On Exploiting Logical Dependencies for Minimizing Additive Cost Metrics in Resource-Limited Crowdsensing

Hu, Shaohan; Shen, Li; Yao, Shuochao; Lu, Shan; Govindan, Ramesh; Hobbs, Reginald L.; Abdelzaher, Tarek

doi:10.1109/dcoss.2015.26

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…User responses are quantitative and heavily depend on user perspective; that is, typically humans can confidently respond whether it is raining but they will be subjective when asked to estimate the precipitation of rain (Kerman et al 2009). Thus, estimating the state of an event is considerably more difficult than traditional approaches that provide binary choices (Gu et al 2014;Hu et al 2015) or multiple choices from a predefined list of answers (Boutsis and Kalogeraki 2014;Cao et al 2012), which are known in advance.…”

Section: System Modelmentioning

confidence: 99%

State Detection Using Adaptive Human Sensor Sampling

Boutsis

Kalogeraki

Gunopulos³

2016

HCOMP

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With the massive prevalence of smartphones, mobile social sensing systems in which humans acting as social sensors respond to geo-located crowdsourcing tasks, became extremely popular. Such systems can provide significant benefits particularly during crisis management and emergency situations. However, not only querying users can be extremely costly but also human sensors are mobile, subjective and their response delays can highly vary. In this paper we develop a social sensing system that performs sampling on mobile social sensors to achieve accurate and real-time detection of the state of emergency events. Our contributions are two-fold: (i) our approach can capture well emergencies even in large geographical regions, and (ii) our sampling approach considers the individual characteristics of the social sensors to maximize the probability of receiving accurate responses in a timely manner. We provide comprehensive experiments that indicate that our approach accurately identifies critical real-world events, has low overhead and reduces the classification error up to 90% compared to traditional approaches.

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Section: System Modelmentioning

confidence: 99%

State Detection Using Adaptive Human Sensor Sampling

Boutsis

Kalogeraki

Gunopulos³

2016

HCOMP

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“…We developed crowd sensing data retrieval algorithms for decreasing bandwidth consumption by leveraging logical dependencies among data items. [3] Crowd sensing refers to local measurements performed by humans for data collection and aggregation. Another form of social sensing is capturing intent from context.…”

Section: Introductionmentioning

confidence: 99%

Using Intelligent Agents for Social Sensing across Disadvantaged Networks

Hobbs

Dron²

2015

2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems

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Users who retrieve information across disadvantaged networks need to do so in such a way as to minimize network performance impact while maximizing the usefulness and the quality of information (QOI) received. Taking advantage of features from all three network genres (telecommunication, information, and social) will enable this balancing act. Often, these users need to interact using unstructured, ad-hoc queries to decrease the cognitive overload of specialized training or the necessity of learning a constrained language. High QOI can be maintained if an intelligent agent on the network can use social sensing to capture the intent of the query and identify the implied task. Knowing the task will allow agents that service the requests to filter, summarize, or transcode data prior to responding, lessening the network footprint. This paper describes an approach that uses natural language processing (NLP) techniques, multi-valued logic based inferencing, network status checking, and task-relevant metrics for information retrieval. This research effort has resulted in: 1) an example-based NLP approach that can be used to capture intent from unstructured text, 2) a quality metric formed from intrinsic and extrinsic attributes of the objects and the tasks under consideration, and 3) a simple inferencing approach to allow intelligent agents to make quality assessments, delivering the appropriate form of the information that will lessen the impact on a disadvantaged network.

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“…Initial work on decision-driven resource management was recently published in the context of centralized systems [25,31]. It needs to be extended to a more general decision model and to distributed resource management.…”

Section: Decision-driven Resource Management: Optimizing Retrieval Costmentioning

confidence: 99%

Athena: Towards Decision-Centric Anticipatory Sensor Information Delivery

Lee

Marcus

Abdelzaher

et al. 2018

JSAN

Self Cite

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Abstract:The paper introduces a new direction in quality-of-service-aware networked sensing that designs communication protocols and scheduling policies for data delivery that are optimized specifically for decision needs. The work complements present decision monitoring and support tools and falls in the larger framework of decision-driven resource management. A hallmark of the new protocols is that they are aware of the inference structure used to arrive at decisions (from logical predicates), as well as the data (and data quality) that need to be furnished to successfully evaluate the unknowns on which these decisions are based. Such protocols can therefore anticipate and deliver precisely the right data, at the right level of quality, from the right sources, at the right time, to enable valid and timely decisions at minimum cost to the underlying network. This paper presents the decision model used and the protocol design philosophy, reviews the key recent results and describes a novel system, called Athena, that is the first to embody the aforementioned data delivery paradigm. Evaluation results are presented that compare the performance of decision-centric anticipatory information delivery to several baselines, demonstrating its various advantages in terms of decision timeliness, validity and network resources used. The paper concludes with a discussion of remaining future challenges in this emerging area.

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On Exploiting Logical Dependencies for Minimizing Additive Cost Metrics in Resource-Limited Crowdsensing

Cited by 9 publications

References 26 publications

State Detection Using Adaptive Human Sensor Sampling

State Detection Using Adaptive Human Sensor Sampling

Using Intelligent Agents for Social Sensing across Disadvantaged Networks

Athena: Towards Decision-Centric Anticipatory Sensor Information Delivery

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