Enabling Real-Time In-Situ Processing of Ubiquitous Mobile-Application Workflows

Viswanathan, Hariharasudhan; Lee, Eun Kyung; Pompili, Dario

doi:10.1109/mass.2013.86

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…For example, in [15], the three benchmarks fall in the category of parallel chains of trees. In Wireless Sensor Networks, an application typically has a tree-structured workflow [16].…”

Section: B More General Task Graphsmentioning

confidence: 99%

“…These sensors are often equipped with functional microprocessors for some specific tasks. Hence, in some cases, WSN applications face the dilemma of pre-processing on less powerful devices or transmitting raw data to back-end processors [16]. Depending on channel conditions, MABTSA can adapt the strategies by assigning pre-processing tasks on front-end sensors when channel is bad, or simply forwarding raw data when channel is good.…”

Section: Wireless Sensor Network and Iotmentioning

confidence: 99%

“…The first inequality in (16) follows by C i ex ∩ C j ex is a subset of C j ex and the last inequality follows by R(yi…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

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Online Learning for Wireless Distributed Computing

Kao,

Wright,

Krishnamachari

et al. 2016

Preprint

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There has been a growing interest for Wireless Distributed Computing (WDC), which leverages collaborative computing over multiple wireless devices. WDC enables complex applications that a single device cannot support individually. However, the problem of assigning tasks over multiple devices becomes challenging in the dynamic environments encountered in real-world settings, considering that the resource availability and channel conditions change over time in unpredictable ways due to mobility and other factors. In this paper, we formulate a task assignment problem as an online learning problem using an adversarial multi-armed bandit framework. We propose MABSTA, a novel online learning algorithm that learns the performance of unknown devices and channel qualities continually through exploratory probing and makes task assignment decisions by exploiting the gained knowledge. For maximal adaptability, MAB-STA is designed to make no stochastic assumption about the environment. We analyze it mathematically and provide a worstcase performance guarantee for any dynamic environment. We also compare it with the optimal offline policy as well as other baselines via emulations on trace-data obtained from a wireless IoT testbed, and show that it offers competitive and robust performance in all cases. To the best of our knowledge, MABSTA is the first online algorithm in this domain of task assignment problems and provides provable performance guarantee.

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“…For example, in [15], the three benchmarks fall in the category of parallel chains of trees. In Wireless Sensor Networks, an application typically has a tree-structured workflow [16].…”

Section: B More General Task Graphsmentioning

confidence: 99%

Section: Wireless Sensor Network and Iotmentioning

confidence: 99%

See 1 more Smart Citation

Online Learning for Wireless Distributed Computing

Kao,

Wright,

Krishnamachari

et al. 2016

Preprint

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“…For more details on how devices are profiled and on the time taken to execute a unit task of G-causality the interested reader is refereed to [14]. Figure 4(a) shows the performance of the three scheduling approaches in terms of workload completion time.…”

Section: Distributed Computation Of G-causalitymentioning

confidence: 99%

Real-time Tracking of Stress Propagation using Distributed Granger Causality

Lee

Pandey

Pompili

2013

Proceedings of the 8th International Conference on Body Area Networks

Self Cite

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Stress is one of the key factors that impacts the quality of our daily life. It is known that stress can propagate from one individual to others working in close proximity or towards a common goal, e.g., in a military operation or workforce, thus affecting productivity, efficiency, and the ability to make rational decisions. Real-time assessment of the stress of individuals alone is, however, not sufficient as understanding its source and direction in which it propagates in a group is equally if not more important. In this paper, the direction of propagation and magnitude of influence of stress in a group of individuals are studied by applying real-time, in-situ analysis of Granger Causality. G-causality has established itself as one of the promising non-invasive approaches in operational neuroscience to reveal the direction of influence between brain areas by analyzing temporal precedence. Extending G-causality analysis on real-time group data faces, however, communication and computation challenges, to address which a distributed mobile computational framework is employed and workflows defining how data and tasks are divided among the entities of the framework are designed.

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A Distributed Computing Framework for Real-Time Detection of Stress and of Its Propagation in a Team

Pandey

Lee

Pompili

2016

IEEE J. Biomed. Health Inform.

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Stress is one of the key factor that impacts the quality of our daily life: From the productivity and efficiency in the production processes to the ability of (civilian and military) individuals in making rational decisions. Also, stress can propagate from one individual to other working in a close proximity or toward a common goal, e.g., in a military operation or workforce. Real-time assessment of the stress of individuals alone is, however, not sufficient, as understanding its source and direction in which it propagates in a group of people is equally-if not more-important. A continuous near real-time in situ personal stress monitoring system to quantify level of stress of individuals and its direction of propagation in a team is envisioned. However, stress monitoring of an individual via his/her mobile device may not always be possible for extended periods of time due to limited battery capacity of these devices. To overcome this challenge a novel distributed mobile computing framework is proposed to organize the resources in the vicinity and form a mobile device cloud that enables offloading of computation tasks in stress detection algorithm from resource constrained devices (low residual battery, limited CPU cycles) to resource rich devices. Our framework also supports computing parallelization and workflows, defining how the data and tasks divided/assigned among the entities of the framework are designed. The direction of propagation and magnitude of influence of stress in a group of individuals are studied by applying real-time, in situ analysis of Granger Causality. Tangible benefits (in terms of energy expenditure and execution time) of the proposed framework in comparison to a centralized framework are presented via thorough simulations and real experiments.

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Enabling Real-Time In-Situ Processing of Ubiquitous Mobile-Application Workflows

Cited by 9 publications

References 12 publications

Online Learning for Wireless Distributed Computing

Online Learning for Wireless Distributed Computing

Real-time Tracking of Stress Propagation using Distributed Granger Causality

A Distributed Computing Framework for Real-Time Detection of Stress and of Its Propagation in a Team

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