Real-time imaging sonar is crucial for underwater monitoring where optical sensing fails, but its use is limited by low uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) affecting up to 98% of frames. We present SCOPE, a self-supervised framework that jointly performs compression and artifact correction without clean–noise pairs or synthetic assumptions. SCOPE combines (i) Adaptive Codebook Compression (ACC), which learns frequency-encoded latent representations tailored to imaging sonar, with (ii) Frequency-Aware Multiscale Segmentation (FAMS), which decomposes frames into low-frequency structure and sparse high-frequency dynamics while suppressing rapidly fluctuating artifacts. A hedging training strategy further guides frequency-aware learning using low-pass proxy pairs generated without labels. Evaluated on months of in-situ ARIS sonar data, SCOPE achieves a structural similarity index (SSIM) of 0.77, representing a 40% improvement over prior self-supervised denoising baselines, at bitrates down to ≤ 0.0118 bpp. It reduces uplink bandwidth by more than 80% while improving downstream detection. The system runs in real time, with 3.1 ms encoding on an embedded GPU and 97 ms full multi-layer decoding on the server end. SCOPE has been deployed for months in three Pacific Northwest rivers to support real-time salmon enumeration and environmental monitoring in the wild. Results demonstrate that learning frequency-structured latents enables practical, low-bitrate sonar streaming with preserved signal details under real-world deployment conditions.

WiFi-based passive indoor localization has gained prominence owing to its high accuracy and ease of deployment in GPS-denied environments. However, Channel State Information (CSI)-based systems face challenges, including high data acquisition requirements, significant computational overhead, and limited transferability. In this paper, we introduce DBLoc, a WiFi localization system that leverages beamforming feedback information (BFI), a novel attribute provided by modern WiFi hardware. BFI’s clear-text transmission and stable characteristics make it an ideal choice for localization tasks. We prove that BFI provides a lightweight alternative to CSI, significantly reducing both data acquisition and storage requirements. Compared with traditional deep learning frameworks using convolutional networks, DBLoc employs a pruning-based residual architecture to reduce computational overhead, achieving an inference cost of only 175.7 MFLOPs, thus optimizing performance within an edge-deployment budget. To enable transferability that surpasses current meta-learning approaches, DBLoc incorporates a virtual-domain-based meta-learning algorithm, ensuring robust performance with minimal target-domain data. Additionally, a spatial-encryption mechanism is proposed to safeguard the BFI-based model from eavesdropping. Extensive evaluations demonstrate that DBLoc achieves a median localization error of approximately 0.5 m, while significantly reducing localization accuracy for unauthorized attackers.

Satellite networks are believed to become an indispensable component in the forthcoming 6G network and beyond. The surging demands attract numerous satellite network operators into this market to compete, yet also cooperate via resource sharing for cost and performance improvement, which is similar to the growth trajectory of how the Internet becomes the network of networks. Hence, we envision a federated network of satellite networks (shortened as federated satellite network) in this paper, where satellite network operators will eventually federate with each other to achieve a win-win situation. However, the yet-to-come federated satellite network faces two unique challenges: sustainability and dynamic topology. As such, we propose a sustainable and service-differentiable framework named Federated Cross-orbit Satellite Network (FCSN). Different from most existing solutions which focused on the Internet or simple cooperation among satellites, the FCSN orchestrates network resources in the dynamic topology to improve sustainability, through service-differentiable offloading in the resource-limited scenario. We formulate the sustainability-oriented federated offloading problem based on the utility and cost models tailored for the FCSN and propose an efficient hardware-budget constrained auction algorithm with a bounded approximation ratio. Finally, we design a truthful and rational payment scheme to motivate the construction of the FCSN. Extensive simulation results based on real-world deployments show that our solution significantly improves sustainability and delay, making it one step further toward the vision of the federated network of satellite networks.

Sonar radar captures visual representations of underwater objects and structures using sound wave reflections, making it essential for exploration, mapping, and continuous surveillance in wild ecosystems. Real-time analysis of sonar data is crucial for time-sensitive applications, including environmental anomaly detection and in-season fishery management, where rapid decision-making is needed. However, the lack of both relevant datasets andpre-trained DNN models, coupled with resource limitations in wild environments, hinders the effective deployment and continuous operation of live sonar analytics.

Driven by the large amount of spatio-temporal data obtained from location-based social networks, the implementation of cross-domain user linkage, also known as the User Identity Linkage (UIL), has attracted increasing research attentions. While most of the existing UIL works discretize the spatio-temporal sparse data when identifying encountering or co-located events for UIL, user’s distinctive behavior patterns implicit in the “check-in” spatio-temporal data with continuous nature pave the way for enhancing UIL performance. In this paper, we propose an approach dubbedCP-Linkthat exploits user behavior patterns in a continuous way. In CP-Link, the continuous space is divided into irregularly shaped stay regions, and a continuous time-based improved dynamic time warping (IDTW) method is proposed to calculate the similarity. To bridge the gap between the ideal scenario with ample records and the reality with sparse data, we adopt the user-associated location frequent pattern (LFP) model to compensate for the sparse deficiency. Extensive experiments conducted on real-world datasets demonstrate the effectiveness and superiority of CP-Link, which outperforms the state of the arts by more than 20% in terms of the AUC.

For the recent two years, companies such as Starlink, Kuiper, and Telesat are launching low earth orbit (LEO) satellites to form LEO satellite mega-constellations. Unfortunately, the LEO satellite mega-constellations are not sustainable in the long term since their large size makes LEO congested, causing issues such as satellite brightness, satellite conjunction, and space debris. The issues become worse as LEO satellites have shorter battery lifespans and experience drag force, which shortens the satellite life and produces more space debris objects when LEO satellites reach the end of life. To address the issues, we propose deploying higher-orbit satellites to form a satellite-based sustainable multi-tier space network (SMTSN) instead of launching a massive number of LEO satellites. In this paper, we model the costs and gains for routing traffic with our SMTSN framework. We propose a solution to find the optimal routing paths and an efficient distributed coverage-aware (EDCA) algorithm to predict the number of skipped LEO satellites when the traffic is routed through a higher-orbit satellite. We run extensive simulations to compare the LEO satellite constellations with and without our SMTSN framework, and the results show a significant improvement in the battery cell cycle life consumption with the SMTSN framework.

In recent years, data has become the new oil that fuels various machine learning (ML) applications. Just as the oil refining, providing data to an ML model is a product of massive costs and expertise efforts. However, how to protect the intellectual property (IP) of the training data in ML remains largely open. In this paper, we present MeFA, a novel framework for detecting training data IP embezzlement via Membership Fingerprint Authentication, which is able to determine whether a suspect ML model is trained on the to be protected target data or not. The key observation is that a part of data has a similar influence on the prediction behavior of different ML models. On this basis, MeFA leverages membership inference techniques to extract these data as the fingerprints of the target data and constructs an authentication model to verify the data’s ownership by identifying the obtained membership fingerprints. MeFA has several salient features. It does not assume any knowledge of the suspect model except for its black-box prediction API, through which we can merely get the prediction output of a given input, and also does not require any modification to the dataset or the training process, since it takes advantage of the inherent membership property of the data. As a by-product, MeFA can also serve as a post-protection to verify the ownership of ML models, without modifying the training process of the model. Extensive experiments on three realistic datasets and seven types of ML models validate the effectiveness of MeFA, and demonstrate that it is also robust to scenarios when the training data is partially used or preprocessed with representative membership inference defenses.

Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning method-ology that can eliminate the influence of a federated client’s data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server’s storage for unlearned model’s construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of 4× compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.

Virtualization has attained mainstream status in enterprise IT industry. Despite its widespread adoption, it is known that virtualization also introduces non-trivial overhead when tasks are executed on a virtual machine (VM). In particular, a combined effect from device virtualization overhead and CPU scheduling latency can cause performance degradation when computation intensive tasks and I/O intensive tasks are co-located on a VM. Such an interference also causes extra energy consumption. In this paper, we present Hylics, a novel solution that enables efficient data traverse paths for both I/O and computation intensive workloads. This is achieved with the provision of in-memory file system and network service at the hypervisor level. Several important design issues are pinpointed and addressed during our prototype implementation, including efficient intermediate data sharing, network service offloading, and QoS-aware memory usage management. Based on our real-world deployment on KVM, we show that Hylics can significantly improve computation and I/O performance for hybrid workloads. Moreover, this design also alleviates the existing virtualization overhead and naturally optimizes the overall energy efficiency.

The explosion of online shopping brings great challenges to traditional logistics industry, where the massive parcels and tight delivery deadline impose a large cost on the delivery process, in particular the last mile parcel delivery. On the other hand, modern cities never lack transportation resources such as the private car trips. Motivated by these observations, we propose a novel and effective last mile parcel delivery mechanism through car trip sharing, to leverage the available private car trips to incidentally deliver parcels during their original trips. To achieve this, the major challenges lie in how to accurately estimate the parcel delivery trip cost and assign proper tasks to suitable car trips to maximize the overall performance. To this end, we develop Car4Pac, an intelligent last mile parcel delivery system to address these challenges. Leveraging the real-world massive car trip trajectories, we first build up a 3D (time-dependent, driver-dependent and vehicle-dependent) landmark graph that accurately predicts the travel time and fuel consumption of each road segment. Our prediction method considers not only traffic conditions of different times, but also driving skills of different people and fuel efficiencies of different vehicles. We then develop a two-stage solution towards the parcel delivery task assignment, which is optimal for one-to-one assignment and yields high-quality results for many-to-one assignment. Our extensive real-world trace driven evaluations further demonstrate the superiority of our Car4Pac solution.

Indoor localization has witnessed a rapid development in the past few decades. Tremendous solutions have been put forwarded in the literature and the localization accuracy has reach an unprecedent centimeter-level. Among the available approaches, acoustic-enabled solutions have attracted much attention. They customarily achieve decimeter-level localization accuracy with affordable infrastructure costs. However, there still exist several open issues for the acoustic-based approaches which prohibit their wide-scale adoptions. First, although extra infrastructures (i.e., beacons) are economical, deployment, and maintenance can incur excessive labor cost. Second, current approaches have much latency to obtain a location fix, making it infeasible for mobile target tracking. Third, the localization performance of current solutions degrades easily by the near-far problem, multipath effect, and device diversity. To address these issues, this paper presents an asynchronous acoustic-based localization system with participatory sensing. We leverage the collaborative efforts of the participatory users who are relatively stationary in indoor environments as virtual anchors (VAs) to eliminate the predeployment and post-maintenance costs incurred in traditional anchor-based solutions. To mitigate the latency to obtain a location fix, we design an orthogonal ranging mechanism to enable concurrent beacon message transmission, which is $2\boldsymbol \times $ faster than previous work in obtaining a location fix. Moreover, we propose a robust method to address the near-far problem and device diversity, and we conquer the multipath problem via a genetic algorithm-based approach. Our VA-based system is self-deployable, cost-effective, and robust to environmental dynamics. We have implemented and evaluated a system prototype, demonstrating a median accuracy of 0.98 m in typical indoor settings.

The enabling Internet-of-Things technology has inspired many innovative sensing mechanisms by repurposing the onboard sensors. Leveraging the built-in acoustic sensors for ranging is among one of the interesting applications. However, among the few studies on acoustic ranging, the one-way sensing method suffers from synchronization errors and requires cumbersome kernel modifications; the other two-way approaches overcome these shortcomings, but they are sensitive to system delays. In this case, this paper proposes a novel lightweight one-way sensing paradigm without the above drawbacks. The key insight of this paper is to perform ranging by estimating the propagation time of acoustic signals via linear frequency modulation signal mixing. Such a signal mix operation can translate range estimation into fine-grain frequency estimation, thereby enhancing ranging accuracy. In addition, our system can have multiple receivers co-exist and thus the measurement dimensions are boosted. We have implemented and evaluated our system prototype in real-world settings. The prototype demonstrated centimeter-level ranging performance.

In recent decades, unmanned aerial vehicles (UAVs, also known as drones) equipped with multiple sensors have been widely utilized in various applications. Nevertheless, constrained by limited battery capacities, the hovering time of UAVs is quite limited, prohibiting them from serving a wide area. To cater with remote sensing applications, people often employ vehicles to transport, launch, and recycle them. The so-called vehicle-drone cooperation (VDC) benefits from both the far driving distance of vehicles and the high mobility of UAVs. Efficient routing and scheduling can greatly reduce time consumption and financial expenses incurred in VDC. However, previous works in vehicle-drone cooperative sensing considered only one drone, thus unable to simultaneously cover multiple targets distributed in an area. Using multiple drones to sense different targets in parallel can significantly promote efficiency and expand service areas. Therefore, we propose a novel problem, referred to as vehicle-assisted multidrone routing and scheduling problem. To tackle the problem, we contribute an efficient algorithm, referred to as vehicle-assisted multi-UAV routing and scheduling algorithm (VURA). In VURA, we maintain and iteratively update a memory containing candidate UAV routes. VURA works by iteratively deriving solutions based on UAV routes picked from the memory. In every iteration, VURA jointly optimizes anchor point selection, path planning, and tour assignment via nested optimization operations. To the best of our knowledge, we are the first to tackle this novel yet challenging problem. Finally, performance evaluation is presented to demonstrate the effectiveness and efficiency of our algorithm when compared with existing solutions.

The ubiquitous luminaries provide a new dimension for indoor navigation, as they are often well-structured and the visible light is reliable for its multipath-free nature. However, existing visible light-based technologies, which are generally frequency-based, require the modulation on light sources, modification to the device, or mounting extra devices. The combination of the cost-extensive floor map and the localization system with constraints on customized hardwares for capturing the flashing frequencies, no doubt, hinders the deployment of indoor navigation systems at scale in, nowadays, smart cities. In this paper, we provide a new perspective of indoor navigation on top of the virtual graph representation. The main idea of our proposed navigation system, named PILOT, stems from exploiting the peak intensities of ubiquitous unmodulated luminaries. In PILOT, the pedestrian paths with enriched sensory data are organically integrated to derive a meaningful graph, where each vertex corresponds to a light source and pairwise adjacent vertices (or light sources) form an edge with a computed length and direction. The graph, then, serves as a global reference frame for indoor navigation while avoiding the usage of pre-deployed floor maps, localization systems, or additional hardwares. We have implemented a prototype of PILOT on the Android platform, and extensive experiments in typical indoor environments demonstrate its effectiveness and efficiency.

Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users' channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.

Edge computing technology can greatly reduce the network load and the user response delay, which can effectively make up the defect of the cloud computing. However, edge storage nodes have much smaller space than cloud computing, therefore it is necessary to select an appropriate cache replacement policy to replace data that some users do not frequently request in order to cache the newly accessed data. This paper focuses on the problem of data replacement and the corresponding replacement strategy when the edge storage node lacks buffer space. Maximum file utility of system (Max-FUS) caching replacement algorithm are proposed to solve this problem. Finally, the edge computing system model, which is established in order to verify the feasibility of the algorithm by MATLAB simulation experiment, shows the Max-FUS can achieve better performance than the existing methods.

Deep learning has been applied in many recent advanced applications in the field of transportation, finance and medicine. These applications require significant computation resources and large-scale training samples. Cloud becomes a natural choice for conducting these learning tasks due to its abundant resources. However, deeper penetration of deep learning techniques in mission critical applications, like driverless car, calls for stricter time requirement to guarantee its interaction and larger amount of dataset for training to guarantee its accuracy, which cannot be easily satisfied by the cloud and makes the network transmission become the bottleneck. Edge learning emerges to be a promising direction to reduce data transmission time by processing and compressing the raw data at the edge of the network, while brings the concern of accuracy reduction at the meantime. To balance this tradeoff under cloud-edge architecture, we study a task scheduling problem for reducing weighted transmission time which takes learning accuracy into consideration. We also propose efficient scheduling algorithms which are able to achieve up to 50% reduction in makespan with extensive trace-driven simulations.

Recently, there is a growing emphasis on device-to-device (D2D) communication, which is the key component of the Internet-of-Things ecosystem. D2D communication can operate on the licensed spectrum of cellular networks so as to improve the spectrum utilization. In this paper, we focus on the resource allocation problem for general multihop D2D communication and introduce users' mobility into D2D communication underlaying cellular networks. Maximizing the total end-to-end data rate involves complex tasks, such as resource allocation and routing. By leveraging on the square tessellation technique, we propose an efficient square-division-based resource allocation scheme. Furthermore, we design a relay-assisted D2D communication protocol that addresses the challenges in enabling multihop D2D communications, namely, spectrum resource allocation, users' mobility, and relay incentive. Through extensive simulations, we show that our relay-assisted D2D communication protocol improves the system throughput up to 55% and the user access rate up to four times in typical scenarios, as compared with state-of-the-art schemes.

MapReduce effectively partitions and distributes computation workloads to a cluster of servers, facilitating today's big data processing. Given the massive data to be dispatched, and the intermediate results to be collected and aggregated, there have been a significant studies on data locality that seeks to co-locate computation with data, so as to reduce cross-server traffic in MapReduce. They generally assume that the input data have little dependency with each other, which however is not necessarily true for that of many real-world applications, and we show strong evidence that the finishing time of MapReduce tasks can be greatly prolonged with such data dependency. In this paper, we present Dependency-Aware Locality for MapReduce (DALM) for processing the real-world input data that can be highly skewed and dependent. DALM accommodates data-dependency in a data-locality framework, organically synthesizing the key components from data reorganization, replication, placement. Beside algorithmic design within the framework, we have also closely examined the deployment challenges, particularly in public virtualized cloud environments, and have implemented DALM on Hadoop 1.2.1 with Giraph 1.0.0. Its performance has been evaluated through both simulations and real-world experiments, and compared with that of state-of-the-art solutions.

With the prevalence of broadband network and wireless mobile network accesses, distributed interactive applications (DIAs) such as online gaming have attracted a vast number of users over the Internet. The deployment of these systems, however, comes with peculiar hardware/software requirements on the user consoles. Recently, such industrial pioneers as Gaikai, Onlive, and Ciinow have offered a new generation of cloud-based DIAs (CDIAs), which shifts the necessary computing loads to cloud platforms and largely relieves the pressure on individual user's consoles. In this paper, we aim to understand the existing CDIA framework and highlight its design challenges. Our measurement reveals the inside structures as well as the operations of real CDIA systems and identifies the critical role of cloud proxies. While its design makes effective use of cloud resources to mitigate client's workloads, it may also significantly increase the interaction latency among clients if not carefully handled. Besides the extra network latency caused by the cloud proxy involvement, we find that computation-intensive tasks (e.g., game video encoding) and bandwidth-intensive tasks (e.g., streaming the game screens to clients) together create a severe bottleneck in CDIA. Our experiment indicates that when the cloud proxies are virtual machines (VMs) in the cloud, the computation-intensive and bandwidth-intensive tasks may seriously interfere with each other. We accordingly capture this feature in our model and present an interference-aware solution. This solution not only smartly allocates workloads but also dynamically assigns capacities across VMs based on their arrival/departure patterns.

The state-of-the-art cloud computing platforms are facing challenges, such as the high volume of crowdsourced data traffic and highly computational demands, involved in typical deep learning applications. More recently, Edge Computing has been recently proposed as an effective way to reduce the resource consumption. In this paper, we propose an edge learning framework by introducing the concept of edge computing and demonstrate the superiority of our framework on reducing the network traffic and running time.

Device-to-device (D2D) communication has been recently proposed to mitigate the burden of base stations by leveraging the underutilized cellular spectrum resources, where high overall network throughput and D2D access rate are critical for its service performance and availability. In this paper, we study the resource allocation problem to push the limit of D2D communication underlaying cellular networks by allowing multiple D2D links to share resource with multiple cellular links. We propose FRESH, afullresourcesharing scheme where each subchannel can be shared by a cellular link and an arbitrary number of D2D links. In particular, FRESH first divides the communication links into so-called full resource sharing sets such that, within each set, all D2D link members are able to reuse the whole allocated resources. Thereafter, it allocates a sum of spectrum resources to each obtained full resource sharing set. As compared with state-of-the-art schemes, FRESH provides fine-grained resource allocation, resulting in throughput improvements of up to one order of magnitude, and D2D access rate improvements of up to 5 times with a moderate node density (e.g., on the order of 1 user per 400 square meters).

Recent years have witnessed the rapid growth of new live broadcast services, represented by Twitch.tv and YouTube live events, where videos are crowdsourced from amateur users (e.g., game players), rather than from commercial and professional TV broadcaster or content providers. The viewers also actively contribute to the content through embedded open-chat channels. Such community interactions among viewers, or even between broadcasters and viewers, make content generation highly diversified and engaging, particularly for the young generation. In this context, cross-viewer synchronization is highly desirable; otherwise the viewers with shorter broadcast latency may act as spoilers, significantly affecting the user experience of other viewers. In this paper, we show that the end-to-end delay has a dramatically amplified impact on the broadcast latency for individual viewers. We suggest smart rate adaptation to achieve cross-viewer synchronization, and develop distributed algorithms based on dual decomposition. We further extend our solution to the cloud environment, and present the concept of ShadowCast, which moves broadcasters to the cloud to provide high-quality streams beyond broadcasters' network bandwidth constraint. Its practicability and effectiveness is demonstrated by our implementation and test bed experiments.

Internet of Things (IoT) is one of the hottest research fields nowadays and has attracted huge interests and research efforts from both academia and industry.IoT can connect a large number of sensors, actuators, devices, vehicles, buildings, and/or other objects to form a network where data can be collected from the physical world, exchanged and processed in the cyber world, and then fed back into the physical world through actuations.This makes IoT one of the key foundations towards the vision of smart cities, with many promising applications such as environmental monitoring, infrastructure management, manufacturing, energy management, medical and healthcare, building and home automation, and transportation.Recently, the advances in various wireless communication protocols in technologies such as 5G, RFID, Wi-Fi-Direct, Li-Fi, LTE, and 6LoWPAN have greatly boosted the potential capabilities of IoT and made it become more prevalent than ever, which also accelerate the further integration of IoT with emerging technologies in other areas such as sensing, wireless recharging, data exchanging, and processing.Yet, how these technologies especially the corresponding wireless communication protocols can be well aligned with IoT to maximize their benefits on such performance as scalability, service quality, energy efficiency, and cost effectiveness is still open to investigation and thus calls for novel solutions.And the involved privacy and security issues also need to be carefully examined and addressed.This special issue aims to summarize the latest development in wireless communication protocols for Internet of

We study the energy harvesting problem in the Internet of Things with heterogeneous users, where there are three types of single-antenna users: ID users that only receive information, EH users that can only receive energy, and ID/EH users that receive information and energy simultaneously from a multiantenna base station via power splitting. We aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the ID users and ID/EH users by jointly designing the power allocation at the transmitter and the power splitting strategy at the ID/EH receivers under the maximum transmit power and the minimum energy harvesting constraints. Specifically, we first apply the semidefinite relaxation (SDR), zero-forcing (ZF), and maximum ratio transmission (MRT) techniques to solve the nonconvex problems. We then apply the zero-forcing dirty paper coding (ZF-DPC) technique to eliminate the multiuser interference and derive the closed-form optimal solution. Numerical results show that ZF-DPC provides higher achievable minimum SINR than SDR and ZF in most cases.

Virtualization has become a building block for modern IT industry, and many datacenters are now highly virtualized. It is known that virtualization also introduces non-trivial overhead, which can cause severe self-interference inside a VM when CPU intensive tasks and bandwidth intensive tasks are co-located. Energy efficiency of the server can be affected as well. While such overhead is well-studied in application/protocol specific context, a more comprehensive solution is yet to be explored for general cloud services. In this paper, we present MemNet, a novel protocol-independent solution that enables para-virtualized memory sharing between host and guest VMs. This design successfully decouples I/O and computation operations and lifts the offered interface from the physical devices to high-level network services. Our real-world implementation on KVM indicates that, MemNet can achieve 27% and 70% gain in terms of computing and networking performance, respectively, by resolving the self-interference. It also provides 32% improvement in terms of energy efficiency.

In this paper, we take a close look to understand the cloud-based file synchronization and collaboration systems. Using the popular Dropbox as a case study, our measurement reveals its cascaded computation and communication operations that are far more complicated than those in conventional file hosting. We show that this serial design is necessary for the cloud deployment, which effectively avoids the possible task interference inside the computation cloud; yet it also leads to higher service variance across users. Even worse, in a collaborative file editing session, users' updates would be discarded without any warning. The drop rate is unfortunately related to the slowest collaborator, which severely hinders the system scalability and user satisfaction. We further investigate the root causes of this phenomenon as well as other performance bottlenecks and offer hints for practical improvement.

Nowadays technology advances of wireless networking and mobile devices have made anytime anywhere data access become readily available. This also enables crowdsourced content capturing and sharing, especially for such multimedia data as video. One example is Twitter's Vine, which mainly target mobile devices, allowing users to create ultra-short video clips and instantly share with their followers. In this paper, we take an initial study on this new generation of mobile instant video clip sharing service and explore the potentials towards its further enhancement. We closely investigate its unique mobile interface, featured user behaviors with screen scrolling, revealing the key differences between Vine-enabled anytime anywhere data access patterns and that of traditional counterparts. We then examine the scheduling policy to maximize the user watching experience as well as the cost efficiency. We show that the generic scheduling problem involves two subproblems, namely, pre-fetching scheduling and watch-time download scheduling, and develop effective solutions towards both of them. The superiority of our solution is demonstrated by extensive trace-driven simulations. To the best of our knowledge, this is the first work on modeling and optimizing the view experience of the instant video clip sharing service on mobile devices.

Mobile data traffic has been experiencing a phenomenal rise in the past decade. This ever-increasing data traffic puts significant pressure on the infrastructure of state-of-the-art cellular networks. Recently, device-to-device (D2D) communication that smartly explores local wireless resources has been suggested as a complement of great potential, particularly for the popular proximity-based applications with instant data exchange between nearby users. Significant studies have been conducted on coordinating the D2D and the cellular communication paradigms that share the same licensed spectrum, commonly with an objective of maximizing the aggregated data rate. The new generation of cellular networks, however, have long supported heterogeneous networked applications, which have highly diverse quality-of-service (QoS) specifications. In this paper, we jointly consider resource allocation and power control with heterogeneous QoS requirements from the applications. We closely analyze two representative classes of applications, namely streaming-like and file-sharing-like, and develop optimized solutions to coordinate the cellular and D2D communications with the best resource sharing mode. We further extend our solution to accommodate more general application scenarios and larger system scales. Extensive simulations under realistic configurations demonstrate that our solution enables better resource utilization for heterogeneous applications with less possibility of underprovisioning or overprovisioning.

Recent years have witnessed the prevalence of MapReduce-based systems, e.g., the Apache Hadoop, in large-scale distributed data processing. Fetching data from remote servers across multiple network switches is known to be costly. Hence, it is highly desirable to co-locate computation with data. State-of-the-art popularity-based replication achieves data locality through replicating popular files and spreading the replicas over multiple servers. While working well for independent files, they can store highly dependent files in different servers, resulting in excessive remote data accesses exchanges and consequently prolonging the job completion time. In this paper, we develop DALM (Dependency-Aware Locality for MapReduce), a novel replication strategy for general real-world input data that can be highly skewed and dependent. DALM accommodates data-dependency in a data-locality framework that comprehensively weights such key factors as popularity and storage budget. We extensively evaluate DALM through both simulations and real-world implementations, and have compared with state-of-the-art solutions, including the Hadoop system and the popularity-based Scarlett. The results show that DALM can significantly improve data locality for different inputs. For a popular iterative graph processing application on Hadoop, our prototype implementation of DALM reduces the remote data access and job completion time by 34.3% and 9.4%, respectively.

Distributed interactive applications (DIAs) such as online gaming have attracted a vast number of users over the Internet. It is however known that the deployment of DIA systems comes with peculiar hardware/software requirements on the users' consoles. Recently, such industrial pioneers as Gaikai, Onlive and Ciinow have offered a new based distributed interactive applications generation of cloud (CDIAs), which shift the necessary computing loads to cloud platforms and largely relieve the pressure on individual user consoles. In this paper, we take a first step towards understanding the CDIA framework and highlight its design challenges. Our measurement reveals the inside structure as well as the operations of real CDIA systems and identifies the critical role of the cloud proxies. While this design makes effective use of cloud resources to mitigate the clients' workloads, it can also significantly increase the interaction latency among clients if not carefully handled. Besides the extra network latency due to the involvement of cloud proxies, we find that the computation-intensive tasks (e.g., Game rendering) and bandwidth-intensive tasks (e.g., Streaming the game screen to the clients) together create a severe bottleneck in CDIA. Our experiment indicates that when the cloud proxies are virtual machines (VMs) in the cloud, the computation-intensive and bandwidth-intensive tasks will seriously interfere with each other if not handled carefully. We accordingly capture this feature in our model and present an interference-aware solution. This approach not only smartly allocates the workloads but also dynamically assigns the capacities across VMs.

YouTube is arguably the most popular online videos sharing site nowadays. To further augment its service with better revenue, it has started working with content owners (known as YouTube partners) whose copyrighted videos and channels have pulled massive audience. By uploading high-quality premium videos, the partners have essentially changed the user-generated content feature of YouTube and further increased YouTube's popularity. Understanding the latest YouTube access pattern is thus crucial to both YouTube and its partners, as well as to other providers of relevant services. In this paper, we for the first time analyze a large-scale YouTube dataset from a partner's view. We make effective use of Insight, a new analytics service of YouTube that offers inside statistics for partners about their content accesses and audience behaviours. From the raw Insight data that are confined to simple scalars and charts, we reveal the inherent relationship among the various metrics that affect the popularity of the videos. Our findings facilitate YouTube partners to adapt their content deployment and user engagement strategies, having great potentials for them to collaborate with YouTube to generate more views and subsequently increasing their revenues.

Popularity prediction, with both technological and economic importance, has been extensively studied for conventional video sharing sites (VSSes), where the videos are mainly found via searching, browsing, or related links. Recent statistics however suggest that online social network (OSN) users regularly share video contents from VSSes, which has contributed to a significant portion of the accesses; yet the popularity prediction in this new context remains largely unexplored. In this paper, we present an initial study on the popularity prediction of videos propagated in OSNs along friendship links.

This paper proposes an algorithm of data subcarrier designing to apply the tone reservation(TR) peak-to-average power ratio (PAPR) reduction algorithm in OFDM-based wireless communication systems and overcome the high computational cost issue. Different from the existing works, the proposed algorithm focuses on designing data subcarriers, controlling both the iteration times and the number of subcarriers. The new algorithm exhibits similar performance as the traditional TR algorithm with lower computational complexity and the ability to control the number of used subcarriers. Simulation results show that the proposed algorithm can significantly reduce the PAPR by only 2 or 3 iterations.

Youtube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Facebook-like online social networks (OSNs), have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSSes are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users' viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends are more likely to have common interests and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for VSS video recommendation. An auto-encoder model is developed to learn the social similarity of different videos in terms of their sharing in OSN. We therefore propose a similarity-based strategy to enhance recommended video lists for VSSes. Evaluation results demonstrate that this strategy can remarkably improve the precision in VSSes, as compared to state-of-the-art strategies without social information.

Video streaming has become one of the most popular networked applications and, with the increased bandwidth and computation power of mobile devices, anywhere and anytime streaming has become a reality. Unfortunately, it remains a challenging task to compress high-quality video in real-time in such devices given the excessive computation and energy demands of compression. On the other hand, transmitting the raw video is simply unaffordable from both energy and bandwidth perspective.

Energy efficient broadcasting is of paramount importance for many broadcast applications in wireless ad hoc networks. With respects network coding, it has been proved that the energy gain is upper bounded by 3. However, the coding opportunity is often highly dependent on the established routing paths, resulting in that a lot of coding opportunities could be lost in practice. By combining network coding with the Connected Dominating Set (CDS)-based broadcasting, we take full use of network coding. The intuition behind our algorithm is to intersect information flows at nodes in CDS to increase the coding opportunities. We propose a novel scheme named NCAB, a Network Coding Aware based Broadcast routing mechanism, integrating the network coding and the dynamic implementation of connected dominating set. Our experimental results show that NCAB provides up to 169% gains compared to flooding, and 41% gains compared to CDS-based broadcasting.

The problem of streaming packetized media has been intensively studied for a long time. In this paper, we revisit this problem in the high-speed railway context, where passengers encode and upload videos through increasingly powerful smartphones. The challenge is highlighted by the fast changing channel conditions in high-speed trains and the limited battery of cell phones. Inspired by the unique spatial-temporal characteristics of wireless signals along high-speed railways, we propose a novel energy-efficient and rate-distortion optimized approach for video streaming. Our solution effectively predicts the signal strength through its spatial-temporal periodicity in this new application scenario. It then smartly adjusts the GOF budget, schedules the video transmission to achieve graceful rate-distortion performance and yet conserves the energy consumption. Performance evaluation based on simulated railway scenarios and H.264 video traces demonstrates the effectiveness of our solution and its superiority as compared to existing solutions.

Data collection is of paramount importance in many applications of wireless sensor networks (WSNs). Especially, to accommodate ever increasing demands of signal source coding applications, the capacity of processing multi-user data query is crucial in WSNs where the efficiency is one key consideration. To that end, this paper presents EDCA: an Efficient Data Collection Approach for data query in WSNs, which exploits recent matrix completion techniques. Specifically, for the efficiency of energy consumption, we randomly select a part of nodes from the sensor network to sample at each time instance and directly forward the data to the sink. Then, to recover the data precisely, we shift the rank minimization problem, which is NP-hard, to a convex optimization one. Compared with the centralized scheme, energy consumption using EDCA is significantly reduced due to lower sampling rate and fewer packets to transmit. The experimental results demonstrate that EDCA significantly outperforms the existing naive method in terms of energy consumption and the introduced errors are quite trivial.

Routing is one of the most challenging open problems in pocket-switched-networks (PSN). In this paper, we propose a novel co-route media content forwarding scheme (CRMF), in which new contact opportunities are created for occasionally disconnected mobile users. Our study is inspired by two observations: one is that many people tend to make regular journeys to the same place, so their trajectories show a high degree of temporal and spatial regularity. The other is that the number of repeated journeys for an individual commuter is greater than that of the repeated contacts with another commuter who possess similar seasonal movement patterns. Our main contributions include: we properly install store-and-forward routers based on vehicle mobility patterns and human regular movement behaviors; we also propose a router-centric prediction scheme that collects passenger historical trajectory information to determine the delivery scheme. The simulation results demonstrate that this approach improves delivery ratio and also reduces the delivery latency compared to memory (history)-less delivery scheme.

We investigate the problem of joint frequency offset and channel estimation for OFDM systems. The complexity of the joint maximum likelihood (ML) estimation procedure motivates us to propose an adaptive MLE algorithm which iterates between estimating the frequency offset and the channel parameters. Pilot tones are used to obtain the initial estimates and then a decision-directed technique provides an effective estimation technique. The joint modified (averaged) Cramer-Rao lower bounds (MCRB) of the channel coefficients and frequency offset estimates are derived and discussed. It is shown that, for the case of a large number of subcarriers in the OFDM system, there is approximately a 6 dB loss in the frequency offset estimate lower bound due to the lack of knowledge of the channel impulse response (CIR). The degradation of the CIR lower bound is less severe and depends on the channel delay spread. We show both analytically and by simulation, that the channel estimate accuracy is less sensitive to unknown frequency offset than the frequency offset estimation is affected by the unknown CIR. Comprehensive simulations have been carried out to validate the effectiveness of the adaptive joint estimation algorithm.

The combination of multiple-antenna and orthogonal frequency division multiplexing (OFDM) provides reliable communications over frequency selective fading channels. We investigate this approach and focus on the application of space-time block codes (STBC) and space-frequency block codes (SFBC) in OFDM systems. We compare the performance of maximum likelihood (ML), zero forcing (ZF) and conventional detection algorithms. We show that ZF provides a good trade-off between computational complexity and performance. The problem of channel estimation in STBC-OFDM and SFBC-OFDM systems is also studied, including the derivation of the Cramer-Rao lower bound (CRLB). Since knowledge of the channel is required to coherently decode STBC-OFDM and SFBC-OFDM, we propose an iterative channel estimation algorithm based on the EM algorithm that requires very few pilot symbols. The CRLB can be achieved by the channel estimation algorithm.

Estimating a channel that is subject to frequency selective Rayleigh fading is a challenging problem in an orthogonal frequency division multiplexing (OFDM) system. We propose an enhanced channel estimation algorithm that combines the EM-based algorithms proposed previously and a least squares polynomial fitting (LSPF) approach. The combined algorithm can efficiently estimate the channel response of an OFDM system operating in an environment with multipath fading and additive white Gaussian noise (AWGN). The algorithm can improve the channel estimate obtained from the EM-based algorithms by polynomial fitting. Simulation results show that the bit error rate (BER) as well as the mean square error (MSE) of the channel can be improved by the algorithm. In particular, with these additional computations and demodulation delay, the MSE can be made smaller than the Cramer-Rao lower bound (CRLB).

Performance of both orthogonal frequency division multiplexing (OFDM) and single carrier (SC) systems suffers from a carrier frequency offset (CFO) due to Doppler effect and the carrier instability between the transmitter and the receiver. We investigate the bit error rate (BER) performance degradation of OFDM and SC systems due to the frequency offset in an additive white Gaussian noise (AWGN) channel as well as multipath Rayleigh fading channels. We consider three effects to the BER degradation, i.e., phase shift, useful power decrease and intercarrier interference (ICI). We also derive the approximate expressions of BER under binary phase shift keying (BPSK) and quaternary phase shift keying (QPSK) for both OFDM and SC systems in the presence of CFO. In general, SC is more robust to CFO in the AVVGN channel than OFDM in terms of BER, while both of them suffer similarly from CFO in the multipath Rayleigh fading channels assuming the the same CFO.

We propose an EM-based algorithm to efficiently detect transmitted data in an OFDM system as well as estimating the channel impulse response (CIR). The maximum likelihood estimate of CIR is obtained by using channel statistics (their means and covariances) via the expectation-maximization (EM) algorithm. This algorithm can improve signal detection and the channel estimation accuracy by making use of pilot symbols to obtain an initial estimate for the iteration. Simulation results show that the bit error rate (BER) can be significantly reduced by this algorithm, and validate its good convergence and robust properties.