More Like this

1. Cost-Performance Trade-Offs in Fog Computing for IoT Data Processing of Social Virtual Reality

   Wang, Songjie ( November 2019 , 2019 IEEE International Conference on Fog Computing (ICFC))

   Virtual Reality (VR)-based Learning Environments (VRLEs) are gaining popularity due to the wide availability of cloud and its edge (a.k.a. fog) technologies and high-speed networks. Thus, there is a need to investigate Internet-of-Things (IoT)-based application design concepts within social VRLEs to offer scalable, cost-efficient services that adapt to dynamic cloud/fog system conditions. In this paper, we investigate the costperformance trade-offs for an IoT-based application that integrates large-scale sensor data from Social VRLEs and coordinates the real-time data processing and visualization across cloud/fog platforms. To facilitate dynamic performance adaptation of the IoT-based application with increased user scale, we present a set of cost-aware adaptive control rules. The implementation of the rules is based on an analytical queuing model that determines the performance states of the IoT-based application, given the current workload and the allocated cloud/fog resources. Using the IoTbased application in an exemplar VRLE use case, we evaluate the cost-performance trade-offs with three system architectures i.e., cloud-only, edge-only and edge-cloud architectures. Experiment results illustrate the best/worst practices in the cost-performance trade-offs for a range of simulated IoT scenarios involving monitoring user emotional data collected by using brain sensors. Our results also detail the impact of the system architecture selection, and the benefits in enabling feedback about student emotions to instructors during Social VR learning sessions. Lastly, we show the benefits of integrating our model-based feedback control in maximizing IoT-based application performance while keeping the associated costs at a minimum level. 

   more » « less
2. A 41.5 pJ/b, 2.4GHz Digital-Friendly Orthogonally Tunable Transceiver SoC with 3-decades of Energy-Performance Scalability

   https://doi.org/10.1109/CICC48029.2020.9075915  

   Chatterjee, Baibhab ; Sen, Shreyas ( April 2020 , IEEE Custom Integrated Circuits Conference (CICC))

   null (Ed.)

   Adaptive communication for Internet of Things (IoT) and Wireless Body Area Network (WBAN) technologies is becoming increasingly popular due to the large power-performance trade-offs and highly dynamic channel conditions. Path loss, low signal to noise ratio (SNR) in the channel and network congestion adversely affect the data communication, each of which can be taken care of using different strategies such as reducing the data rate (for reducing congestion), increasing the output power (for increased path loss) and application of error correction coding (ECC, for low SNR). In this paper, we present a digital-friendly Transceiver SoC consisting of an RF-DAC based transmitter with orthogonally tunable output power, data rate and ECC that enables optimum system level bit error rate (BER) and energy for over 3-orders of energy-performance scalability, along with an ultra-low-power OOK receiver that receives the transmitter's control bits from a nearby base station for closed-loop control. The data rate and ECC control is achieved through a digital baseband, while a tapped capacitor matching network controls the output power. The energy efficiency of the transmitter is 27.6pJ/b at 10MSps and at 0.8V supply (~9X improvement over state-of-the-art), while the entire SoC (Transmitter+OOK receiver for controller feedback) consumes only 41.5pJ/b. 

   more » « less
3. Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge

   https://doi.org/10.1109/PERCOMW.2019.8730577  

   Pettet, Geoffrey ; Sahoo, Saroj ; Dubey, Abhishek ( March 2019 , 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops))

   The Internet of Things (IoT) requires distributed, large scale data collection via geographically distributed devices. While IoT devices typically send data to the cloud for processing, this is problematic for bandwidth constrained applications. Fog and edge computing (processing data near where it is gathered, and sending only results to the cloud) has become more popular, as it lowers network overhead and latency. Edge computing often uses devices with low computational capacity, therefore service frameworks and middleware are needed to efficiently compose services. While many frameworks use a top-down perspective, quality of service is an emergent property of the entire system and often requires a bottom up approach. We define services as multi-modal, allowing resource and performance tradeoffs. Different modes can be composed to meet an application's high level goal, which is modeled as a function. We examine a case study for counting vehicle traffic through intersections in Nashville. We apply object detection and tracking to video of the intersection, which must be performed at the edge due to privacy and bandwidth constraints. We explore the hardware and software architectures, and identify the various modes. This paper lays the foundation to formulate the online optimization problem presented by the system which makes tradeoffs between the quantity of services and their quality constrained by available resources. 

   more » « less
4. WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

   https://doi.org/10.1155/2018/8125126  

   Dargazany, Aras R. ; Stegagno, Paolo ; Mankodiya, Kunal ( November 2018 , Mobile Information Systems)

   This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics. 

   more » « less
5. Flow-level Dynamic Bandwidth Allocation in SDN-enabled Edge Cloud using Heuristic Reinforcement Learning

   Arslan Qadeer, Myung Lee ( January 2021 , IEEE Conference, on Future Internet of Things and Cloud)

   Edge Cloud (EC) is poised to brace massive machine type communication (mMTC) for 5G and IoT by providing compute and network resources at the edge. Yet, the EC being regionally domestic with a smaller scale, faces the challenges of bandwidth and computational throughput. Resource management techniques are considered necessary to achieve efficient resource allocation objectives. Software Defined Network (SDN) enabled EC architecture is emerging as a potential solution that enables dynamic bandwidth allocation and task scheduling for latency sensitive and diverse mobile applications in the EC environment. This study proposes a novel Heuristic Reinforcement Learning (HRL) based flowlevel dynamic bandwidth allocation framework and validates it through end-to-end implementation using OpenFlow meter feature. OpenFlow meter provides granular control and allows demand-based flow management to meet the diverse QoS requirements germane to IoT traffics. The proposed framework is then evaluated by emulating an EC scenario based on real NSF COSMOS testbed topology at The City College of New York. A specific heuristic reinforcement learning with linear-annealing technique and a pruning principle are proposed and compared with the baseline approach. Our proposed strategy performs consistently in both Mininet and hardware OpenFlow switches based environments. The performance evaluation considers key metrics associated with real-time applications: throughput, end-to-end delay, packet loss rate, and overall system cost for bandwidth allocation. Furthermore, our proposed linear annealing method achieves faster convergence rate and better reward in terms of system cost, and the proposed pruning principle remarkably reduces control traffic in the network. 

   more » « less