Shanhe Yi

A Survey of Fog Computing: Concepts, Applications and Issues

Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.

Fog Computing: Platform and Applications

Despite the broad utilization of cloud computing, some applications and services still cannot benefit from this popular computing paradigm due to inherent problems of cloud computing such as unacceptable latency, lack of mobility support and location-awareness. As a result, fog computing, has emerged as a promising infrastructure to provide elastic resources at the edge of network. In this paper, we have discussed current definitions of fog computing and similar concepts, and proposed a more comprehensive definition. We also analyzed the goals and challenges in fog computing platform, and presented platform design with several exemplar applications. We finally implemented and evaluated a prototype fog computing platform.

Security and Privacy Issues of Fog Computing: A Survey

Fog computing is a promising computing paradigm that extends cloud computing to the edge of networks. Similar to cloud computing but with distinct characteristics, fog computing faces new security and privacy challenges besides those inherited from cloud computing. In this paper, we have surveyed these challenges and corresponding solutions in a brief manner.

Preserving secondary users' privacy in cognitive radio networks

Cognitive radio plays an important role in improving spectrum utilization in wireless services. In the cognitive radio paradigm, secondary users (SUs) are allowed to utilize licensed spectrum opportunistically without interfering with primary users (PUs). To motivate PU to share licensed spectrum with SU, it is reasonable for SU to pay PU a fee whenever the former is utilizing the latters licensed spectrum. SUs detailed usage information, such as when and how long the licensed spectrum is utilized, is needed for PU to calculate payment. Providing usage information to PU, however, may compromise SUs privacy. To solve this dilemma, we are the first to propose a novel privacy-preserving mechanism for cognitive radio transactions through commitment scheme and zero-knowledge proof. This mechanism, on one hand, only allows PU to know the total payment to SU for a billing period, plus a little portion of SUs usage information. On the other hand, it guarantees PU that the payment is correctly calculated. We have implemented our mechanism and evaluated its performance.

AMIL: Localizing neighboring mobile devices through a simple gesture

Smartphone users are often grouped to exchange files or perform collaborative tasks when meeting together. We argue that the location information of group members is critical to many mobile applications. Existing localization solutions mostly rely on anchor nodes or infrastructures to perform ranging and positioning. These approaches are inefficient for ad hoc scenarios. In this paper, we propose AMIL, an Acoustic Mobility-Induced TDoA (Time-Difference-of-Arrival)-based Localization scheme for smartphones. In AMIL, a smartphone user can use simple gestures (e.g., hold the phone and draw a triangle in the air) to quickly obtain the relative coordinates of neighboring mobile devices. We have implemented and evaluated AMIL on off-the-shelf smartphones. The field tests have shown that our scheme can achieve less than three degree orientation errors and can successfully build a simple map of 12 people in an office room with average error of 50cm.

Secondary User Monitoring in Unslotted Cognitive Radio Networks with Unknown Models

Cognitive radio networking (CRN) is a promising technology to improve the spectrum utilization by allowing secondary users (unlicensed users) to opportunistically access white space (spectrum holes) in licensed bands. Monitoring the detailed characteristics of an operational cognitive radio network is critical to many system administrative tasks. However, very limited work has been done in this area. In this paper, we study the passive secondary user monitoring problem in an unslotted cognitive radio network, where the users’ traffic statistics are unknown in priori. We formulate the problem as a multi-armed bandit (MAB) problem with weighted virtual reward. We propose a dynamic sniffer-channel assignment policy to capture as much as interested user data. Simulation results show that the proposed policy can achieve a logarithmic regret with relative scalability.

CamK: A camera-based keyboard for small mobile devices

Due to the smaller size of mobile devices, on-screen keyboards become inefficient for text entry. In this paper, we present CamK, a camera-based text-entry method, which uses an arbitrary panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. CamK captures the images during the typing process and uses the image processing technique to recognize the typing behavior. The principle of CamK is to extract the keys, track the users fingertips, detect and localize the keystroke. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. Additionally, CamK optimizes computation-intensive modules to reduce the time latency. We implement CamK on a mobile device running Android. Our experiment results show that CamK can achieve above 95% accuracy of keystroke localization, with only 4.8% false positive keystrokes. When compared to on-screen keyboards, CamK can achieve 1.25X typing speedup for regular text input and 2.5X for random character input.

Efficient service handoff across edge servers via docker container migration

Supporting smooth movement of mobile clients is important when offloading services on an edge computing platform. Interruption-free client mobility demands seamless migration of the offloading service to nearby edge servers. However, fast migration of offloading services across edge servers in a WAN environment poses significant challenges to the handoff service design. In this paper, we present a novel service handoff system which seamlessly migrates offloading services to the nearest edge server, while the mobile client is moving. Service handoff is achieved via container migration. We identify an important performance problem during Docker container migration. Based on our systematic study of container layer management and image stacking, we propose a migration method which leverages the layered storage system to reduce file system synchronization overhead, without dependence on the distributed file system. We implement a prototype system and conduct experiments using real world product applications. Evaluation results reveal that compared to state-of-the-art service handoff systems designed for edge computing platforms, our system reduces the total duration of service handoff time by 80%(56%) with network bandwidth 5Mbps(20Mbps).

LAVEA: latency-aware video analytics on edge computing platform

Along the trend pushing computation from the network core to the edge where the most of data are generated, edge computing has shown its potential in reducing response time, lowering bandwidth usage, improving energy efficiency and so on. At the same time, low-latency video analytics is becoming more and more important for applications in public safety, counter-terrorism, self-driving cars, VR/AR, etc. As those tasks are either computation intensive or bandwidth hungry, edge computing fits in well here with its ability to flexibly utilize computation and bandwidth from and between each layer. In this paper, we present LAVEA, a system built on top of an edge computing platform, which offloads computation between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design and formulated an optimization problem for offloading task selection and prioritized offloading requests received at the edge node to minimize the response time. In case of a saturating workload on the front edge node, we have designed and compared various task placement schemes that are tailed for inter-edge collaboration. We have implemented and evaluated our system. Our results reveal that the client-edge configuration has a speedup ranging from 1.3x to 4x (1.2x to 1.7x) against running in local (client-cloud configuration). The proposed shortest scheduling latency first scheme outputs the best overall task placement performance for inter-edge collaboration.

WearLock: Unlocking Your Phone via Acoustics Using Smartwatch

Smartphone lock screens are implemented to reduce the risk of data loss or compromise given the fact that increasing amount of person data are accessible on smartphones nowadays. Unfortunately, many smartphone users abandon lock screens due to the inconvenience of unlocking their phones many times a day. With the wide adoption of wearables, token-based approaches have gained popularity in simplifying unlocking and retaining security at the same time. To this end, we propose to take advantage of the smartwatch for easy smartphone unlocking. In this paper, we have designed WearLock, a system that uses acoustic tones as tokens to automate the unlocking securely. We build a sub-channel selection and an adaptive modulation in the acoustic modem to maximize unlocking success rate against ambient noise only when those two devices are nearby. We leverage the motion sensor on the smartwatch to reduce the unlock frequency. We offload smartwatch tasks to the smartphone to speed up computation and save energy. We have implemented the WearLock prototype and conducted extensive evaluations. Results achieved a low average bit error rate (BER) as 8% in various experiments. Compared to traditional manual personal identification numbers (PINs) entry, WearLock achieves at least 18% unlock speedup without any manual effort.