Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

International audienceOpportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens' active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resource-efficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the work load in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly 1 million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach

IAM - Interpolation and Aggregation on the Move: Collaborative Crowdsensing for Spatio-temporal Phenomena

International audienceCrowdsensing allows citizens to contribute to the monitoring of their living environment using the sensors embedded in their mobile devices, e.g., smartphones. However, crowdsensing at scale involves significant communication, computation, and financial costs due to the dependence on cloud infrastructures for the analysis (e.g., interpolation and aggregation) of spatio-temporal data. This limits the adoption of crowdsensing by activists although sorely needed to inform our knowledge of the environment. As an alternative to the centralized analysis of crowdsensed observations, this paper introduces a fully distributed interpolation-mediated aggregation approach running on smartphones. To achieve so efficiently, we model the interpolation as a distributed tensor completion problem, and we introduce a lightweight aggregation strategy that anticipates the likelihood of future encounters according to the quality of the interpolation. Our approach thus shifts the centralized postprocessing of crowdsensed data to distributed pre-processing on the move, based on opportunistic encounters of crowdsensors through state-of-the-art D2D networking. The evaluation using a dataset of quantitative environmental measurements collected from 550 crowdsensors over 1 year shows that our solution significantly reduces-and may even eliminate-the dependence on the cloud infrastructure, while it incurs a limited resource cost on end devices. Meanwhile, the overall data accuracy remains comparable to that of the centralized approach

Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

International audienceOpportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens' active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resource-efficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the work load in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly 1 million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach

User-centric Context Inference for Mobile Crowdsensing

International audienceMobile crowdsensing is a powerful mechanism to aggregate hyper-local knowledge about the environment. Indeed, users may contribute valuable observations across time and space using the sensors embedded in their smartphones. However, the relevance of the provided measurements depends on the adequacy of the sensing context with respect to the phenomena that are analyzed. This paper concentrates more specifically on assessing the sensing context when gathering observations about the physical environment beyond its geographical position in the Euclidean space, i.e., whether the phone is in-/out-pocket, in-/out-door and on-/under-ground. We introduce an online learning approach to the local inference of the sensing context so as to overcome the disparity of the classification performance due to the heterogeneity of the sensing devices as well as the diversity of user behavior and novel usage scenarios. Our approach specifically features a hierarchical algorithm for inference that requires few opportunistic feedbacks from the user, while increasing the accuracy of the context inference per user

Predictive Anomaly Detection

International audienceCyber attacks are a significant risk for cloud service providers and to mitigate this risk, near real-time anomaly detection and mitigation plays a critical role. To this end, we introduce a statistical anomaly detection system that includes several auto-regressive models tuned to detect complex patterns (e.g. seasonal and multi-dimensional patterns) based on the gathered observations to deal with an evolving spectrum of attacks and a different behaviours of the monitored cloud. In addition, our system adapts the observation period and makes predictions based on a controlled set of observations, i.e. over several expanding time windows that capture some complex patterns, which span different time scales (e.g. long term versus short terms patterns). We evaluate the proposed solution using a public dataset and we show that our anomaly detection system increases the accuracy of the detection while reducing the overall resource usage

Log-based Distributed Intrusion Detection for Hybrid Networks

International audienceWe propose a novel hybrid distributed security operation center which collects logs that are generated by any application, service, and protocol regardless of the layer of the protocol stack and the device (e.g., router); providing a global view of the supervised system based on which complex and distributed intrusions can be detected. Our HDSOC further (i) distributes its capabilities and (ii) provides extensive coordination capabilities for guarantying that both the network and the HDSOC components do not constitute isolated entities largely unaware of each others

Energy-aware Web caching over Hybrid Networks

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