - ε times worse than the optimal value, with ε any given constant close to 0. Moreover, we demonstrate how the optimal strategy theory can be utilized to improve accuracy of location estimation by resolving the issue of similar fingerprints for both faraway and close-by locations, with an iterative algorithm developed to cross check fingerprints sampled in different locations, in order to derive the best possible result of localization. Further, we reveal the relationship between accuracy of location estimation and coverage of Wi-Fi signals in indoor spaces when planning deployment of APs. Experiment results are presented to validate our theoretical analysis.

Recent study presents a fundamental limit of the RSS fingerprinting based indoor localization. In this paper, we theoretically show that the temporal correlation of the RSS can further improve accuracy of the fingerprinting localization. In particular, we construct a theoretical framework to evaluate how the temporal correlation of the RSS can influence reliability of location estimation, which is based on a newly proposed radio propagation model considering the time-varying property of signals from Wi-Fi APs. The framework is then applied to analyze localization in the one-dimensional physical space, which reveals the fundamental reason why localization performance can be improved by leveraging temporal correlation of the RSS. We extend our analysis to high-dimensional scenarios and mathematically depict the boundaries in the RSS sample space, which distinguish one physical location from another. Moreover, we develop an algorithm to utilize temporal correlation of the RSS to improve the location estimation accuracy, where the process for choosing key design parameters are provided through experiments. Experiment results show that the localization reliability and accuracy can be improved by up to 13 and 30 percent with appropriate leveraging the RSS temporal correlation information.

Network tomography can help detect network link delay. End-to-end path measurement is a promising method in network tomography which could reduce measurement overhead. In end-to-end path measurement, however, deriving link states is computational complex due to the existence of massive links. Because only a few links are congested at the same time, we can locate sparsely congested links by using compressed sensing (CS). In this paper, based on expander graphs, we demonstrate that routing matrix constructed by full tree network can satisfy Restricted Isometry Property (RIP) and be used as measurement matrix. Finally, sparsely congested links are given with CS. Evaluation results show that routing matrix constructed in this paper can help achieve high link delay recovery accuracy, i.e., small recovery error by using 1i-minimization in CS. It also illustrate that the recovery error decreases with the number of congested links decrease.

Facial paralysis is a disease caused by nerve damage, which can make patients lose facial movements. Facial paralysis patients usually have muscles on one side of the face noticeably droop, which seriously impacts the person's quality of life as shown in Fig. [skull]. Worse still, the eye on the affected side is unable to blink and will become dry and infected by debries, which can incur eye damage even blindness. To the best of scientists' knowledge, the paralysis is due to the pressure incurred by infection in the tunnel containing main trunk of facial nerves, where the tunnel is inside of the people's head termed as the Facial canal. In this demo, we present iBlink [1], a novel system to help paralysis patients to blink. Paralysis usually occurs in just one side of the face, and clinical trials show that electrical stimulation could trigger blink. Based on such observations, the basic idea of iBlink is to monitor the normal side of the face with a camera and stimulate the paralysed side, so that eye-movements of the both sides become symmetric.

Facial paralysis makes patients lose their facial movements, which can incur eye damage even blindness since patients are incapable of blinking. The paralysis usually occurs on just one side of the face, and clinical trials show that electrical stimulation could trigger blink. Based on such observations, we design and implement a pair of smart glasses iBlink to assist facial paralysis patients to blink. The basic idea is to monitor the normal side of the face with a camera and stimulate the paralysed side, so that the blink of the both eyes become symmetric. To the best of our knowledge, this is the first piece of wearable device for facial paralysis therapy. Our contributions are: First, we propose an eye-blink detection mechanism based on deep convolutional neural network (CNN), which can detect asymmetric blinks of patients under various illumination conditions with an accuracy above 99%. Our eye-image library for training CNN models is published online for further related studies, which contains more than $30,000$ eye images. Second, we design and implement an automatic stimulation circuits to generate electrical impulse for stimulating the patient's facial nerve branches, which can configure operational parameters in a self-adaptive manner for different patients. Third, we implement the entire iBlink system, which integrates the two functions above and a communication function module for tele-medicine applications. Moreover, we conduct clinical trials in a hospital, in order to obtain the design basis and verify effectiveness of our device.

Coded caching has demonstrated the superiority in mitigating traffic pressure through jointly considering content delivery and storage schemes. However, existing works mainly focus on the situation where users have uniform demands with multiple layer of caches. In this paper, we propose a multilevel hierarchical coded caching scheme when users have nonuniform demands in a multi-hop content delivery network scenario. Specifically, to maintain the symmetry constraint of coded caching we utilize K-means to separate the file set with arbitrary distribution of popularity into several file subsets. We also formulate the Jensen's inequality and derive the upper bound of the transmission rate in each layer. To evaluate the system efficiency, we leverage the open-source Netflix dataset as our file set and conduct an experiment on a content delivery network with two layers of caches. Experimental results demonstrates that our multilevel hierarchical coded caching scheme performs much better than the baseline LFU caching scheme.

In the last two decades, Multi-Cast schemes in mobile ad-hoc networks have been widely studied and lots of research results have been put into use and changed our everyday life. However, packet loss for Multi-Cast schemes in mobile ad-hoc networks has not been well studied yet. The influence of node density and relay schemes on packet loss is not clear. Thus, we study the packet loss problem for Multi-Cast schemes in MANETs in this paper. First, we present a general Multi-Cast probabilistic model to get a better understanding of the packet loss problem. Second, based on the general model and Chernoff Bounds, we prove that the up'er bound of the general packet loss for Multi-Cast is Ω(me- 2), where n is the node number and m denotes number of hops. Thus we can conclude that large node density decreases the packet loss rate by exponential factor, and bigger hops increase packet loss rate linearly. In addition, we present a reliable Multi-Cast protocol(RMP). Through an ACK Aware Tree to complete packet acknowledgement and repair host selection, RMP is able to minimize the impact of packet loss on the Multi-Cast throughput in the simulation.

Fingerprinting localization systems are outstanding for its convenient deployment, where a major challenge is the high cost for collecting a huge number of received signal strength (RSS) fingerprints. Mobile crowdsensing (MCS) paradigm is cost-effective for large-scale data collection; however, a quality-aware data pricing mechanism dedicated to MCSed fingerprints accommodating practical application situations including budget constraints and online data submission is still unavailable. In this paper, we present a data pricing scheme dedicated to MCSed fingerprints by enhancing the online learning technique. We first reveal the principle of fingerprints quality assessment for accurate localization. Based on the principle, we design corresponding loss and regret function, reflecting the values of the fingerprints with respect to localization accuracy. We then present an online pricing scheme for MCSed data, which results in that the worker's payoff is a random variable following an optimal probability density function (PDF) leading to the minimum expected regret. Further, we extend our scheme to application scenarios with different budget settings, where the pricing strategies for the scenarios of regret minimization with fixed budget and budget minimization for certain fingerprints quality level are investigated. Experimental results are presented to verify our theoretical analysis.

Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving, such as image classification, optical character recognition, and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of multi-class labeling tasks under strict budget constraint. We properly profile the tasks’ difficulty levels and workers’ quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational, and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.

Indoor localization for quadrotors has attracted much attention recently. While efforts have been made to perform location estimation of quadrotors leveraging dedicated indoor infrastructures, the low-cost and commonly used RSS fingerprinting based approach utilizing existing Wi-Fi APs has yet to be applied. The challenge is that the high-speed flight reduces the RSS measuring opportunities for fingerprints comparison; moreover, the 3D space fingerprints collection incurs more overhead than in the traditional 2D case. In this paper, we present HiQuadLoc, a RSS fingerprinting based indoor localization system for quadrotors. We propose a series of mechanisms including path estimation, path fitting, and location prediction to deal with the negative influence incurred by the high-speed flight; moreover, we develop a 4D RSS interpolation algorithm to reduce the site survey overhead, where 3D is for the indoor physical space and 1D is for the RSS sample space. Experimental results demonstrate that HiQuadLoc reduces the average location error by more than 50 percent compared with simply applying the RSS fingerprinting based approach for 2D localization, and the overhead of RSS training data collection is reduced by more than 80 percent.

Indoor localization has been an active research field for decades, where received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques, such as the recently proposed one building fingerprints database with crowdsourcing. While efforts have been dedicated to improve accuracy and efficiency of localization, performance of the RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental issue: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size. We reveal the interaction among accuracy, reliability, and the number of measurements in the localization process. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best localization accuracy with given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization system. Further, we analyze the influence of imperfect database information on the reliability of localization, and find that the impact of imperfect information is still under control with reasonable number of samplings when building the database.

Video on-demand (VoD) service is very popular over the mobile Internet. With the demand and quality of video contents become increasingly high, the capacity demand on mobile networks is explosively increasing. In order to sustainably accommodate the future traffic growth, operators such as Verizon start to offload video traffic with broadcasting. However, how to improve VoD users' quality of experience (QoE) under wireless broadcasting is still an open issue, where the major challenge is the time-varying wireless channel capacity. In this paper, we design a wireless VoD scheme with a periodic broadcasting approach. The basic idea is to fragment a video into segments, which are then delivered over different broadcasting channels periodically. We integrate network coding into our scheme so that packet redundancy can counteract the wireless unreliability. Moreover, we reveal the fundamental limits our proposed scheme can achieve in terms of two key QoE metrics: access delay and probability of continuous playout. We then show the intrinsic connections between the two QoE metrics and the choice of design parameters. The tradeoff between QoE improvement and bandwidth overhead is also presented. We implement the scheme in a testbed and demonstrate comprehensive experiment results.

Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality,$k$-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for$k$-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and$k$-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.

When social behaviors are considered into ad hoc wireless networks, the users prefer to forward the traffic from their social contacts. In this way, the network capacity is highly related with the social characteristics. To this end, we apply a social contact based wireless network model and explore its throughput capacity. The wireless network capacity will get improved when the contacts are located nearby the source node. Directional antennas are applied to further enhance the capacity performance, which can reduce the interference brought by other simultaneous communications. Our results demonstrate that when the wireless communications are dominated by the short-distance social contact traffics, the throughput capacity can be greatly improved. Meanwhile, when the directional antenna beamwidth is at the order of Ω( 1 √log ), the throughput capacity is a constant. The wireless network with social contacts is scalable.

Data volume has been increasing explosively in recent years and learning methods are vitally important to extract key information in such mass data. Traditional offline learning requires multiple traversals to the dataset, thus frequently suffering from lack of computational resources. Online learning can benefit in shrinking total time consumed by training model and lowering computational capacity. However they often converge slowly due to memory loss. Considering partial feedback, we uniquely propose online Confidence- Weighted learning in Bandit setting (CWB) for lower cumulative error and higher convergence rate. Specifically, historical information is preserved to adjust the weights of features for speeding up the convergence rate. Moreover, we novelly integrate the random sampling into the confidence-weighted learning, which can balance the exploitation and exploration in bandit setting. Extensive experiments demonstrate efficiency and effectiveness of our proposed scheme.

Wireless sensor networks (WSNs) always collect an enormous amount of rich diverse environmental information. When WSNs grow large in scale, it is difficult for the sink to gather data due to the increasing transmission overhead. In order to improve the fidelity of data recovery and save energy, we propose a novel data aggregation and space-time global recovery scheme. The scheme exploits block Compressed Sensing (CS) to achieve both recovery fidelity and energy efficiency. We employ diffusion wavelets to partition a large WSN into sub-networks, which are regarded as blocks. For each sub-networks, diffusion wavelets are applied to get the sparse basis for the data to be compressed. In addition, we introduce temporal and spatial correlation into the optimum target function of global recovery algorithm. Simulation results show that space-time global recovery scheme holds higher fidelity of data recovery and greatly reduces the energy consumption. Typically, the normalized mean absolute error of our recovery scheme is less than 5%. Furthermore, the energy consumption is reduced more than 50% against plain CS.

Crowdclustering clusters data items in a crowdsourcing manner, which makes discovered item categories more consistent with human perception. However, due to diversity of crowdsourcing workers and fluctuation of the number of tasks assigned to each worker, inferring stable and reliable clusters is challenging. Moreover, an item may be associated with multiple attributes, and such items should be put into different clusters, which makes inferring accurate clusters more complicated. To address the challenges above, in this paper we present a robust and fast crowdclustering scheme for finding overlapping clusters of items. Distinguished from existing works, we extract reliable and stable cluster information from workers' answers by majority voting. We then formulate an optimization problem to find overlapping clusters, and develop a nonnegative matrix factorization based approach to approximate the optimal solution. Experiments show the robustness, accuracy and efficiency of our approach.

How to integrate labels from multiple labelers in order to obtain an accurate estimate of the ground truth is a major topic of crowdsourcing. One challenging issue is that, the labelers' abilities may vary significantly and the tasks distinguish each other in difficulties. Moreover, for a crowdsourcing system, task distributors have no idea in advance how many labels will be enough for each task. Consequently, an online task assignment mechanism based on the labeler expertise and question heterogeneousness becomes necessary. In this paper, we present such an online task assignment algorithm based on a probabilistic model consisting of both labeler abilities and question difficulties. We apply the online EM (Expectation Maximization) algorithm to make online estimations of system parameters, based on which we assign tasks adaptively. A series of simulation results have been demonstrated to show that our proposed scheme outperforms the conventional EM algorithm in efficiency and accuracy.

Indoor localization based on RSS fingerprinting approach has been attracting many research efforts in the past decades. Recent study presents a fundamental limit of the approach: given requirement of estimation accuracy, reliability of the user's localization result can be derived. As highly accurate indoor localization is essential to enable many location based services, a natural question to ask is: can we further improve the accuracy of the localization scheme fundamentally? In this paper, we theoretically show that the temporal correlation of the RSS can improve accuracy of the RSS fingerprinting based indoor localization. In particular, we construct a theoretical framework to evaluate how the temporal correlation of the RSS can influence the reliability of location estimation, which is based on a newly proposed radio propagation model considering the time-varying property of signals from a given Wi-Fi AP. Such a theoretical framework is then applied to analyze localization in the one dimensional physical space, which reveals the fundamental reason why performance improvement of localization can be brought by temporal correlation of the RSS. We further extend our analysis to high-dimensional scenarios. Experimental results corroborate our theoretical analysis.

In this paper, we show how to partition K users in a coded caching domain into L groups to reduce the complexity from O(2K) to [EQUATION]. We first show that the negative effects of the heterogeneity of cache sizes and partitioning on coded caching performance neutralize each other, which is the theoretical foundation of our partitioning schemes. We then prove the submodularity of the coded caching traffic volume function, based on which we develop a partition algorithm for minimizing the traffic volume. We show that the approximation ratio of proposed algorithm is better than the state-of-art result. Moreover, we develop a rounding algorithm to obtain the final partition based on Lovász extension, which resolves the issue of unbalanced partition. We implement our schemes in a testbed and validate our analysis and design with experiment results.

Distributed TDMA medium access protocol plays an important role in stabilizing the throughput under various network states. The major challenge of implementing such protocol lies in the difficulty of slot assignment. Previous works provide a variety of mechanisms to implement the collision-free slot assignment. However, when network topology changes frequently, existing approaches suffer from low throughput due to the waste of idle slots. In this paper, we propose a novel distributed TDMA protocol called HD-MAC to improve the throughput performance under high dynamic change of network topology. Specifically, we come up with a frame structure with fixed length and establish a network join principle regarding the slot assignment for the newly added node, which avoid the overhead of maintaining extra frame length information among nodes. In addition, we adopt a contention-based channel access mechanism for enhancing the utilization of the idle slots, thus increasing the overall throughput. Finally, simulation results are demonstrated to validate the effectiveness of HD-MAC.

Periodic broadcasting has achieved prominent performance in VoD (Video on Demand) service in wired network. However, the development of wireless VoD service still has gone on hard and slowly in comparison to the rapid growth of mobile video service. In this paper, we propose a scalable video streaming method HQOBA (Heterogeneous Quality-Oriented Bandwidth Allocation) to address the problems in wireless network. To be concrete, in order to tackle the heterogeneous client type problem, we employ variable bandwidth allocation, instead of fixed bandwidth value for each channel. Moreover, to handle the problem of scarce server bandwidth and unpredictable wireless channel conditions, the proposed method takes the advantage of FGS (Fine Granular Scalable) video coding and Periodic Broadcasting to successfully transmit video segment. Further-more, we develop a bandwidth allocation algorithm to maximize client perceptual visual quality for the proposed transmit scheme. The experimental results of HQOBA scheme also validate the effectiveness.

Recent study on Wi-Fi RSS fingerprinting based indoor localization reveals that reporting fingerprints with respect to different set of access points (APs) results in location estimations in different levels of accuracy; however, how to find the best strategy for fingerprints reporting with reasonable computational cost, and how to exploit the finding to streamline the design of the localization system are still unknown. In this paper, we revisit the design principles of the localization system with the opportunity provided by the theory of best fingerprints reporting strategy. We first present the localization reliability bounds under the best strategy, and develop algorithms to find the best strategy in practice. We then demonstrate how the best strategy theory can be utilized to improve accuracy of location estimation by resolving the issue of similar fingerprints for both faraway and close-by locations. An iterative algorithm is developed to cross check fingerprints sampled in different locations, in order to derive the best possible result of localization. Moreover, we reveal the relationship between accuracy of location estimation and coverage of Wi-Fi signals in a region, when planning deployment of APs. Experiment results are presented to validate our analysis and design.

Lane-level vehicular localization has been regarded as a critical technology component for future vehicle navigation services. Current lane-level vehicular localization systems require dedicated devices, making the systems difficult to popularize. Moreover, most systems depend heavily on continuous accurate GPS data, which may be interfered under specific environments. Efficient lane-level map building is another problem to deal with. In this paper, we propose an integrated system with the capability of map building and lane- level localization using smartphones. This system employs a crowdsourcing-based approach to collect information from multiple sensors (including GPS, orientation sensor and acceleration sensor), such as lane changes and turns. Based on the information, a lane localization schemes is designed using the tool of machine learning. The experimental results show that the proposed system achieves high accuracy of map building and lane-level localization.

Comparing with the unicast technology, multiple flows from the same source in multicast scenario can be aggregated even if their destinations are different. This paper evaluates such distinction by the multicast gain on per-node capacity and delay, which are defined as the per-node capacity and delay ratios between multi-unicast and multicast ( m destinations for each multicast session). Particularly, the restricted mobility model is proposed, which is a representative mobility model characterizing a class of mobility models with different average moving speeds. The theoretical analysis of this model indicates that the mobility significantly decreases the multicast gain on per-node capacity and delay, though the per-node capacity of both unicast and multicast can be enhanced by mobility. This finding suggests that mobility weakens the distinction between multicast and unicast. Finally, a general framework of multicast study is constituted by analyzing the upper-bound ( Θ(m)), the lower-bound ( Θ(1)) and the main determinants of the multicast gain on both per-node capacity and delay regardless of mobility model.

In this paper, we derive the critical transmission range, i.e., the smallest transmission distance of nodes such that wireless network can be connected, in large-scale clustered wireless networks. Contrary to most previous literature on independent and homogeneous mobility of nodes, we consider general settings with inhomogeneous node distribution and correlated mobility. In particular, we consider three network states based on the degree of correlation among nodes, i.e., cluster-sparse state (strong correlations), cluster-dense state (weak correlations), and cluster-transitional state (medium correlations). Under each state, we focus on the following problems: 1) how to place cluster-head nodes to minimize the critical transmission range and 2) what is the corresponding minimum critical transmission range. We derive the optimal distribution of cluster-head nodes that minimizes the critical transmission range, and show that the inhomogeneous distribution of mobile nodes leads to a smaller critical transmission range.

Abstract In this paper, we focus on maximizing the influence of online social networks (OSNs). Particularly, we try to answer how to select proper information initiators such that information can propagate as widely as possible. We stress our attention on the susceptible‐infected model, a type of epidemic models, to describe the process of information diffusion. In general, OSNs can be classified into two categories, Facebook‐like OSNs and Twitter‐like OSNs. The former ones require bidirectional connections, while the latter do not, so we use the undirected unweighted graph and directed unweighted graph to describe them, respectively. We also pay additional attention to the nonidentity of the link probability on information transmission and build the weight graph, which can also cover both the two types of OSNs. In order to determine values of weight graph's weights, we introduce a learning method to obtain useful factors from raw data for assessing the true link probability on information transmission. Based on spectral analysis within the three graphs, our investigations on the information diffusion show that the spectral radius of the graph adjacency matrix can reflect the capability of information propagation, according to which we could determine effective initiators. We conduct our simulations on real OSNs. Experimental results show that our approach could effectively discover the initiators that spread information widely. Copyright © 2016 John Wiley & Sons, Ltd.

The demands of video traffic transmission have grown rapidly in recent years. The emergence of converged network alleviates heavy burden on cellular network by introducing broadcasting. The transmission cost of contents with large demands can be effectively reduced. The content distribution strategy compromising between broadcasting and unicasting plays an important role in improving the performance of the converged network. To this end, we formulate the content delivery in a converged network by a game with three players, which are broadcast network service provider, unicast network service provider and a content provider. The cooperation without reward sharing among these players is formulated as a Stackelberg game. In this game, the two service providers are leaders to determine their transmission prices in a competitive way. The content provider is the follower to decide the content distribution strategy. Its Nash Equilibrium is derived consequently. In addition, we investigate the cooperation with reward sharing among the three players by introducing Shapley Value model. According to numerical results, it shows that the proposed Shapley Value based reward sharing scheme can improve the fairness of network cooperation. The gain of content provider using converged strategy over single mode transmission is validated as well.

Multiple Radio Access Technologies (Multi-RAT) make it possible to exploit the advantages of Heterogeneous networks (HetNets) resulting from a joint consideration of the networks as a whole. Users in HetNets can be served with a proper RAT to maximize the system-level utility. Especially, when user dynamics are considered, they can stay in a RAT or handover to another RAT with a transition probability depending on system configuration. By formulating these dynamics as a Markov chain model, the system-level utility is defined as a combinatorial object function. However, the combinatorial optimization is NP-hard, thus we can only use exhaustive search to obtain the optimum solution, which comes up with high computational complexity and is not practical. To this end, we use Markov approximation to obtain the approximate utility and transition probability. In addition, we propose a Count Down and Select (CDS) algorithm to implement the RAT selection. Numerical results validate the convergence of Markov approximation and the effectiveness of the CDS algorithm.

Device-to-device (D2D) communication is the need of the hour in the domain of next generation wireless networking and in the rapidly evolving smartphone network world. D2D technology facilitates mobile users to communicate with each other directly, bypassing the cellular base stations. As a popular D2D technique, WiFi-Direct is also a budding new technology that has the ability to set up wireless communications between a group of smartphones. While single-hop D2D based networks have been promising and energy efficient, multi-hop D2D based networks, though demanded in some emerging applications, are not well studied. In this paper, we elaborate the concept of multihop smartphone networks based on WiFi-Direct and propose an energy efficient cluster-based routing protocol, QGRP, to address the energy issue of increasing importance due to high energy costs of smartphones. Simulations demonstrate that QGRP can save significant amounts of energy compared to the cases without QGRP.

Exploiting multiple radio interfaces over multiple channels and using directional antennas are promising technologies to enhance the performance of wireless networks. However, in such a multi-dimensional network resource space, assignment of radios and channels and configuration of antenna directions are coupled, making the complexity for network capacity optimization dramatically increase. Existing work has considered either multi-radio multi-channel (MRMC) networks or networks with directional antennas (DA); however, there lacks a generic framework for such complex MRMC-DA wireless networks. In this paper, we employ the tuple concept to define Link- Radio-Antenna-Channel tuple links, which are then utilized as building blocks to construct a multi-dimensional conflict graph (MDCG) of the MRMC-DA network. The MDCG model facilitates mapping the original MRMC-DA network into a simple virtual single-radio single-channel network, on which the capacity optimization problem can be formulated as a linear program. To circumvent searching the exponentially many independent sets, we apply the delayed column generation method to design our algorithm. Simulations demonstrate the performance of the proposed method and analyze the different effects of the numbers of channels, radios and antenna choices.

With the pervasion of mobile devices, crowdsourcing based received signal strength (RSS) fingerprint collection method has drawn much attention to facilitate the indoor localization since it is effective and requires no pre-deployment. However, in large open indoor environment like museums and exhibition centres, RSS measurement points cannot be collocated densely, which degrades localization accuracy. This paper focuses on measurement point collocation in different cases and their effects on localization accuracy. We first study two simple preliminary cases under assumption that users are uniformly distributed: when measurement points are collocated regularly, we propose a collocation pattern which is most beneficial to localization accuracy; when measurement points are collocated randomly, we prove that localization accuracy is limited by a tight bound. Under the general case that users are distributed asymmetrically, we show the best allocation scheme of measurement points: measurement point density ρ is proportional to (cμ)2/3in every part of the region, where μ is user density and c is a constant determined by the collocation pattern. We also give some guidelines on collocation choice and perform extensive simulations to validate our assumptions and results.

Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques such as the recently proposed one building the fingerprint database with crowd-sourcing. While efforts have been dedicated to improve the accuracy and efficiency of localization, the fundamental limits of RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental question: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system. We reveal the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best accuracy for given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization facilitated by crowdsourcing paradigm.

Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving such as image classification, optical character recognition and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of binary tasks under strict budget constraint. We properly profile the tasks' difficulty levels and workers' quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.

Exploiting direct transmissions between geographically close mobile users without passing through the base stations, device‐to‐device (D2D) communications contribute significant improvement to the spectral efficiency of cellular networks. In D2D‐assisted cellular networks, the social interaction of mobile users is an important property that will affect the practical performance and should be seriously accounted in the network resource allocation, which is yet to be fully explored. In this study, the authors investigate the social interactions for D2D transmissions and develop a contact time model to characterise the D2D links. A D2D link can be considered for resource allocation only when the two users encounter and their contact time is enough long to complete a meaningful transmission. They formulate and compare both sociality‐blind and sociality‐aware optimisation problems for resource allocation in D2D‐assisted cellular networks. Extensive numerical results are presented, validating that the sociality‐aware resource allocation can achieve higher performance than that of the sociality‐blind approach.

In this study, a three‐dimensional mean shift tracking algorithm, which combines the multi‐scale model and background weighted spatial histogram, is proposed to address the problem of scale estimation under the framework of mean shift tracking. The target template is modelled with multi‐scale model and described with three‐dimensional spatial histogram. The tracking algorithm is implemented by three‐dimensional mean shift iteration, which translates the problem of scale estimation in two‐dimensional image plane into the localisation in three‐dimensional image space. To enhance the robustness, the background weighted histogram is employed to suppress the background information in the target candidate model. Firstly, the multi‐scale model and three‐dimensional spatial histogram are introduced to represent the target template. Then, the three‐dimensional mean shift iteration formulation is derived based on the similarity measure between the target model and the target candidate model. Finally, a multi‐scale mean shift tracking algorithm combining multi‐scale model and background weighted spatial histogram is proposed. The proposed algorithm is evaluated on some challenging sequences which contain scale changed targets and other complex appearance variations in comparison with three representative mean shift based tracking algorithms. Both the qualitative results and quantitative analysis indicate that the proposed algorithm outperforms the referenced algorithms in both tracking precision and scale estimation.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, interference generated by D2D communications is usually viewed as an obstacle to cellular communications. However, in this paper, we present a new perspective on the role of D2D interference by taking security issues into consideration. We consider a large-scale D2D-enabled cellular network with eavesdroppers overhearing cellular communications. Using stochastic geometry, we model such a network and analyze the signal-to-interference-plus-noise ratio (SINR) distributions, connection probabilities and secrecy probabilities of both the cellular and D2D links. We propose two criteria for guaranteeing performances of secure cellular communications, namely the strong and weak performance guarantee criteria. Based on the obtained analytical results of link characteristics, we design optimal D2D link scheduling schemes under these two criteria respectively. Both analytical and numerical results show that the interference from D2D communications can enhance physical layer security of cellular communications and at the same time create extra transmission opportunities for D2D users.

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = nβ, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α-2α). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(nβ/3log n) when β <; 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.

In cognitive radio networks (CRNs), secondary users (SUs) can flexibly access primary users' (PUs') idle spectrum bands, but such spectrum opportunities are dynamic due to PUs' uncertain activity patterns. In a multihop CRN consisting of SUs as relays, such spectrum dynamics will further cause the invalidity of predetermined routes. In this paper, we investigate spectrum-mobility-incurred route-switching problems in both spatial and frequency domains for CRNs, where spatial switching determines which relays and links should be reselected and frequency switching decides which channels ought to be reassigned to the spatial routes. The proposed route-switching scheme not only avoids conflicts with PUs but also mitigates spectrum congestion. Meanwhile, tradeoffs between routing costs and channel switching costs are achieved. We further formulate the route-switching problem as the Route-Switching Game, which is shown to be a potential game and has a pure Nash equilibrium (NE). Accordingly, efficient algorithms for finding the NE and the$\epsilon $–NE are proposed. Then, we extend the proposed game to the incomplete-information scenario and provide a method to compute the Bayesian NE. Finally, we prove that the price of anarchy of the proposed game has a deterministic upper bound.

In this paper,1we investigate the throughput and delay in heterogeneous cognitive radio networks (HCRN), where the data source and the destination (S-D) is heterogeneously distributed following a rank based model and secondary users (SUs) provide relay service for primary users (PUs). We consider two scenarios: 1) PUs and SUs are both static; and 2) PUs are static and SUs are mobile. For scenario 1, we show that the primary network throughput is the same for different heterogeneous extents of S-D distribution owing to the flexible assistance of SUs, while the throughput of secondary networks is proven to be changing with the S-D heterogeneity exponent α, which depicts the variation of different heterogeneous extents of S-D distribution. In addition, the delay of both primary and secondary networks are shown to be altering with α. Further, we reveal that the number of SUs required to assist PUs can be dramatically reduced when considering the S-D heterogeneity, while achieving the same primary network throughput. For scenario 2, we utilize a modified uniform mobility (MUM) model to depict the motion of SUs and mainly focus on the analysis of throughput and delay for primary networks. It shows that the primary network throughput is also free of the heterogeneous extent of S-D distribution, while the delay changes with α. Due to the mobility of SUs, a better delay-throughput tradeoff of primary networks is achieved compared with that in scenario 1.

This paper investigates random walk mobility models with location heterogeneity, where different locations may have different neighboring regions. We consider$n$locations in a one-dimension network and investigate two cases, i.e.,full-range locationswhere nodes situated have the capability to shuffle throughout the network andlong-range locationswhere nodes are allowed to move to positions nearby within a certain range. In the former situation, with the exact expressions derived, we find location heterogeneity has a critical impact on the first hitting time of random walk, varying from$\Theta (n)$to$\Theta \left(n^3\right)$according to different extent of heterogeneity. The result covers, as two special cases, both the classic independent and identically distributed (i.i.d) mobility and traditional random walk when varying the number of full-range locations. In the latter one, our asymptotic results on both the first crossing time and cover time suggest that they are inversely proportional to the range of neighboring region$r$($\propto r^{-2}$and$\propto r^{-1}$, respectively). Furthermore, with multiple concurrent random walks introduced, the first hitting time can be drastically decreased and the effect is strengthened if combined with location heterogeneity. In addition, our investigation into the stationary distribution of nodes indicates that the uniformity no longer holds due to different transition probabilities, as a result of location heterogeneity. We also conduct extensive simulation results to verify our observations and enhance the understanding on the impact of network parameters. Based on the insights obtained, we move forward to investigate the impact of location heterogeneity in two-dimension networks.

In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.

Abstract As a powerful economic theory, auction mechanism has been extensively studied in dynamic spectrum allocation for cognitive radio networks (CRNs) recently. Different from most of existing works that focused on the mechanism design from the spectrum owner's side, we study from a new perspective on profit maximization of the secondary users (SUs). Because the spectrum auction mechanism has already been designed by the spectrum owner, we derive SUs' optimal bid strategies, which maximize their profits. First, we relax the limitation of SU's value on spectrum band, which is formerly defined as the transmission rate on channel, and introduce the affiliated value considering the impacts from other SUs. Further, the optimal value determination function is derived, which maximizes SU's expected profit; second, we analyze the auctioneer cheating issue, which has great influence on SU's profit, and the Nash equilibrium strategies for both spectrum owner and SUs are derived. Moreover, the repeated auction game mechanism is proposed that resists the auctioneer cheating effectively. Copyright © 2013 John Wiley & Sons, Ltd.

Online Social Network becomes the most prevalent platform for exchanging information between users, maintaining friendships online. As is well-known to us, however, some friendships even those intimate ones might vanish. Therefore, precisely modeling and predicting state of each online relationship is worthwhile in many respects. For social communication services such modeling permits new and novel online services. In addition, constructing this model might enlighten us in exploiting information spreading pattern in online social network. In this paper, we propose a model in determining a probability distribution which describes the ‘surviving time’ of each friendships by applying one commonly used method in sociology, survival analysis. We discuss a series of social explanatory variables that highly affect this probability distribution. Moreover, methods in the moving average process are devoted to determining the appropriate parameter in survival model. Furthermore, to avoid the high computational complexity in kernel learning we impose sparsity in our model. Finally, with the experiments on real data, the proposed survival model is proven to be of high accuracy, and thus of great potential for further applications.

Auction is often applied in cognitive radio networks due to its efficiency and fairness properties. An important issue in designing an auction mechanism is how to utilize the limited spectrum resource in an efficient manner. In order to achieve this goal, we propose a predictive double spectrum auction model in this paper. Our auction model first obtains the bidding range from statistical analysis, and then separates the interval into independent states and employees a Markovian prediction based algorithm to generate guidelines for the bidding range of primary and secondary users, respectively. Comparing with existing approaches, our proposed auction model is more efficient in spectrum utilization and satisfies the economic properties. Extensive simulation results show that our work achieves an utilization ratio up to 91%.

Network throughput and packet delay are both important performance metrics in Ad-Hoc networks. While uncorrelated mobility has been proved to be able to increase the network capacity at the cost of delay, the influence of correlated mobility on network performance has not been well studied. In correlated mobility model, nodes in the same group are constrained to lie in a disc area, whose center moves uniformly according to the i.i.d. model; however, it is still an open issue how to evaluate the performance of capacity and delay tradeoff in hybrid Ad-Hoc networks. In this paper, we propose a theoretical framework to examine such issue when relay nodes are under correlated mobility and static nodes are acting as transmitters and receivers. Simulation results shows that our algorithm provides better throughput delay tradeoff than i.i.d. model.

Recent studies show that the 60GHz wireless technology could help resolving the hotspot issue in data center networks (DCNs). However, transmissions over 60GHz suffer from limitations of short transmission range and blockage, which makes it a new challenge how to appropriately establish wireless links in the DCN. In this paper, we propose to integrate wireless links into the DCN with the wireless neighborway scheme. Each top-of-rack switch (ToR) has multiple 60GHz wireless links connecting to its neighboring ToRs, which are termed as neighborways. The elephant flow from the sending ToR can be partially offloaded through neighborways, which are then delivered to the ToRs around the receiving ToR through wired links of the DCN, and finally converged to the receiving ToR. The fundamental challenge for the design is how to prevent the offloaded traffics from forming new hotspots in the network fabric. To this end, we developed a wireless network planning solution with corresponding IP address assignment and traffic engineering scheme, which can leverage the potential underutilized wired links in the DCN and meanwhile avoid forming new hotspots. The simulation results show that the proposed scheme can notably relieve the hotspots in the DCN.

Crowdsourcing is an effective paradigm in human centric computing for addressing problems by utilizing human computation power. While efforts have been made to study the crowdsourcing systems for labeling tasks such as classification, those for scoring tasks with continuous and correlative answers have not been well studied. In this paper, we propose two inference algorithms, MCE (Maximum Correlation Estimate) and WMCE (Weighted Maximum Correlation Estimate), to infer true answers based on answers submitted by workers. When estimating answers, WMCE algorithm assigns diverse weight to submitted answers of workers based on their quality while MCE algorithm assigns identical weight to submitted answers of all workers. For a fixed worker population, we reveal that the increase in task redundancy1can improve accuracy of estimated answers but such improvement is limited within a certain level. We further show that WMCE algorithm can reduce the influence of this limitation better than MCE algorithm for the same crowdsourcing system. Simulation results validate our theoretical analysis and show that WMCE algorithm outperforms MCE algorithm in the accuracy of estimated answers.

Mobile Crowdsensing is an effective paradigm to perform tasks in many scenarios by utilizing crowd intelligence and sensing resources. However, the responses from users are often unreliable due to the low-paid rewards and their biased expertise of the crowd. In this paper, we propose a multi-class labeling scheme based on BCH codes (BCH-MCL) for the mobile crowdsensing system where the quality of crowd is unknown. For multi-class labeling tasks, BCH-MCL provides each label a BCH codeword with maximum error correction capability, and maps the responses of the crowd into an estimated codeword to determine the final estimated label. The theoretical analysis characterizes the fault-tolerance capability of BCH-MCL, and derives an upper bound of the mis-label probability for the proposed BCH-MCL scheme, with a necessary condition and a sufficient condition presented. Furthermore, we prove that BCH-MCL can achieve a more accurate estimation and much lower computational complexity, compared with prior work DCFECC. Simulation results validate our theoretical analysis and indicate the effectiveness and efficiency of the proposed BCH-MCL.

Offloading data from cellular networks to WiFi or femtocell networks is an efficient way to alleviate the network congestion caused by rapidly increasing demands for mobile data. To this end, an Internet Service Provider (ISP) is willing to deploy WiFi/femtocell networks. With the advent of such networks, it is necessary to analyze how the ISP sets data plans to improve its profit while mobile data offloading is supported. In this paper, we develop a contract-based scheme to deal with data plan setting problem in two-tier networks. The contract offered by the ISP is a set of data plans which provide different combinations of data volume and price. We classify consumers into different types according to their percentages of data traffic offloaded to WiFi/femtocell networks. Each consumer can choose its own data plan based on its type, which is private information. Under asymmetric information scenario, we provide the necessary and sufficient conditions for the feasibility of a contract and then we derive the optimal contract which maximizes the ISP's profit. Numerical results validate the effectiveness of our scheme and indicate that the ISP can improve its profit by raising the throughput of its WiFi/femtocell networks and/or lowering its energy cost.

Cognitive radio (CR) technique is considered an effective mechanism to relieve the spectrum scarcity issue, where the secondary users (SUs) can utilize the idle spectrum of the primary users (PUs). How the performance of the wireless network will be influenced by the introduction of CR technique has been attracting much attention in past years. While many efforts have been made to study the cognitive radio network, where the data source and the destination (S-D) is homogeneously distributed, the research on cognitive radio networks (CRN) with heterogeneous S-D distribution is still very limited. In this paper, we investigate the throughput and delay scaling law in the heterogeneous cognitive radio network (HCRN), where the S-D pair follows a rank based model and SUs provide relay service for PUs in reciprocating the utilization of PUs' idle spectrum. By applying a cellular TDMA scheduling scheme, we show that the primary network throughput is the same for different heterogeneous extents of S-D distribution owing to the flexible assistance of SUs, while the throughput of secondary networks is proven to be changing with respect to the S-D heterogeneity exponent denoted by α. In addition, the delay scaling are derived for both primary and secondary networks and shown to be altering in accordance with α. Further, we reveal that the density of SUs required to assist PUs can be dramatically reduced when considering the S-D heterogeneity, while achieving the same primary network throughput.

We analyze the scaling law in wireless ad hoc networks with the correlated mobility model. The former work about correlated mobility has shown the maximum throughput and the corresponding delay of several sub-cases, but the optimal throughput performances under various delay tolerant condition (the optimal delay-throughput tradeoff) remains open. We study the properties of correlated mobility model and establish the upper bound of delay-throughput tradeoff for several sub-cases. Then we find out an achievable lower bound by studying the optimal scheduling parameters and their constrains. We exploit the node correlation to achieve the delay-throughput tradeoff and give a picture that how node correlation impacts the packet delay, asymptotic throughput, and their tradeoff.

There have been many traffic light control systems around the globe, but the high cost of infrastructure and maintenance hinders their wide deployment. However, speed-advisory systems enabled by on-vehicle devices are much cheaper and easier to deploy. The first challenge of such systems is to get the traffic signal schedule in complex intersections. The second challenge is to get map information and calculate the distance. Facing these challenges we devise and implement a speed-advisory driving system called CityDrive, which harnesses the sensor and GPS data from a wide participation of smartphones to suggest proper speed for drivers so that they arrive at intersections in green phase. CityDrive first generates a road map and then infers traffic signal schedules, using only smartphones and a server. CityDrive does not eliminate stops at intersections, but it tries to maximize the probability that vehicles cruise through intersections in green phase. Both simulation and real test show that this continuous speed advisory service effectively smoothes traffic flow and significantly reduces energy consumption.

Increasingly, more spectrum bands are utilized for unlicensed use in wireless cognitive networks. It is important to study how information-theoretic secrecy capacity is affected in large-scale cognitive networks. We consider two scenarios: (1) non-colluding case, where eavesdroppers decode messages individually. In this case, we propose a new secure protocol model to analyze the transmission opportunities of secondary nodes. We show that the secrecy capacity of the primary network is not affected, while the secondary network can achieve the same performance as a standalone network in the order sense. Since our analysis is general as we only make a few relaxed assumptions on both networks, the conclusions hold when both networks are classic static networks, networks with i.i.d mobility, multicast networks etc. (2) colluding case where eavesdroppers can collude to decode a message. In that case, we show that the lower bound of per-node secrecy capacity of the primary network is Ω(1/√n φe-2/α-1(n)) when the eavesdropper density is φe(n)=Ω(log2n). Interestingly the existence of secondary nodes increases the secrecy capacity of the primary network.

Robust visual tracking is a very challenging problem especially when the target undergoes large appearance variation. In this study, the authors propose an efficient and effective tracker based on watershed regions. As middle‐level visual cues, watershed regions contain more semantics information than low‐level features, and reflect more structure information than high‐level model. First, the authors manually select the target template in initial frame, and predict the target candidate in the next frame using motion prediction. Then, the authors utilise marker‐based watershed algorithm to obtain the watershed regions of target template and candidate template, and describe each region with multiple features. Next, the authors calculate the nearest neighbour in feature space to match the watershed regions and construct an affine relation from target template to candidate template. Finally, the authors resolve the affine relation to calculate the final tracking result, and update the template for the following tracking. The authors test their tracker on some challenging sequences with appearance variation range from illumination change, partial occlusion, pose change to background clutters and compare it with some state‐of‐the‐art works. Experiment results indicate that the proposed tracker is robust to the large appearance variation and exceeds the state‐of‐the‐art trackers in most situations.

In this paper, we develop a scalable destination-oriented multicast (DOM) protocol for computer networks where the routers have enhanced intelligence to process packets. The basic idea of DOM is that each multicast data packet carries explicit destinations information, instead of an implicit group address, to facilitate the data delivery. Based on such destinations information, each router can compute necessary multicast copies and next-hop interfaces. A fundamental issue in DOM is to constrain the bandwidth overhead due to explicit addressing, which is tackled with a Bloom-filter based design. Our design incorporates the reverse path forwarding (RPF) concept and the BGP routing information, so that DOM can work efficiently in practical networking scenarios especially with asymmetric inter-domain routing. A critical issue in Bloom-filter based design is the issue of forwarding loop due to false positives. We propose an accurate tree branch pruning scheme, which equips the DOM the capability to completely and efficiently remove the false-positive forwarding loop. Furthermore, we study how the DOM can be deployed in an incremental manner over a network, in which only a small fraction of the routers have DOM-aware intelligence while others are legacy routers. We present extensive simulation results over a practical topology to demonstrate the performance of DOM, with comparison to the traditional IP multicast and the free riding multicast (FRM) protocols.

We study the relay-based communication schemes for cooperative spectrum sharing among multiple primary users (PUs) and multiple secondary users (SUs) with incomplete information. Inspired by the matching theory, we model the network as a matching market. In this market, each PU proposes a certain proposal representing a combination of relay power and spectrum access time to attract the SUs, while each SU maximizes its utility by selecting the most suitable PU. We derive the sufficient and necessary conditions for a stable matching in which none of the PUs or SUs would like to change its decision. We further establish a distributed matching algorithm (DMA) and a DMA with utility increasing (DMA-UI) to achieve the equilibria in partially incomplete and incomplete information scenarios, respectively. Moreover, we provide detailed discussions on the implementation of the distributed algorithms in practical networks. Simulation results show that the losses of PUs' total utilities caused by incomplete information are diminished when the number of SUs increases. Specifically, the effects of the incomplete information are reduced as the competition among SUs (PUs) is more intensive than that among PUs (SUs).

In this paper, we give a global perspective of multicast capacity and delay analysis in Mobile Ad Hoc Networks (MANETs). Specifically, we consider four node mobility models: (1) two-dimensional i.i.d. mobility, (2) two-dimensional hybrid random walk, (3) one-dimensional i.i.d. mobility, and (4) one-dimensional hybrid random walk. Two mobility time-scales are investigated in this paper: (i) fast mobility where node mobility is at the same time-scale as data transmissions and (ii) slow mobility where node mobility is assumed to occur at a much slower time-scale than data transmissions. Given a delay constraint$D$, we first characterize the optimal multicast capacity for each of the eight types of mobility models, and then we develop a scheme that can achieve a capacity-delay tradeoff close to the upper bound up to a logarithmic factor. In addition, we also study heterogeneous networks with infrastructure support.

We study the multicast capacity under a network model featuring both node's mobility and infrastructure support. Combinations between mobility and infrastructure, as well as multicast transmission and infrastructure, have already showed effective ways to increase multicast capacity. In this work, we jointly consider the impact of the above three factors on network capacity. We assume that$m$static base stations and$n$mobile users are placed in an ad hoc network. A general mobility model is adopted, such that each user moves within a bounded distance from its home-point with an arbitrary pattern. In addition, each mobile node serves as a source of multicast transmission, which results in a total number of$n$multicast transmissions. We focus on the situations in which base stations actually benefit the capacity improvement, and find that multicast capacity in a mobile hybrid network falls into several regimes. For each regime, reachable upper and lower bounds are derived. Our work contains theoretical analysis of multicast capacity in hybrid networks and provides guidelines for the design of real hybrid systems combing cellular and ad hoc networks.

In this paper, we develop a truthful and efficient combinatorial auction scheme under a novel spectrum allocation model that can achieve a worst-case approximation ratio \sqrt{m} in social welfare. We propose to tackle the dynamic spectrum access problem in cognitive radio (CR) networks with time-frequency flexibility requirements. We model the spectrum opportunity in a time-frequency division manner and the spectrum allocation as a combinatorial auction. Then we design an auction mechanism to reach the upper bound in polynomial time and propose a combined approach to improve the bound in the cost of increasing computational complexity. A truthful payment that gives incentive to the SUs for revealing the truthful valuation of the desirable bundle of slots is presented. In order to reduce the complexity, we simplify the general model to a modified model that only allows frequency flexibility, and then present a truthful, optimal and computationally efficient auction mechanism. Extensive simulation results of the social welfare and spectrum ratio show that the performance of the combined approximation algorithm is better than the sorting based greedy algorithm.

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve spectrum utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and the spatial reusability simultaneously, which leaves room to further enhance the spectrum efficiency. In this paper, we introduce the coalition double auction for efficient spectrum allocation in CRNs, where secondary users (SUs) are partitioned into several coalitions and the spectrum reusability can be executed within each coalition. The partition formation process is not only related to the interference condition between SUs, but also the expected economic goals. Therefore, we propose a fully-economic spatial spectrum allocation mechanism by incorporating the coalition formation approach with auction theory. With the proposed scheme, the primary operator acts as an auctioneer, who performs multiple virtual auctions to form a stable partition of SUs and conducts a final auction to decide the winning SUs. Moreover, we propose a possible operation rules for the primary operator to iteratively change the partition, and prove that the virtual auctions could converge in finite time. Comprehensive theoretical analysis and simulation results are presented to show that our scheme can satisfy the crucial economic robustness properties of double auction, and outperform existing mechanisms.

Intra-cell interference management is one of the technical challenges for device-to-device (D2D) communication underlaying cellular networks. In this paper, we propose two superposition coding-based cooperative relaying schemes to exploit the transmission opportunities for the D2D users without deteriorating the performance of the cellular users. In the first scheme, the D2D transmitter (DT) is enabled to decode and regenerate the cellular signal, and transmit the cellular signal by superposing it with its own signal. In this way, the interference from the D2D pair to the cellular pair can be canceled by properly allocating time and power. To further exploit the transmission opportunity for the D2D pair, in the second scheme the cellular transmitter splits its signal into two parts and broadcasts these two parts in a superposition signal. DT relays only one part of the cellular signal. Analytic and numerical results confirm the efficiency of the proposed schemes.

The rapid development of cloud computing stimulates many research works on Data Center Networks (DCNs), among which studies on energy consumption account a considerable proportion. However, a generic simulation tool that is flexible and user-friendly for such studies is still unavailable, which hinders the research in this area as it is impractical for each research group to build a DCN testbed for themselves. To tackle with the issue, this paper presents a generic simulation framework compatible with the network simulator ns-3, which is emerging as a more and more popular network simulator but still lacks built-in DCN simulation package. With our framework, developers could conveniently implement their energy saving algorithm and configure various simulation scenarios. Public interfaces are exposed to help developers monitor DCN operation details such as total power consumption of each node in the entire simulation period. Moreover, two DCN power consumption models are implemented within the framework for the developers' convenience, which enables users to simulate both traffic level and task level energy consumption simulation. Several practical scenarios are simulated and results are presented to demonstrate the validity of with our framework.

The existence of under-utilized spectrum and the congestion of certain spectrum give rise to development on cognitive radios as a promising technology to address these problems. However, the general precondition that secondary users should evacuate from the channel which primary users are willing to use, makes the cognitive ad hoc networks vulnerable and unstable since network partition may occur since the links affected by primary users may form a cut set of the network. In this paper, we address this problem through a hybrid channel assignment scheme, where channels are carefully assigned to each link to guarantee the connectivity and the capacity based on spanning trees. Since the optimal solution is NP-hard, we propose an effective approximation algorithm. Also, dynamic channel assignment is proposed to further improve the network performance.

Recently, a novel 3D wireless mechanism based on 60 GHz band has been proposed to mitigate the job completion time (JCT) in Data Center Networks (DCNs), where signals bounce off DC ceilings to establish wireless connections. The 3D scheme could alleviate hotspots in DCNs with flexible multigigabit wireless links, which bypasses the line-of-sight limitation of 60 GHz wireless links. However, the novel wireless transmission mechanism incurs significant change in the traditional interference model for wireless networks, and the theoretical analysis tool for such hybrid networks is still unavailable. This paper presents a theoretical framework for such 3D DCNs, where the entire network is first transformed from 3D to 2D by remodeling the 3D interference effects. The transformed graph is then processed with a multi-dimensional conflict graph methodology, where the wired and wireless sub-graphs induced by the DCN topology are jointly analyzed. The last but not least, the processed graph is modeled as a minimum job completion time (MJCT) problem, where the optimal traffic engineering, channel allocation and scheduling schemes in the original DCN can be obtained. Simulation results are presented to demonstrate the delay performance of our proposed approach.

In this paper, we propose a particle filter based tracker using multiscale mode to solve the scale changeable object tracking problem. Firstly, we discuss the traditional tracker's deficiency in dealing with scale changeable object and propose the multiscale mode. Then, we design our tracker under the particle filter framework using the proposed mode and "many to one" searching strategy. Finally, we compare our tracker with some existing trackers by tracking test on some video clips, which contain three categories of object scale change. Simulation results indicate that the proposed tracker distinctly improves both the tracking precision and efficiency. Compared with the traditional particle filter tracker, our tracker needs fewer particles and obtains more precise location and scale estimation.

In Cognitive Radio Networks (CRNs), Secondary Users (SUs) are provided with the flexibility of accessing Primary Users' (PUs') idle spectrum bands but the availability of spectra is dynamic due to PUs' uncertain activities of channel reclamation. In the multi-hop CRNs consisting of SUs as relays, such spectrum mobility will cause the invalidity of pre-determined routes of data flows since some of the channels pre-assigned to the routes become unavailable. In this paper, we investigate the spectrum-mobility-incurred route-switching problem in both spatial and frequency domains for CRNs, where the spatial switching determines which relays and links should be re-selected and the frequency switching decides which channels ought to be re-assigned to the spatial routes. We further formulate the route-switching problem as the Route-Switching Game which is shown to be a potential game and has a pure Nash Equilibrium (NE). Accordingly, an efficient algorithm for finding the NE is proposed. The proposed route-switching scheme not only avoids conflicts with PUs but also mitigates spectrum congestion. Meanwhile, tradeoffs between routing costs and channel switching costs are achieved.

The problem of jointly estimating clock skew and offset for wireless sensor networks (WSNs) in a pairwise broadcast synchronization (PBS) protocol is considered. The random part of the delay is supposed to be an exponential random variable. We consider two estimators, i.e., joint maximum-likelihood estimator (JMLE) and generalized ML-like estimator (GMLLE) proposed by Leng and Wu . For both estimators, the corresponding algorithms are explicitly derived and presented. For the GMLLE, the corresponding performance bound based on the reduced set of observations is derived and the optimal value of a user-defined parameter is identified accordingly. At last, analytical results are corroborated by numerical experiments. We observe that: (i) JMLE usually outperforms GMLLE at the cost of larger computational complexity; (ii) JMLE, while achieving the same estimation accuracy as that of the LP method presented in , enjoys significantly lower computational complexity than that of the latter.

Channel zapping time is a critical quality of experience (QoE) metric for IP-based video delivery systems such as IPTV. An interesting zapping acceleration scheme based on time-shifted subchannels (TSS) was recently proposed, which can ensure a zapping delay bound as well as maintain the picture quality during zapping. However, the behaviors of the TSS-based scheme have not been fully studied yet. Furthermore, the existing TSS-based implementation adopts the traditional IP multicast, which is not scalable for a large-scale distributed system. Corresponding to such issues, this paper makes contributions in two aspects. First, we resort to theoretical analysis to understand the fundamental properties of the TSS-based service model. We show that there exists an optimal subchannel data rate which minimizes the redundant traffic transmitted over subchannels. Moreover, we reveal a start-up effect, where the existing operation pattern in the TSS-based model could violate the zapping delay bound. With a solution proposed to resolve the start-up effect, we rigorously prove that a zapping delay bound equal to the subchannel time shift is guaranteed by the updated TSS-based model. Second, we propose a destination-oriented-multicast (DOM) assisted zapping acceleration (DAZA) scheme for a scalable TSS-based implementation, where a subscriber can seamlessly migrate from a subchannel to the main channel after zapping without any control message exchange over the network. Moreover, the subchannel selection in DAZA is independent of the zapping request signaling delay, resulting in improved robustness and reduced messaging overhead in a distributed environment. We implement DAZA in ns-2 and multicast an MPEG-4 video stream over a practical network topology. Extensive simulation results are presented to demonstrate the validity of our analysis and DAZA scheme.

Compressive sensing (CS) provides a new paradigm for efficient data gathering in wireless sensor networks (WSNs). In this paper, with the assumption that sensor data is sparse we apply the theory of CS to data gathering for a WSN where n nodes are randomly deployed. We investigate the fundamental limitation of data gathering with CS for both single-sink and multi-sink random networks under protocol interference model, in terms of capacity and delay. For the single-sink case, we present a simple scheme for data gathering with CS and derive the bounds of the data gathering capacity. We show that the proposed scheme can achieve the capacity Θ(\frac{nW}{M}) and the delay Θ(M\sqrtfrac{nlog n}), where W is the data rate on each link and M is the number of random projections required for reconstructing a snapshot. The results show that the proposed scheme can achieve a capacity gain of Θ (\frac{n}{M}) over the baseline transmission scheme and the delay can also be reduced by a factor of Θ(\fracsqrt{n\log n}{M}). For the multi-sink case, we consider the scenario where n_d sinks are present in the network and each sink collects one random projection from n_s randomly selected source nodes. We construct a simple architecture for multi-session data gathering with CS. We show that the per-session capacity of data gathering with CS is Θ(\frac{n\sqrt{n}W}{M n_d \sqrt{n_s \log n}}) and the per-session delay is Θ(M\sqrtfrac{{n}{log n}}). Finally, we validate our theoretical results for the scaling laws of the capacity in both single-sink and multi-sink networks through simulations.

While the spectrum resource of modern society is more and more insufficient, Cognitive Radio, which allows the Secondary Users (unlicensed users, SU) to access the licensed spectrum, is a promising solution to make the utilization of spectrum resource more efficient. Among many different paradigms of cognitive radio, market-driven spectrum trading has been proved to be an efficient way to deal with Cognitive Radio problems. In this paper, we consider the problem of spectrum trading with single primary user (PU) who has multiple spectra selling his idle spectra to multiple SUs in multiple types. Since there is only one PU, so it is a monopoly market, in which the PU sets the prices, powers and time for the spectrum he sells, just as a monopolist. SUs as customers choose the spectrum with exact price, power and time to buy. We model it as a two-dimensional power-time-price contract which is much different from the usual contract because the time could either be a strategy that an SU could decide to choose itself or a type which is not decided by SUs. We first discuss the situation in which the time is set as the strategy and we will prove that it can derive a feasible contract with some conditions. Then we will discuss the second situation in which the time is set as a type. In this situation, because the SU has two kinds of types, so it's difficult to make it become a feasible contract, however we will provide a solution to deal with this problem.

In this paper, we consider a general scenario where tphe wireless network is modeled as a rectangle with side lengths √n and n1/2−β, where 0 ≤ β ≤ 1/2 is a variance independent of n. n source-destination pairs are randomly located in the network, with their communication subject to the short-distance SNR, the long-distance SNR and the path loss. Based on these conditions, we identify the scaling laws of capacity for the network. The upper bound of the capacity is derived for the network, with the adoption of Multiple Input Multiple Output (MIMO) technology. Furthermore, we propose three different schemes, i.e., multihop, MIMO and hybrid schemes to achieve the upper bound. The capacity performance exhibits distinctive intriguing results as the side length of the network varies. Moreover, our results capture the impact of network shape on capacity and can unify the previous capacity results obtained in square networks.

Smart Grid integrates digital processing, sensor technology, automatic control and communication to the traditional power grid to achieve more efficient electricity distribution and management. Applying wireless sensor networks (WSNs) to Smart Grid can greatly facilitate the real-time information exchange within the power management system, and enable fast adaptation of the system to environmental changes. However, there are many challenges that need to be addressed for applying WSNs to the Smart Grid. One critical issue is how to receive data at the controller's node in a timely manner considering the typically time sensitive environment in Smart Grid and the limited battery power supply in WSNs. Based on data classification, this paper proposes a data transmission strategy in WSNs. After appropriate processing while collecting those large and complex data during aggregation, they are classified into different priority levels according to their various features. This classification process ensures the most important data which occupy a quite small percent of the total amount. The proposed cognitive transmission measure can guarantee a minimum delay for the most key data under the constraints of the electric-power-system environment and battery power supply. We offer simulation results to show the performance of the proposed cognitive transmission scheme.

Cognitive radio networks (CRN) is a promising technology for the efficient utilization of spectrum resource. However, how to dynamically allocate the spectrum combining with the characteristics of CRN is a crucial challenge for the mechanism design. In this paper, from the perspective of frequency-time characteristics of the spectrum opportunity in CRN and the secondary users' flexible requirements, we model the dynamic spectrum allocation problem as a knapsack problem and prove that maximizing the social welfare is NP-hard. Therefore, a spectrum auction mechanism is developed to approximate the optimal social welfare, and we prove its approximate ratio. Further, to guarantee the truthfulness, we devise a corresponding payment scheme. In simulation results, we show that no matter how many SUs are in the CRN, our scheme can approximate the maximal social welfare perfectly, and the spectrum utilization can be improved to over 96.5% or even larger.

Energy efficiency is the core issue in wireless sensor network's scheduling algorithm design, which calls up wide attention from researchers. However, few of the previous works notice the constraints on sensors caused by limited buffer size. It challenges the system's meeting deadlines performance. To alleviate this concern, we propose a scheduling policy: the least energy and free storage remaining first, which extends wireless sensor network's lifetime and provides a high real-time service's quality. To make our strategy more practical, we consider the user's non-cooperative behavior out of self-interests. It increases the system's packet lost in buffer overflow and reduces the energy efficiency. Based on a mixed strategy game model, we show that the non-cooperative game converges to an inefficient Nash Equilibrium, where the spectrum resource is significantly wasted. In order to eliminate user's selfish manner, we design a punishment scheme via a repeated game, which considers the time-variant character of network and can detect non-cooperative action precisely and rapidly.

In this paper, we study the multicast capacity of wireless ad hoc networks with infrastructure support. The network under study is termed as hybrid wireless network, where L-Maximum-Hop resource allocation strategy is adopted. There are n uniformly deployed normal wireless nodes and m regularly placed base stations dividing the network region into m cells. We show that the maximum capacity O(n1/2/k1/2(log n)1/2W1) + O(mW2) is achieved when the hop number L = Θ (n1/4/(k1/4(log n)3/4)) with the number of destinations k = O (a2/r2), where a is the side length of network region and r is transmission range of wireless terminals. This result provides a meaningful guide for the design of hybrid wireless networks. Moreover, we demonstrate that it is more efficient to adopt Infrastructure Mode than Ad Hoc Mode when k = Ω(a2/r2), because infrastructure nodes can cover the whole cell and broadcast to nodes more efficiently. In this case, maximum capacity is O (W1) + O(mW2), when L = Θ(1). Furthermore, we reveal that the per-node capacity does not vanish to zero only if the number of base stations m = Ω(n).

In this paper, we study the throughput-optimal control in the multi-radio multi-channel (MR-MC) wireless networks, which is particularly challenging due to the coupled link scheduling and channel/radio assignment. This paper has threefold contributions: 1) We develop a new model by transforming a network node into multiple node-radio-channel (NRC) tuples. Such modeling facilitates the development of a tuple-based back pressure algorithm, the solution of which can jointly solve the link scheduling, routing and channel/radio assignment in the MRMC network. 2) The tuple-based model enables the extensions of some well-known algorithms, e.g., greedy maximal scheduling and maximal scheduling, to MR-MC networks with guaranteed performance. We provide stability and capacity efficiency ratio analysis to the tuple-based scheduling algorithms. 3) The tuple-based framework facilitates a decomposable cross-layer formulation that enhances the delay performance of throughput-optimal control by integrating the link-layer scheduling with the network-layer path selection, where both hop-count and queuing delay are considered. Simulation results are presented to demonstrate the capacity region and delay performance of the proposed methodology, with comparison to the existing approach [3].

We study the capacity and delay scaling laws for cognitive radio network (CRN) with static primary users and heterogeneous mobile secondary users coexisting in the unit planar area. The primary network consists of n randomly and uniformly distributed static primary users (PUs) with higher priority to access the spectrum. The secondary network consists of m = (h + 1)n1+∈heterogeneous mobile secondary users (SUs) which should access the spectrum opportunistically, here h = O(log n) and ∈ > 0. Each secondary user moves within a circular area centered at its initial position with a restricted speed. The moving area of each mobile SU is n−α, where a is a random variable which follows the discrete uniform distribution with h + 1 different values, ranging from 0 to α0(α0> 0). α0and h together determine the mobility heterogeneity of secondary users. By allowing the secondary users to relay the packets for primary users, we have proposed a joint routing and scheduling scheme to fully utilize the mobility heterogeneity of secondary users. We show that the primary network and secondary network can achieve optimal capacity and delay scalings if we increase the mobility heterogeneity of secondary users, i.e., the value of h and α0, until h = Θ(log n) and α0≥ 1 + ∈. In this optimal condition, both the primary network and part of the secondary network can achieve almost constant capacity and delay scalings except for poly-logarithmic factor.

Recently, several Bloom filter based multicast schemes have been proposed, in which multicast routing information is carried with an in-packet Bloom filter. Since routers have no need to maintain forwarding states on a per-group basis, the Bloom filter based multicast protocols have desirable scalability. However, a critical issue is that these schemes may incur forwarding loops due to the false positive inherent in the Bloom filter. Existing solutions can only conditionally mitigate the probability of the forwarding loop, instead of fully preventing such events which (once occurred) will cause severe damage to the network. In this paper, we resolve this issue in the context of a destination-oriented multicast (DOM) scheme, a Bloom filter based multicast protocol carrying destinations IP addresses with the in-packet Bloom filter. With a theoretical analysis of the loop issue in DOM context developed, we reveal that the DOM design natively supports automatical elimination of permanent forwarding loops in all cases except a subtle one termed as conservation of bits. Based on the conclusion, we derive a probability upper bound on the loop occurrence in DOM. Furthermore, we propose an accurate tree branch pruning scheme, which equips the DOM the capability to completely and efficiently remove the false-positive forwarding loop. We present simulation results over a practical topology to demonstrate the performance of the loop mitigating DOM, with comparison to a representative Bloom filter based multicast scheme FRM and traditional IP multicast.

In this paper, we study data gathering with compressive sensing from the perspective of in-network computation in random networks, in which n nodes are uniformly and independently deployed in a unit square area. We formulate the problem of data gathering to compute multiround random linear function. We study the performance of in-network computation with compressive sensing in terms of energy consumption and latency in centralized and distributed fashions. For the centralized approach, we propose a tree-based protocol for computing multiround random linear function. The complexity of computation shows that the proposed protocol can save energy and reduce latency by a factor of Θ(√n= log n) for data gathering comparing with the traditional approach, respectively. For the distributed approach, we propose a gossip-based approach and study the performance of energy and latency through theoretical analysis. We show that our approach needs fewer transmissions than the scheme using randomized gossip.

A cognitive network refers to the one where two overlaid structures, called primary and secondary networks coexist. The primary network consists of primary nodes who are licensed spectrum users while the secondary network comprises unauthorized users that have to access the licensed spectrum opportunistically. In this paper, we study the percolation degree of the secondary network to achieve k-percolation in large scale cognitive radio networks. The percolation degree is defined as the number of nearest neighbors for each secondary user when there are at least k vertex-disjoint paths existing between any two secondary relays in the percolated cluster. The percolated cluster is formed when there are an infinite number of mutually connected secondary users spanning the whole network. Each user in the cluster is possibly connected to several neighbors, inducing more communication links between any two of them. Since nodes located near the boundary have fewer neighbors, the boundary effect becomes a bottleneck in determining the percolation degree. For cognitive networks, when the primary node density becomes considerably large, the boundary effect spreads inside the network. The transmission area of most secondary users who are located near the primary nodes decreases due to the restriction of the primary network. Therefore, to ensure k-connectivity in the percolated cluster, each secondary user must be connected to more neighbors, and the percolation degree of the secondary network yields a function of the primary node density. We specify the relationship into three regimes regarding the topology variation of the cognitive network. A closed-form expression of the percolation degree under different primary node densities is presented. The expression characterizes the connectivity strength in the secondary percolated cluster, therefore providing analytical insight on fault tolerance improvement in cognitive networks.

Vehicular Ad Hoc Networks (VANETs) with base stations are called hybrid VANET, where base stations are deployed to improve the throughput capacity. In this paper, we study the multicast throughput capacity for hybrid wireless VANET with a directional antenna on each vehicle and the end-to-end delay is constrained. In the hybrid VANET, there are n mobile vehicles (or nodes) distributed in a unit area with m strategically deployed base stations connected using high-bandwidth wire links. There are n_s multicast sessions and each multicast session has one source which transmits identical data to its associated p destinations. We investigate the multicast throughput capacity for two mobility models with two mobility scales, respectively, while each vehicular node is equipped with a directional antenna and with a tolerant delay D. That is, a source node transmits to its p destinations only with the help of normal nodes within D consecutive time slots. Otherwise, the transmission will be performed with in the infrastructure mode, i.e., with the help of base stations. We demonstrate that the one dimensional i.i.d. slow mobility pattern catch the main feature of VANETs. And we find that the multicast throughput capacity of the hybrid wireless VANET greatly depends on the delay constraint D, the number of base stations m, and the beamwidth of directional antenna θ. In the order of magnitude, we obtain the closed form of the multicast throughput capacity of the hybrid directional VANET, where the impact of D, m and θ on the multicast throughput capacity is analyzed. Moreover, we derive the lower bound of the muticast throughput using a similar raptor coding approach.

In this paper, we define an ad hoc network where multiple sources transmit packets to one destination as Converge-Cast network. We will study the capacity delay tradeoffs assuming that n wireless nodes are deployed in a unit square. For each session (the session is a dataflow from k different source nodes to 1 destination node), k nodes are randomly selected as active sources and each transmits one packet to a particular destination node, which is also randomly selected. We first consider the stationary case, where capacity is mainly discussed and delay is entirely dependent on the average number of hops. We find that the per-node capacity is Θ (1/√(n log n)) (given nonnegative functions f(n) and g(n): f(n) = O(g(n)) means there exist positive constants c and m such that f(n) ≤ cg(n) for all n ≥ m; f(n)= Ω (g(n)) means there exist positive constants c and m such that f(n) ≥ cg(n) for all n ≥ m; f(n) = Θ (g(n)) means that both f(n) = Ω (g(n)) and f(n) = O(g(n)) hold), which is the same as that of unicast, presented in (Gupta and Kumar, 2000). Then, node mobility is introduced to increase network capacity, for which our study is performed in two steps. The first step is to establish the delay in single-session transmission. We find that the delay is Θ (n log k) under 1-hop strategy, and Θ (n log k/m) under 2-hop redundant strategy, where m denotes the number of replicas for each packet. The second step is to find delay and capacity in multisession transmission. We reveal that the per-node capacity and delay for 2-hop nonredundancy strategy are Θ (1) and Θ (n log k), respectively. The optimal delay is Θ (√(n log k)+k) with redundancy, corresponding to a capacity of Θ (√((1/n log k) + (k/n log k)). Therefore, we obtain that the capacity delay tradeoff satisfies delay/rate ≥ Θ (n log k) for both strategies.

Game theory has made great contributions to Cognitive Radio (CR) algorithm design and it calls up wide interest of CR researchers, while the considerable iterations and the operation complexity would hinder its implementation. And the evaluation under practical environment is an indispensable step before its application. To alleviate the concerns, we propose a distributed relay selection algorithm based on Stackelberg Game and overcome the challenges caused by the high calculation complexity to make our strategy applicable. Moreover, we utilize a real time testbed to evaluate the CR MAC algorithm, which contains the necessary components: programmable RF layer, software-defined MAC layer and adaptive network layer to fulfill the requirements of CR research. It is based on system-on-chip processors with strong configure ability which can develop large scale network. We make experiments on our platform to reveal the game equilibrium's properties and verify our scheme's performance in improving the capacity at the destination and secondary user's spectrum utilization efficiency.

Compressive sensing (CS) provides a new paradigm for efficient data gathering in wireless sensor networks (WSNs). The theory of CS allows to reconstruct all sensor data of the network, while only collecting a small number of measurements at a sink. In this paper, we consider a scenario where a sink collects spatially correlated sensor data from n sensor nodes randomly deployed in a region. We investigate the fundamental limitation of data gathering with CS in such a scenario, in terms of capacity and delay. We construct a scheduling and routing scheme based on CS for data gathering in WSNs. We show that the proposed scheme can achieve a per-node transport capacity of Θ(1/ log n) under physical interference model. Furthermore, we also study the delay performance of the proposed scheme and show that the delay for collecting a snapshot with CS is Θ(√n log n). In particular, our results demonstrate that the proposed scheme can achieve a capacity gain of Θ(n/log n) over the case without CS and the delay can also be reduced by a factor of Θ(√n/log n).

How to design the next-generation Internet is an open technique issue. One of the mainstream ideas is to enhance network routers with application-oriented intelligence. For example, firewalls,Web proxies/caches, mobile gateways, and multicast capable nodes are equipments with application-oriented intelligence for security/performance enhancement. However, there is no systematic study on what intelligence should be incorporated into the router and what the fundamental benefit of the application-oriented networking is. This paper presents a generic application-oriented networking (GAON) simulation framework compatible with the Network Simulator ns-2 to facilitate the research in the area. With GAON, developers can conveniently enhance the ns-2 nodes with customized functionalities, and seamlessly incorporate them into the regular ns-2 system. GAON provides a generic scenario control interface, through which ns- 2 users can flexibly load/unload customized GAON processing agents on network nodes. The regular ns-2 node structure is extended, where a GAON agent classifier is set up to dispatch GAON traffic to correct GAON agents. Moreover, a unified interface to the ns-2 built-in routing table is developed to facilitate GAON agents forwarding packets. Two multicast protocols are implemented to demonstrate the validation of GAON, with the simulation results presented.

In this paper, we propose an agent-based spectrum trading model, where an agent can play a third-party role in the spectrum trading process. Providing service to Secondary Users (SUs) with spectrum bought from Primary Users (PUs), the agent can make profits during the process by providing service to secondary users. During each trading period, the agent has to decide how much spectrum it should lease from PUs and what price it should charge SUs. Therefore, the most significant challenge to implement this spectrum trading model is finding the most profitable strategy for agent(s). We address this challenge under two scenarios in which: 1) a single agent and 2) multiple agents. Instead of quantifying SUs' spectrum demand by a deterministic function of price, we take the randomness of secondary users' demand or demand uncertainty into consideration. To the best of our knowledge, this is the first solution to agent-based spectrum trading considering demand uncertainty.

Most multicast routing protocols construct reverse shortest path trees (SPTs) to deliver shared data. However, the use of the reverse SPT presents a challenge in the inter-domain routing environment, as the path from the source to a receiver could be asymmetric to the one used to go from the receiver to the source. A possible approach is to utilize the round-trip joining message but it incurs the demerit of long joining delay. In this paper, we propose a BGP-view based fast-join (BFJ) mechanism, where the receiver domain border router leverages the BGP routing information in the border router of the source domain to identify an efficient joining path. The initial joining message can then be delivered using source routing along the identified path to quickly construct the reverse SPT even in asymmetric routing environment. Subsequent joining messages temporarily label corresponding interfaces at intermediate routers so that requested data packets are steered to the subscriber as soon as possible. The NS2 simulation results show that the proposed BFJ scheme is more efficient than the approach of round-trip joining message even under the favored condition of the latter.

In this paper, we propose a full-hardware context-based adaptive binary arithmetic coder (CABAC) encoder, which is the entropy coding tool adopted in the main and higher profiles of the video coding standard H.264/AVC. All CABAC coding features are implemented in hardware (HW), and different coding modes including rate-distortion optimization (RDO) are fully supported. An efficient memory access scheme is also proposed to reduce context RAM access frequency, context RAM size, and operation delay for RDO context state backup and restoration. Constant throughput of 1 bin per cycle is achieved in different coding configurations. The CABAC encoder is physically implemented in 0.13 mum process, and its post-layout simulation can be run at 328 MHz. The chip takes up 1.41 mm2, and dissipates 0.79 mW to support 720p60 HDTV real-time encoding in RDO-off mode. Compared to the state-of-the-art reference design, the most significant advantage of this design is that a full HW implementation of CABAC encoder is proposed, which minimizes the computation on the host processor and data transfer on the system bus. Power consumption is minimal compared to reference designs using the same technology.

This paper develops an efficient and scalable multicast scheme for high-quality multimedia distribution. The traditional IP multicast, a pure network-layer solution, is bandwidth efficient in data delivery but not scalable in managing the multicast tree. The more recent overlay multicast establishes the data-dissemination structure at the application layer; however, it induces redundant traffic at the network layer. We propose an application-oriented multicast (AOM) protocol, which exploits the application-network cross-layer design. With AOM, each packet carries explicit destinations information, instead of an implicit group address, to facilitate the multicast data delivery; each router leverages the unicast IP routing table to determine necessary multicast copies and next-hop interfaces. In our design, all the multicast membership and addressing information traversing the network is encoded with bloom filters for low storage and bandwidth overhead. We theoretically prove that the AOM service model is loop-free and incurs no redundant traffic. The false positive performance of the bloom filter implementation is also analyzed. Moreover, we show that the AOM protocol is a generic design, applicable for both intra-domain and inter-domain scenarios with either symmetric or asymmetric routing.

This paper proposes an efficient and scalable multicast scheme based on the concept of application-oriented networking (AON). The traditional IP multicast is bandwidth efficient but suffers from the scalability problem. The overlay multicast, proposed in recent decade, manages a data-dissemination tree at the application layer, and only utilizes unicasts among pairs of hosts; the overlay approach, however, usually incurs a considerable amount of redundant traffic. The essence of AON is to integrate application intelligence into the network. For AON-based multicasting, each packet will carry necessary explicit addressing information, instead of an implicit class-D group address, to facilitate the multicast data delivery. Each AON router will leverage the unicast IP routing table to compute necessary multicast copies and next-hop interfaces. The proposed AON multicast eliminates the need for constructing and maintaining the network-layer multicast routing table, while its bandwidth efficiency is very close to that of the IP multicast.

In this paper, we propose a hardware Context-based Binary Arithmetic Coder (CABAC) targeting the main profile of H.264/AVC standard. The encoder fully supports different coding modes including RDO coding. An efficient memory access scheme is proposed and shown to reduce context memory access rate, context memory size, and RDO context state backup and restore operation delay. Coding throughput of 1 bin/cycle is achieved by pipelined structure. The encoder is physical implemented with 362 MHz clock frequency and power reduction technique is also utilized.

The current trends in service consolidation over Internet Protocol (IP) also stimulates the integration of the industrial automation system with the information technology (IT) infrastructure for more efficient information access and more cost-effective production and management. Field buses have been the de facto communication standard in industrial automation, but mostly based on manufacture-specific protocols. Thus, the interoperability between the manufacturer-specific field bus systems and the external operating environment is the critical factor in enabling the networked industrial automation systems. However, most of the existing field bus integration solutions lack either flexibility or scalability. In this paper, we propose a service-oriented architecture (SOA) based field bus integration architecture (SOAFBIA), where each field bus system is encapsulated with optional interface, manageability interface, and semantic descriptions in a standard format to facilitate interoperability. Moreover, a resource agent is proposed as an enhanced service broker, which implements not only the standard service registry functionality in SOA, but also the resource management functions including admission control, service scheduling, and load balancing.