Future smart classrooms that we envision will significantly enhance learning experience and seamless communication among students and teachers using real-time sensing and machine intelligence. Existing developments in engineering have brought the state-of-the-art to an inflection point, where they can be utilized as components of a smart classroom. In this paper, we propose a smart classroom system that consists of these components. Our proposed system is capable of making real-time suggestions to an in-class presenter to improve the quality and memorability of their presentation by allowing the presenter to make real-time adjustments/corrections to their non-verbal behavior, such as hand gestures, facial expressions, and body language. We base our suggested system components on existing research in affect sensing, deep learning-based emotion recognition, and real-time mobile-cloud computing. We provide a comprehensive study of these technologies and determine the computational requirements of a system that incorporates these technologies. Based on these requirements, we provide a feasibility study of the system. Although the state-of-the-art research in most of the components we propose in our system are advanced enough to realize the system, the main challenge lies in: 1) the integration of these technologies into a holistic system design; 2) their algorithmic adaptation to allow real-time execution; and 3) quantification of valid educational variables for use in algorithms. In this paper, we discuss current issues and provide future directions in engineering and education disciplines to deploy the proposed system.

Towards Emotionally Aware AI Smart Classroom: Current Issues and Directions for Engineering and Education

This paper surveys the literature of opportunistic offloading. Opportunistic offloading refers to offloading traffic originally transmitted through the cellular network to opportunistic network, or offloading computing tasks originally executed locally to nearby devices with idle computing resources through opportunistic network. This research direction is recently emerged, and the relevant research covers the period from 2009 to date, with an explosive trend over the last four years. We provide a comprehensive review of the research field from a multi-dimensional view based on application goal, realizing approach, offloading direction, etc. In addition, we pinpoint the major classifications of opportunistic offloading, so as to form a hierarchical or graded classification of the existing works. Specifically, we divide opportunistic offloading into two main categories based on application goal: traffic offloading or computation offloading. Each category is further divided into two smaller categories: with and without offloading node selection, which bridges between subscriber node and the cellular network, or plays the role of computing task executor for other nodes. We elaborate, compare, and analyze the literatures in each classification from the perspectives of required information, objective, etc. We present a complete introductory guide to the researches relevant to opportunistic offloading. After summarizing the development of the research direction and offloading strategies of the current state-of-the-art, we further point out the important future research problems and directions.

A Survey of Opportunistic Offloading

Mobile, pervasive computing environments respond to users’ requirements by providing access to and composition of various services over networked devices. In such an environment, service composition needs to satisfy a request’s goal, and be mobile-aware even throughout service discovery and service execution. A composite service also needs to be adaptable to cope with the environment’s dynamic network topology. Existing composition solutions employ goal-oriented planning to provide flexible composition, and assign service providers at runtime, to avoid composition failure. However, these solutions have limited support for complex service flows and composite service adaptation. This paper proposes a self-organizing, goal-driven service model for task resolution and execution in mobile pervasive environments. In particular, it proposes a decentralized heuristic planning algorithm based on backward-chaining to support flexible service discovery. Further, we introduce an adaptation architecture that allows execution paths to dynamically adapt, which reduces failures, and lessens re-execution effort for failure recovery. Simulation results show the suitability of the proposed mechanism in pervasive computing environments where providers are mobile, and it is uncertain what services are available. Our evaluation additionally reveals the model’s limits with regard to network dynamism and resource constraints.

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Goal-Driven Service Composition in Mobile and Pervasive Computing

The number of smartphone users and mobile application offerings are growing rapidly. A smartphone is often expected to offer PC-like functionality. In this paper, we present Virtual Smartphone over IP system that allows users to create virtual smartphone images in the mobile cloud and to customize each image to meet different needs. Users can easily and freely tap into the power of the data center by installing the desired mobile applications remotely in one of these images. Because the mobile applications are controlled remotely, they are not constrained by the limit of processing power, memory and battery life of a physical smartphone.

Virtual Smartphone over IP技術 (特集 ネットワークセキュリティ技術の動向)

Vehicular cloud is getting significant research attention due to the technological advancements in smart vehicles. In near future, vehicles are envisioned to become part of a grid network providing cloud services, such as computing, storage, network, and application as a service. Vehicular cloud computing is an emerging area, designed to support delay-sensitive applications. However, this integration of vehicular network and cloud computing introduces new challenges for the research community. New frameworks have been proposed to assimilate and efficiently manage this merger. In this survey paper, we present the recent advancements in vehicular cloud computing domain. The review is primarily focused on two areas. First, we discuss the frameworks designed to utilize the vehicles’ onboard resources to provide cloud services and highlight the design issues and research challenges. Secondly, we focus on a detailed study of mobility generators, network, and vehicular ad hoc network simulators, as well as the available vehicular data sets. We thus provide an overarching view of the complete domain of vehicular cloud computing and identify areas for future research directions.

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Services and simulation frameworks for vehicular cloud computing: a contemporary survey

Mobile ad hoc networks (MANETs) are becoming increasingly common, and typical network loads considered for MANETs are increasing as applications evolve. This, in turn, increases the importance of bandwidth efficiency while maintaining tight requirements on energy consumption, delay and jitter. Coordinated channel access protocols have been shown to be well suited for highly loaded MANETs under uniform load distributions. However, these protocols are in general not as well suited for non-uniform load distributions as uncoordinated channel access protocols due to the lack of on-demand dynamic channel allocation mechanisms that exist in infrastructure based coordinated protocols. In this paper, we present a lightweight dynamic channel allocation mechanism and a cooperative load balancing strategy that are applicable to cluster based MANETs to address this problem. We present protocols that utilize these mechanisms to improve performance in terms of throughput, energy consumption and inter-packet delay variation (IPDV). Through extensive simulations we show that both dynamic channel allocation and cooperative load balancing improve the bandwidth efficiency under non-uniform load distributions compared to protocols that do not use these mechanisms as well as compared to the IEEE 802.15.4 protocol with GTS mechanism and the IEEE 802.11 uncoordinated protocol.

Cooperative Load Balancing and Dynamic Channel Allocation for Cluster-BasedMobile Ad Hoc Networks

Volunteer computing provides a practical and low cost solution to the increasing computational demands of many applications. Recent advancements in mobile device processing capabilities, combined with the energy efficiency of the mobile devices, make their inclusion in a distributed computing architecture particularly appealing. However, the intrinsic requirement of Internet connectivity to participate in volunteer computing limits the direct adoption of mobile devices due to service availability or related costs to connect to the Internet. In this paper, we propose and implement a novel computational architecture that extends the ability of mobile devices to participate in volunteer computing through ad hoc networking. By introducing decentralized task distribution points, mobile devices are invited to join the computation via device to device communication, removing the requirement for an Internet connection. Using a prototype implementation running on Android devices, we investigate the impact of a promising ad hoc communication technology, namely WiFi Direct, and two task distribution algorithms with different computation and communication overheads, under various scenarios. Experimental results show that our proposed approach is feasible with only minor additional energy consumption at the decentralized task distribution points.

Extending volunteer computing through mobile ad hoc networking

The success of a mobile ad hoc network (MANET) is strongly related to the protocol used at the medium access control (MAC) layer. Depending on the requirements and the specific network under concern, the protocol parameters at the MAC layer can be arbitrated to make best use of the channel resources. Typically, extensive simulation studies are used to find the best values for these variables. The problem with this approach is the need for excessive amounts of processing power and time. As the dimensions of the decision space increase, the need for processing power grows exponentially. This paper addresses this problem by developing an analytical model that reflects the relationships between protocol parameters and the overall performance of the protocol under various network conditions. Specifically, we model the MH-TRACE cluster-based protocol, which is capable of supporting real-time data transmission. The model is capable of estimating performance measures such as energy consumption and number of receptions while being simple enough to be run for a large set of parameters. The model can be used to optimize parameters of the protocol (such as the number of frames per superframe) as well as to predict the performance variations as the external conditions (such as data generation rate) vary.

Analytical performance of soft clustering protocols

Network-wide broadcasting and multicasting are two important routing schemes used in group communications. In network-wide broadcasting, generated packets at the source node are distributed to all nodes in the network, while multicasting delivers the packets only to a subset of the nodes by creating and maintaining a data dissemination structure. Due to the overhead in multicasting, in certain situations, it is more efficient to use network-wide broadcasting instead of multicasting, even when the data is destined to a subset of the nodes. In this paper we analyze a specific broadcasting protocol and a specific multicasting protocol for two performance metrics, namely spectrum efficiency and energy efficiency, and determine the conditions that make one of them preferable over the other.

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Multicasting vs. Broadcasting: What Are the Trade-Offs?

Different forms of parallel computing have been proposed to address the high computational requirements of many applications. Building on advances in parallel computing, volunteer computing has been shown to be an efficient way to exploit the computational resources of under utilized devices that are available around the world. The idea of including mobile devices, such as smartphones and tablets, in existing volunteer computing systems has recently been investigated. In this chapter, we present the current state of the art in the mobile volunteer computing research field, where personal mobile devices are the elements that perform the computation. Starting from the motivations and challenges behind the adoption of personal mobile devices as computational resources, we then provide a literature review of the different architectures that have been proposed to support parallel computing on mobile devices. Finally, we present some open issues that need to be investigated in order to extend user participation and improve the overall system performance for mobile volunteer computing. Volunteer Computing on Mobile Devices: State of the Art and Future Research Directions

Bora Karaoglu

Papers

Cooperative Load Balancing and Dynamic Channel Allocation for Cluster-BasedMobile Ad Hoc Networks

Extending volunteer computing through mobile ad hoc networking

Analytical performance of soft clustering protocols

Multicasting vs. Broadcasting: What Are the Trade-Offs?

Volunteer Computing on Mobile Devices: State of the Art and Future Research Directions