Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

The solid-state lighting is revolutionizing the indoor illumination. Current incandescent and fluorescent lamps are being replaced by the LEDs at a rapid pace. Apart from extremely high energy efficiency, the LEDs have other advantages such as longer lifespan, lower heat generation, and improved color rendering without using harmful chemicals. One additional benefit of LEDs is that they are capable of switching to different light intensity at a very fast rate. This functionality has given rise to a novel communication technology (known as visible light communication—VLC) where LED luminaires can be used for high speed data transfer. This survey provides a technology overview and review of existing literature of visible light communication and sensing. This paper provides a detailed survey of 1) visible light communication system and characteristics of its various components such as transmitter and receiver; 2) physical layer properties of visible light communication channel, modulation methods, and MIMO techniques; 3) medium access techniques; 4) system design and programmable platforms; and 5) visible light sensing and application such as indoor localization, gesture recognition, screen-camera communication, and vehicular networking. We also outline important challenges that need to be addressed in order to design high-speed mobile networks using visible light communication.

Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.

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Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.

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Understanding and Modeling of WiFi Signal Based Human Activity Recognition

Typical location determination systems require the presence of a physical device that is attached to the person that is being tracked. In addition, they usually require the tracked device to participate actively in the localization process. In this paper, we introduce the concept of Device-free Passive (DfP) localization. A DfP system is envisioned to be able to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. The system works by monitoring and processing changes in the received physical signals at one or more monitoring points to detect changes in the environment. Applications for DfP systems include intrusion detection and tracking, protecting outdoor assets, such as pipelines, railroad tracks, and perimeters.We describe the DfP system's architecture and the challenges that need to be addressed to materialize a DfP system. We show the feasibility of the system by describing algorithms for implementing different functionalities of a DfP system that works with nominal WiFi equipment. We present two techniques for intrusion detection and a technique for tracking a single intruder. Our results show that the system can achieve very high probability of detection and tracking with very few false positives. We also identify different research directions for addressing the challenges of realizing a DfP system.

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Challenges: device-free passive localization for wireless environments

This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two- bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%.

/pdf/whole-home-gesture-recognition-using-wireless-signals-4tosup7gjr.pdf

Whole-home gesture recognition using wireless signals

Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. While distributed computation frameworks have moved beyond a simple map-reduce model, many users are still left to struggle with complex cluster management and configuration tools, even for running simple embarrassingly parallel jobs. We argue that stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. Furthermore, using our prototype implementation, PyWren, we show that this model is general enough to implement a number of distributed computing models, such as BSP, efficiently. Extrapolating from recent trends in network bandwidth and the advent of disaggregated storage, we suggest that stateless functions are a natural fit for data processing in future computing environments.

https://dl.acm.org/doi/pdf/10.1145/3127479.3128601

Occupy the cloud: distributed computing for the 99%

Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming, and represents an evolution that parallels the transition from assembly language to high-level programming languages. This paper gives a quick history of cloud computing, including an accounting of the predictions of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing, describes applications that stretch the current limits of serverless, and then lists obstacles and research opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing.

Cloud Programming Simplified: A Berkeley View on Serverless Computing

Low latency analytics on geographically distributed datasets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single datacenter significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distributed analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement optimization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries' arrivals, and places the tasks to reduce network bottlenecks during the query's execution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 regions using production queries show that Iridium speeds up queries by 3× -- 19× and lowers WAN usage by 15% -- 64% compared to existing baselines.

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Low Latency Geo-distributed Data Analytics

Serverless computing is poised to fulfill the long-held promise of transparent elasticity and millisecond-level pricing. To achieve this goal, service providers impose a finegrained computational model where every function has a maximum duration, a fixed amount of memory and no persistent local storage. We observe that the fine-grained elasticity of serverless is key to achieve high utilization for general computations such as analytics workloads, but that resource limits make it challenging to implement such applications as they need to move large amounts of data between functions that don’t overlap in time. In this paper, we present Locus, a serverless analytics system that judiciously combines (1) cheap but slow storage with (2) fast but expensive storage, to achieve good performance while remaining cost-efficient. Locus applies a performance model to guide users in selecting the type and the amount of storage to achieve the desired cost-performance trade-off. We evaluate Locus on a number of analytics applications including TPC-DS, CloudSort, Big Data Benchmark and show that Locus can navigate the costperformance trade-off, leading to 4⇥-500⇥ performance improvements over slow storage-only baseline and reducing resource usage by up to 59% while achieving comparable performance with running Apache Spark on a cluster of virtual machines, and within 2⇥ slower compared to Redshift.

/pdf/shuffling-fast-and-slow-scalable-analytics-on-serverless-3cvpiu7h0f.pdf

Qifan Pu

Papers

Whole-home gesture recognition using wireless signals

Occupy the cloud: distributed computing for the 99%

Cloud Programming Simplified: A Berkeley View on Serverless Computing

Low Latency Geo-distributed Data Analytics

Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure