INFOCOM 2019 论文列表

Internet of Things (IoT) has emerged as a new computing paradigm that promises to offer a fully connected “smart” world. However, due to the open nature of wireless medium, the information sensed, collected, and transmitted by IoT devices can be easily intercepted by adversaries, which becomes a serious concern in most IoT applications requiring sensitive data. In practice, cooperative communication approaches can effectively improve the security level for wireless communication under the presence of eavesdroppers with unbounded computational ability. In this paper, we apply the amplify-and-forward (AF) cooperative communication to increase the secrecy capacity of IoT systems by incentivizing relay IoT devices. Specifically, a Stackelberg game is designed to motivate the participation of the relay IoT devices for security enhancement. Extensive experimental results have demonstrated the feasibility and security of the proposed mechanism under both unknown and known channel state information (CSI) models.

Motion tracking technologies have been widely used in mobile interaction applications, such as Virtual Reality (VR), healthy monitoring, and virtual touch control. Compared with dedicated hardware devices, mobile phones use reliable speakers and microphones, and can serve as ubiquitous devices for cheap acoustic-based motion tracking solutions. However, for complex indoor environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter named Multipath Effect Ratio (MER) is defined to indicate the multipath fading effect on received signals at different frequencies. Based on MER, a novel multipath effect mitigating technique is developed to calculate the phase change of acoustic signals and track the corresponding moving distance by using multiple speakers. A Phase-based Acoustic Motion Tracking (PAMT) method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, PAMT can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2mm and 4mm in one-dimensional and two-dimensional scenarios, respectively.

Colocation facilities and Internet eXchange Points (IXPs) provide neutral places for concurrent networks to daily exchange terabytes of data traffic. Although very reliable, these facilities are not immune to failure and may experience difficulties that can have significant impacts on exchanged traffic. In this paper we devise a methodology to identify collocation facilities in traceroute data and to monitor delay and routing patterns between facilities. We also present an anomaly detection technique to report abnormal traffic changes usually due to facilities outages. We evaluate this method with eight months of traceroute data from the RIPE Atlas measurement platform and manually inspect the most prominent events, that are: an IXP outage, a DDoS attack, and a power failure in a facility. These case studies validate the benefits of the proposed system to detect real world outages from traceroute data. We also investigate the impact of anomalies at the metropolitan-level and identify outages that span across up to eight facilities.

This paper introduces a novel caching analysis that, contrary to prior work, makes no modeling assumptions for the file request sequence. We cast the caching problem in the framework of Online Linear optimization (OLO), and introduce a class of minimum regret caching policies, which minimize the losses with respect to the best static configuration in hindsight when the request model is unknown. These policies are very important since they are robust to popularity deviations in the sense that they learn to adjust their caching decisions when the popularity model changes. We first prove a novel lower bound for the regret of any caching policy, improving existing OLO bounds for our setting. Then we show that the Online Gradient Ascent (OGA) policy guarantees a regret that matches the lower bound, hence it is universally optimal. Finally, we shift our attention to a network of caches arranged to form a bipartite graph, and show that the Bipartite Subgradient Algorithm (BSA) has no regret.

Energy supply remains to be a major bottleneck in Wireless Sensor Networks (WSNs). A self-sustainable network operates without battery replacement. Recent efforts employ multi-source energy harvesting to power sensors with ambient energy. Meanwhile, wireless charging is considered in WSNs as a reliable energy source. It motivates us to integrate both fields of research to build a self-sustainable network and guarantee operation under any weather condition. We propose a three-step solution to optimize this new framework. We first solve the Sensor Composition Problem (SCP) to derive the percentage of different types of sensors. Then we enable self-sustainability by bringing energy harvesting storage to the field for charging the Mobile Charger (MC). Next, we propose a 3-factor approximation algorithm to schedule sensor charging and energy replenishment of MC. Our extensive simulation results demonstrate significant improvement of network lifetime and reduction of network cost. The network lifetime can be extended at least three times compared with traditional approaches and the charging capability of MC increases at least 100%.

Currently, cloud computing is a primary enabler of new paradigms such as edge and fog computing. One open issue is the pricing of services or resources. Current pricing schemes are usually oligopolistic and not fair. In this work, we propose OFM, an online learning based marketplace that dynamically determines the price for arbitrary resource types based on supply and demand existing at that period. Unlike state of the art solutions, OFM can handle an arbitrary number of customers and resource types at every instance of time. It further performs integral allocation of resources and thereby avoids the unbounded integrality gap. We evaluate OFM with both real and synthetic datasets to reflect varying buying interests, the number of resources sold and market volatility to demonstrate the feasibility of our solution for several realistic scenarios. We observe that (i) OFM achieves about 9% of optimal prices and maximizes the Nash social welfare (NSW); (ii) OFM converges faster and works with different data distributions; and (iii) OFM scales for a large number of resources and buyers and computational time is in the order of microseconds, making it applicable for real-time use cases especially in edge markets.

Indoor localization has been an active research area given the popularity of Location-Based Services. The CSI fingerprinting based approach is one of the most practical and effective approaches since it can provide adequate accuracy with low overhead for users. The key drawback that limits its wide application is the huge amount of human effort required to build the fingerprint map. This paper is the first to explore addressing this limitation by automating CSI map construction using an Unmanned Aerial Vehicle (UAV). Given the limited battery capacity of commodity UAVs, it is extremely important yet challenging to optimize energy efficiency for the UAV during the CSI measurement task. To address this challenge, we formulate an energy optimization problem based on a novel graph model that includes the cost of possible actions for UAVs. We then transform the formulated problem to the classic Generalized Traveling Salesman Problem (GTSP), which can be solved efficiently. We implement the system on an off-the-shelf programmable drone equipped with a CSI measurement module. We achieve great energy efficiency improvement over the conventional coverage path planning algorithm. Meanwhile, accurate indoor localization can be achieved using the CSI data collected by our UAV system.

To realize an efficient and flexible urban crowd-flow identification, we propose a new scheme called CFid by fusing fine-grained spatio-temporal smartphone signal features. Considering the abundance of ambient WiFi and geomagnetism, we design and validate several of their fine-grained features and similarity metrics to determine if two individuals belong to the same crowd-flow. We formalize a graph stream clustering problem, where co-flow user pairs are opportunistically identified and fed as a dynamic sequence of edges connecting each other. Given such a flexible form, a fast and accurate algorithm processes the edges and identifies the crowd-flows for further upper-level applications, including urban-flow monitoring and shopping-advertisement/recommendation. Using extensive data-driven analytics and 8-month experimental studies upon 50 users (over 2,500 walking traces at 7 different urban sites), we have validated the accuracy (usually >95%), efficiency (only <;5% extra energy-footprint on average than normal usage on mobiles) and flexibility of CFid in identifying large-scale crowd-flows.

Sketch is a compact data structure for network measurements. To achieve fast speed, it needs to be held in the on-chip memory (SRAM), which is very small. To enable the sketch fit into the on-chip memory, the product of counter size and number of counters must be below a certain limit. If we use small counters, e.g., 8 bits, some counters will overflow. If we use large counters, e.g., 16 bits per counter, the total number of counters will be small, each counter will be shared by more flows, leading to poor accuracy. To address this issue, we propose a generic technique: self-adaptive counters (SA Counter). When the value of the counter is small, it works as a normal counter. When the value of the counter is large, we increment it using a predefined probability, so as to represent a large value. Moreover, in SA Counter, the probability decreases when the value increases. This technique can significantly improve the accuracy of sketches. To verify the effectiveness of SA Counter, we apply SA Counter to three typical sketches, and conduct extensive experiments on one real dataset and one synthetic dataset. Experimental results show that, compared with the state-of-the-art, sketches using SA Counter improve the accuracy by up to 13.6 times.

Network slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator's desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog's tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-the-art mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic.

360-degree videos have gained more popularity in recent years, owing to the great advance of panoramic cameras and head-mounted devices. However, as 360-degree videos are usually in high resolution, transmitting the content requires extremely high bandwidth. To protect the Quality of Experience (QoE) of users, researchers have proposed tile-based 360-degree video streaming systems that allocate high/low bit rates to selected tiles of video frames for streaming over the limited bandwidth. It is challenging to determine which tiles should be allocated with a high/low rate, because (1) the video playbacks include too many features that dynamically change over time when making the rate allocation; (2) most of the state-of-the-art systems focus on a fixed set of heuristics to optimize a specific QoE objective, while users may have various QoE objectives that need to be optimized in different ways. This paper presents a Deep Reinforcement Learning (DRL) based framework for 360-degree video streaming, named DRL360. The DRL360 framework helps improve the system performance by jointly optimizing multiple QoE objectives across a broad set of dynamic features. The DRL-based model adaptively allocates rates for the tiles of the future video frames based on the observations collected by client video players. We compare the proposed DRL360 to the existing systems by trace-driven evaluations as well as conducting a realworld experiment over a wide variety of network conditions. Evaluation results reveal that DRL360 can adapt to all considered scenarios, and outperform the state-of-the-art approaches by 20%-30% on average given different QoE objectives.

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.

We introduce a queueing model for passengers waiting to get transport by drivers participating in a ridesharing system. The effect of available drivers' mobility pattern, their willingness to accept rides in a given location, and the incentives offered by the platform, on system throughput is considered. We characterize the largest set of passenger arrival rates which can be served by a fixed number of drivers under any policy dictating the mobility pattern of available drivers. It turns out that any rate in this set can be achieved by offering appropriate constant but region-dependent rewards to drivers for passenger pick up. Moreover, it is shown that dynamic rewards which scale in proportion to the number of passengers waiting for pick up in each region, not only maximize throughput but also significantly decrease pick up delays.

Mobile edge computing (MEC) is a promising technique for providing low-latency access to services at the network edge. The services are hosted at various types of edge nodes with both computation and communication capabilities. Due to the heterogeneity of edge node characteristics and user locations, the performance of MEC varies depending on where the service is hosted. In this paper, we consider such a heterogeneous MEC system, and focus on the problem of placing multiple services in the system to maximize the total reward. We show that the problem is NP-hard via reduction from the set cover problem, and propose a deterministic approximation algorithm to solve the problem, which has an approximation ratio that is not worse than(1-e-1)/4. The proposed algorithm is based on two subroutines that are suitable for small and arbitrarily sized services, respectively. The algorithm is designed using a novel way of partitioning each edge node into multiple slots, where each slot contains one service. The approximation guarantee is obtained via a specialization of the method of conditional expectations, which uses a randomized procedure as an intermediate step. In addition to theoretical guarantees, simulation results also show that the proposed algorithm outperforms other state-of-the-art approaches.

To meet the stringent requirements on the maximally tolerable disruptions of traffic under link failures, many communication networks feature some sort of static failover mechanism for fast rerouting. However, configuring such static failover mechanisms to achieve a high degree of robustness is known to be challenging, in particular when packet tagging or dynamic node state cannot be used. This paper initiates the systematic study of such local fast failover mechanisms which not only provide connectivity guarantees, even under multiple link failures, but also account for the quality of the resulting failover routes, with respect to locality (i.e., route length) and congestion. Failover quality has received less attention in the literature so far, yet it is increasingly important to support emerging applications.We first show that there exists an inherent tradeoff in terms of achievable locality and congestion of failover routes. We then present CASA, an algorithm providing a high degree of robustness as well as a provable quality of fast rerouting. CASA combines two crucial static resilient routing techniques: combinatorial designs and arc-disjoint arborescences. We complement our formal analysis with a simulation study, in which we compare our algorithms with the state-of-the-art in different scenarios and show benefits in terms of stretch, load, and resilience.

Federated learning, i.e., a mobile edge computing framework for deep learning, is a recent advance in privacy-preserving machine learning, where the model is trained in a decentralized manner by the clients, i.e., data curators, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. This paper gives the first attempt to explore user-level privacy leakage against the federated learning by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works that tend to interfere the training process of the federated learning, the proposed method works “invisibly” on the server side. The experimental results demonstrate the effectiveness of the proposed attacking approach and the superior to the state-of-the-art.

Mobile Network Operators (MNOs) are providing more flexible wireless data services to attract subscribers and increase revenues. For example, the data trading market enables user-flexibility by allowing users to sell leftover data to or buy extra data from each other. The rollover mechanism enables time-flexibility by allowing a user to utilize his own leftover data from the previous month in the current month. In this paper, we investigate the economic viability of offering the data trading market together with the rollover mechanism, to gain a deeper understanding of the interrelationship between the user-flexibility and the time-flexibility. We formulate the interactions between the MNO and mobile users as a multi-slot dynamic game. Specifically, in each time slot (e.g., every day), the MNO first determines the selling and buying prices with the goal of revenue maximization, then each user decides his trading action (by solving a dynamic programming problem) to maximize his long-term payoff. Due to the availability of monthly data rollover, a user's daily trading decision corresponds to a dynamic programming problem with two time scales (i.e., day-to-day and month-to-month). Our analysis reveals an optimal trading policy with a target interval structure, specified by a buy-up-to threshold and a sell-down-to threshold in each time slot. Moreover, we show that the rollover mechanism makes users sell less and buy more data given the same trading prices, hence it increases the total demand while decreasing the total supply in the data trading market. Finally, numerical results based on real-world data unveil that the time-flexible rollover mechanism plays a positive role in the user-flexible data trading market, increasing the MNO's revenue by 25% and all users' payoff by 17% on average.

Public-key certificate validation is an important building block for various security protocols for IoT devices, such as secure channel establishment, handshaking, verifying sensing data authenticity from cloud storage, and Blockchains. However, certification validation incurs non-trivial overhead on resource-constrained IoT devices, because it either requires long latency or large cache space. This work proposes to utilize the power of distributed caching and explores the feasibility of using the cache spaces on all IoT devices as a large pool to store validated certificates. We design a Collaborative Certificate Validation (CCV) protocol including a memory-efficient and fast locator for certificate holders, a trust model to evaluate the trustworthiness of devices, and a protocol suite for dynamic update and certificate revocation. Evaluation results show that CCV only uses less than 25% validation time and reduces >90% decryption operations on each device, compared to a recent method. Malicious devices that conduct dishonest validations can be detected by the network using the proposed trust model.

Indoor Visible Light Communications (VLC) are a promising technology to alleviate the looming spectrum crunch crisis in traditional RF spectrum bands. This article studies how to provide throughput-optimal WiFi-like downlink access to users in indoor visible light networks through a set of centrally-controlled and partially interfering light emitting diodes (LEDs). To reduce the effect of interference among users created by the partial overlap of each LED's field of view, we propose LiBeam, a cooperative beamforming scheme, based on forming multiple LED clusters. Each cluster then serves a subset of users by jointly determining the user-LED association strategies and the beamforming vectors of the LEDs. The paper first proposes a mathematical model of the cooperative beamforming problem, presented as maximizing the sum throughput of all VLC users. Then, we solve the resulting mixed integer nonlinear nonconvex programming (MINCoP) problem by designing a globally optimal solution algorithm based on a combination of branch and bound framework as well as convex relaxation techniques. We then design for the first time a large programmable visible light networking testbed based on USRP X310 software-defined radios, and experimentally demonstrate the effectiveness of the proposed joint beamforming and association algorithm through extensive experiments. Performance evaluation results indicate that over 95% utility gain can be achieved compared to suboptimal network control strategies.