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Now showing items 1 - 16 of 3933

  • An Approach to Pre-Schedule Traffic in Time-Dependent Pricing Systems

    Jin, Mingshuang   Gao, Shuai   Luo, Hongbin   Li, Jiawei   Zhang, Yuming   Das, Sajal K.  

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  • Improving QoE in Multi-layer Social Sensing

    Di Stefano, Alessandro   Scat脿, Marialisa   La Corte, Aurelio   Das, Sajal K.   Li貌, Pietro  

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  • Content Placement for Video-on-Demand Services Over Cellular Networks

    Naor, Zohar   Das, Sajal K.   Raj, Mayank  

    The issues of content placement and content replication for video-on-demand streaming over cellular networks are addressed in this study. Using many replications of a relatively small number of the most popular items a significant performance improvement can be achieved. Our method was verified using real video streaming data taken from traces of live content distribution networks. Simulation results show that replicating a relatively small number of video files can significantly reduce the incoming bandwidth from the Internet backbone, as well as the (time) latency for content delivery. The proposed scheme is particularly suitable for IP-based TV services, for which the content popularity can be very often predicted with relatively high accuracy. In addition, we propose a hybrid cache management scheme, in which the cache is partitioned into two components. The first component is for long-term items, and it is updated relatively rarely, while the second component is updated more frequently.
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  • A novel feature set for video emotion recognition

    Mo, Shasha   Niu, Jianwei   Su, Yiming   Das, Sajal K.  

    In video recommendation systems, emotions are used along with several other proposed content-based video features. However, such features are independently based on visual or audio signals and the relationship representing the dependencies between the visual and the audio signals is still unexplored. In order to solve this problem, a novel feature set called HHTC features based on the combination of Hilbert-Huang Transform (HHT) based visual features, HHT-based audio features, and cross-correlation features is proposed in this paper. In addition to the dependencies between the visual and the audio signals, the proposed HHTC features have the ability to indicate the time-varying characteristics of these signals. The proposed features are applied to video emotion recognition with the Support Vector Regression (SVR) with potential use in video affective recommendation systems. Experimental results demonstrate that the proposed approach can achieve an improved performance of video affective recognition. (c) 2018 Elsevier B.V. All rights reserved.
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  • A comprehensive survey of Network Function Virtualization

    Yi, Bo   Wang, Xingwei   Li, Keqin   Das, Sajal K.   Huang, Min  

    Today's networks are filled with a massive and ever-growing variety of network functions that coupled with proprietary devices, which leads to network ossification and difficulty in network management and service provision. Network Function Virtualization (NFV) is a promising paradigm to change such situation by decoupling network functions from the underlying dedicated hardware and realizing them in the form of software, which are referred to as Virtual Network Functions (VNFs). Such decoupling introduces many benefits which include reduction of Capital Expenditure (CAPEX) and Operation Expense (OPEX), improved flexibility of service provision, etc. In this paper, we intend to present a comprehensive survey on NFV, which starts from the introduction of NFV motivations. Then, we explain the main concepts of NFV in terms of terminology, standardization and history, and how NFV differs from traditional middle box based network. After that, the standard NFV architecture is introduced using a bottom up approach, based on which the corresponding use cases and solutions are also illustrated. In addition, due to the decoupling of network functionalities and hardware, people's attention is gradually shifted to the VNFs. Next, we provide an extensive and in-depth discussion on state-of-the-art VNF algorithms including VNF placement, scheduling, migration, chaining and multicast. Finally, to accelerate the NFV deployment and avoid pitfalls as far as possible, we survey the challenges faced by NFV and the trend for future directions. In particular, the challenges are discussed from bottom up, which include hardware design, VNF deployment, VNF life cycle control, service chaining, performance evaluation, policy enforcement, energy efficiency, reliability and security, and the future directions are discussed around the current trend towards network softwarization. (C) 2018 Elsevier B.V. All rights reserved.
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  • A survey on control issues in renewable energy integration and microgrid

    Badal, Faisal R.   Das, Purnima   Sarker, Subrata K.   Das, Sajal K.  

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  • Mobility management-A personal perspective

    Das, Sajal K.  

    This article sketches a historical account of the author's personal journey in mobility management research. After brief discussions on mobility management in cellular networks and wireless internet, an information-theoretic framework is presented for mobility learning and prediction having applicability to a variety of settings including smart services and activity recognition. Next is highlighted the importance of privacy-preservation in location-based services and mobility models, followed by the implication of human mobility and behavior in opportunistic networks in the era of the Internet of People. (C) 2018 Elsevier B.V. All rights reserved.
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  • A Survey on fog computing for the Internet of Things

    Bellavista, Paolo   Berrocal, Javier   Corradi, Antonio   Das, Sajal K.   Foschini, Luca   Zanni, Alessandro  

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  • Kinetic-Powered Health Wearables: Challenges and Opportunities

    Hassan, Mahbub   Hu, Wen   Lan, Guhao   Seneviratne, Aruna   Khalifa, Sara   Das, Sajal K.  

    On-body health monitoring involves continuous sensing and wireless data transmissions, which severely impacts the battery life of wearable devices. However, rapid advances in kinetic energy harvesting (KEH) soon might obviate the need for battery recharging or replacement by harnessing human motion energy. The authors discuss the challenges and opportunities of KEH technology to help realize the ultimate vision of fully autonomous IoT-based healthcare.
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  • Popularity-based caching for IPTV services over P2P networks

    Das, Sajal K.   Naor, Zohar   Raj, Mayank  

    This study suggests to use popularity based caching for IP-based TV (IPTV) services over peer-to-peer (P2P) networks. Each peer in a P2P network can use two levels of cache hierarchy: an internal cache and a neighboring peer cache. Using this property, our main focus is on caching the globally most popular video files nearby the clients, in order to reduce the IPTV service delay, increase the quality of service provided to the clients, and reduce the traffic over the Internet backbone. The proposed framework was applied on real data traces from live P2P networks. The results demonstrate a significant improvement over the Least Recently Used (LRU) and the Least Frequently Used (LFU) cache management schemes. This study is motivated by the vision of large P2P networks consisting of many volunteers serving as peers, each of which has a relatively small cache size, in terms of the number of video items it can store. Since the performance of both the LRU and LFU schemes is very poor for small cache, there is a need for another cache management scheme, which outperforms these schemes, especially for small cache size. The proposed distributed popularity-based caching scheme can significantly increase the performance of P2P networks used for video streaming, with respect to the existing networks, that use the LRU or LFU schemes. The performance metric used for comparison is the cache hit ratio and the expected delay for content delivery. In both parameters a significant improvement is demonstrated.
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  • Survey of Security Advances in Smart Grid: A Data Driven Approach

    Tan, Song   De, Debraj   Song, Wen-Zhan   Yang, Junjie   Das, Sajal K.  

    With the integration of advanced computing and communication technologies, smart grid is considered as the next-generation power system, which promises self healing, resilience, sustainability, and efficiency to the energy critical infrastructure. The smart grid innovation brings enormous challenges and initiatives across both industry and academia, in which the security issue emerges to be a critical concern. In this paper, we present a survey of recent security advances in smart grid, by a data driven approach. Compared with existing related works, our survey is centered around the security vulnerabilities and solutions within the entire lifecycle of smart grid data, which are systematically decomposed into four sequential stages: 1) data generation; 2) data acquisition; 3) data storage; and 4) data processing. Moreover, we further review the security analytics in smart grid, which employs data analytics to ensure smart grid security. Finally, an effort to shed light on potential future research concludes this paper.
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  • Popularity-based caching for IPTV services over P2P networks

    Das, Sajal K.   Naor, Zohar   Raj, Mayank  

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  • Energy Harvesting in Nanonetworks

    Mohrehkesh, Shahram   Weigle, Michele C.   Das, Sajal K.  

    The goal of this chapter is to review the process, issues, and challenges of energy harvesting in nanonetworks, composed of nanonodes that are nano to micrometers in size. Ananonode consisting of nan-memory, a nano-processor, nano-harvesters, ultra nano-capacitor, and a nano-transceiver harvests the energy required for its operations, such as processing and communication. The energy harvesting process in nanonetworks differs from traditional networks (e.g. wireless sensor networks, RFID) due to their unique characteristics such as nanoscale, communication model, and molecular operating environment. After reviewing the energy harvesting process and sources, we introduce the communication model, which is the main source of energy consumption for nanonodes. This is followed by a discussion on the models for joint energy harvesting and consumption processes. Finally, we describe approaches for optimizing the energy consumption process, which includes optimum data packet design, optimal energy utilization, energy consumption scheduling, and energy-harvesting-aware protocols.
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  • Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

    Ma, Dong   Lan, Guohao   Hassan, Mahbub   Hu, Wen   Das, Sajal K.  

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  • PIS: A Multi-Dimensional Routing Protocol for Socially-Aware Networking

    Xia, Feng   Liu, Li   Jedari, Behrouz   Das, Sajal K.  

    Socially-aware networking is an emerging paradigm for intermittently connected networks consisting of mobile users with social relationships and characteristics. In this setting, humans are the main carriers of mobile devices. Hence, their connections, social features, and behaviors can be exploited to improve the performance of data forwarding protocols. In this paper, we first explore the impact of three social features, namely physical proximity, user interests, and social relationship on users' daily routines. Then, we propose a multi-dimensional routing protocol called Proximity-Interest-Social (PIS) protocol in which the three different social dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers. PIS protocol utilizes a time slot management mechanism to discover users' movement similarities in different time periods during a day. We compare the performance of PIS to Epidemic, PROPHET, and SimBet routing protocols using SIGCOMM09 and INFOCOM06 data sets. The experiment results show that PIS outperforms other benchmark routing protocols with the highest data delivery ratio with a low communication overhead.
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  • Incentive Mechanism Design for Crowdsourcing:An All-Pay Auction Approach

    Luo, Tie   Das, Sajal K.   Tan, Hwee Pink   Xia, Lirong  

    Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit a maximal contribution from a group of agents (participants) while agents are only motivated to act according to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal's interest, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage "bid-contribute" crowdsourcing process into a single "bid-cum-contribute" stage, and (ii) eliminate the risk of task nonfulfillment. In our proposed approach, we enhance all-pay auctions with two additional features: an adaptive prize and a general crowdsourcing environment. The prize or reward adapts itself as per a function of the unknown winning agent's contribution, and the environment or setting generally accommodates incomplete and asymmetric information, risk-averse (and risk-neutral) agents, and a stochastic (and deterministic) population. We analytically derive this all-pay auction-based mechanism and extensively evaluate it in comparison to classic and optimized mechanisms. The results demonstrate that our proposed approach remarkably outperforms its counterparts in terms of the principal's profit, agent's utility, and social welfare.
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