The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

Edge Computing: Vision and Challenges

物件導向軟體之架構(Object-Oriented Software Construction)探討

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

/pdf/a-survey-of-mobile-phone-sensing-4to5zwn4tr.pdf

A survey of mobile phone sensing

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Wireless ad-hoc networks are vulnerable to various kinds of security threats and attacks due to relative ease of access to wireless medium and lack of a centralized infrastructure. In this paper, we seek to detect and mitigate the denial of service (DoS) attacks that prevent authorized users from gaining access to the networks. These attacks affect the service availability and connectivity of the wireless networks and hence reduce the network performance. To this end, we propose a novel cross-layer based intrusion detection system (CIDS) to identify the malicious node(s). Exploiting the information available across different layers of the protocol stack by triggering multiple levels of detection, enhances the accuracy of detection. We validate our design through simulations and also demonstrate lower occurrence of false positives

A cross-layer based intrusion detection approach for wireless ad hoc networks

A method described herein includes an act of, at a mobile computing device, receiving an indication that a portion of code of a program executing on the mobile computing device is to be offloaded to a second computing device for execution on the second computing device, wherein the indication is based at least in part upon an estimated energy savings of the mobile computing device by offloading the portion of the code for execution on the second computing device. The method also includes an act of transmitting data to the second computing device that causes the second computing device to execute the portion of the code.

Energy-aware code offload for mobile devices

In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices. RNN models are widely used for Natural Language Processing, Machine translation, and other tasks. However, existing mobile applications that use RNN models do so on the cloud. To address privacy and efficiency concerns, we show how RNN models can be run locally on mobile devices. Existing work on porting deep learning models to mobile devices focus on Convolution Neural Networks (CNNs) and cannot be applied directly to RNN models. In response, we present MobiRNN, a mobile-specific optimization framework that implements GPU offloading specifically for mobile GPUs. Evaluations using an RNN model for activity recognition shows that MobiRNN does significantly decrease the latency of running RNN models on phones.

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Always-on continuous sensing apps drain the battery quickly because they prevent the main processor from sleeping. Instead, sensor hub hardware, available in many smartphones today, can run continuous sensing at lower power while keeping the main processor idle. However, developers have to divide functionality between the main processor and the sensor hub. We implement MobileHub, a system that automatically rewrites applications to leverage the sensor hub without additional programming effort. MobileHub uses a combination of dynamic taint tracking and machine learning to learn when it is safe to leverage the sensor hub without affecting application semantics. We implement MobileHub in Android and prototype a sensor hub on a 8-bit AVR micro-controller. We experiment with 20 applications from Google Play. Our evaluation shows that MobileHub significantly reduces power consumption for continuous sensing apps.

Enhancing mobile apps to use sensor hubs without programmer effort

This short paper presents a detailed empirical study of BBR's performance under different real-world and emulated testbeds across a range of network operating conditions. Our empirical results help to identify network conditions under which BBR outperforms, in terms of goodput, contemporary TCP congestion control algorithms. We find that BBR is well suited for networks with shallow buffers, despite its high retransmissions, whereas existing loss-based algorithms are better suited for deep buffers.To identify the root causes of BBR's limitations, we carefully analyze our empirical results. Our analysis reveals that, contrary to BBR's design goal, BBR often exhibits large queue sizes. Further, the regimes where BBR performs well are often the same regimes where BBR is unfair to competing flows. Finally, we demonstrate the existence of a loss rate "cliff point" beyond which BBR's goodput drops abruptly. Our empirical investigation identifies the likely culprits in each of these cases as specific design options in BBR's source code.

https://dl.acm.org/doi/pdf/10.1145/3355369.3355579

Aruna Balasubramanian

Papers

A cross-layer based intrusion detection approach for wireless ad hoc networks

Energy-aware code offload for mobile devices

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Enhancing mobile apps to use sensor hubs without programmer effort

When to use and when not to use BBR: An empirical analysis and evaluation study