Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation) , combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations.

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing applications. In this paper, we leverage the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated (corresponding to budget), yet ensuring the data quality. Specifically, we propose a novel framework called CCS-TA, combining the state-of-the-art compressive sensing, Bayesian inference, and active learning techniques, to dynamically select a minimum number of sub-areas for sensing task allocation in each sensing cycle, while deducing the missing data of unallocated sub-areas under a probabilistic data accuracy guarantee. Evaluations on real-life temperature and air quality monitoring datasets show the effectiveness of CCS-TA. In the case of temperature monitoring, CCS-TA allocates 18.0-26.5% fewer tasks than baseline approaches, allocating tasks to only 15.5% of the sub-areas on average while keeping overall sensing error below 0.25°C in 95% of the cycles.

The increased adoption of mobile devices and social networking is drastically changing the way people monitor and share knowledge about their environment. Here, information and communication technologies (ICT) offer significant new ways to support social activism in cities by providing residents with new digital tools to articulate projects and mobilize activities. However, the development of ICT for activism is still in its infancy, with activists using basic tools stitched together in an ad hoc manner for their needs. Still, Internet-based technologies and related software architectures feature various enablers for civic action beyond base social networking. To that end, this paper discusses the vision and initial details of AppCivist, a platform that builds on cross-domain research among social scientists and computer scientists to revisit service-oriented architecture and relevant services to further social activism. We discuss the ICT challenges inherent in this project and present our recent work to address them.

Large-scale mobile environments are characterized by, among others, a large number of mobile users, intermittent connectivity and non-homogeneous arrival rate of data to the users, depending on the region's context. Multiple application scenarios in major cities need to address the above situation for the creation of robust mobile systems. Towards this, it is fundamental to enable system designers to tune a communication infrastructure using various parameters depending on the specific context. In this paper, we take a first step towards enabling an application platform for large-scale information management relying on `mobile social crowd-sourcing'. To inform the stakeholders of expected loads and costs, we model a large-scale mobile pub/sub system as a queueing network. We introduce additional timing constraints such as (i) mobile user's intermittent connectivity period; and (ii) data validity lifetime period (e.g. that of sensor data). Using our MobileJINQS simulator, we parameterize our model with realistic input loads derived from the D4D dataset (CDR) and varied lifetime periods in order to analyze the effect on response time. This work provides system designers with coarse grain design time information when setting realistic loads and time constraints.

One of the main benefits of mobile participatory sensing becoming a reality is the increased knowledge it will provide about the real world while relying on a large number of mobile devices. Those devices can host different types of sensors incorporated in every aspect of our lives. However, given the increasing number of capable mobile devices, any participatory sensing approach should be, first and foremost, scalable. To address this challenge, we present an approach to decrease the participation of (sensing) devices in a manner that does not compromise the accuracy of the real-world information while increasing the efficiency of the overall system. To reduce the number of the devices involved, we present a probabilistic registration approach, based on a realistic human mobility model, that allows devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path. We present the design and implementation of a registration middleware based on our techniques, using which mobile devices can base their registration decision. Through experiments performed on real and simulated datasets, we show that our approach scales, while not sacrificing significant amounts of sensing coverage.

Data-driven macroprogramming of wireless sensor networks (WSNs) provides an easy to use high-level task graph representation to the application developer. However, determining an energy-efficient initial placement of these tasks onto the nodes of the target network poses a set of interesting problems. We present a framework to model this task-mapping problem arising in WSN macroprogramming. Our model can capture placement constraints in tasks, as well as multiple possible routes in the target network. Using our framework, we provide mathematical formulations for the task-mapping problem for two different metrics-energy balance and total energy spent. For both metrics, we address scenarios where (1) a single or (2) multiple paths are possible between nodes. Due to the complex nature of the problems, these formulations are not linear. We provide linearization heuristics for the same, resulting in mixed-integer programming (MIP) formulations. We also provide efficient heuristics for the above. Our experiments show that our heuristics give the same results as the MIP for real-world sensor network macroprograms, and show a speedup of up to several orders of magnitude. We also provide worst-case performance bounds of the heuristics.

Macroprogramming is an application development technique for wireless sensor networks (WSNs) where the developer specifies the behavior of the system, as opposed to that of the constituent nodes. In this proposed demonstration, we would like to present Srijan, a toolkit that enables application development for WSNs in a graphical manner using data-driven macroprogramming. It can be used in various stages of application development, viz. i) specification of application as a task graph, ii) customization of the autogenerated source files with domain-specific imperative code, iii) specification of the target system structure, iv) compilation of the macroprogram into individual customized runtimes for each constituent node of the target system, and finally v) deployment of the auto generated node-level code in an over-the-air manner to the nodes in the target system. The current implementation of Srijan targets both the Sun SPOT sensor nodes and larger nodes with J2SE. Our demonstrattion will encourage users to perform end-to-end WSN application development on the SPOTs using Srijan.

Wireless sensor networks are increasingly employed to develop sophisticated applications where heterogeneous nodes are deployed, and multiple parallel activities must be performed. Therefore, application developers require the ability to partition the system based on the node characteristics, and specify complex interactions among different partitions. Existing programming abstractions for sensor networks tackled this problem by providing a notion of scoping. However, this rarely emerges as a first-class programming construct, hence limiting its applicability. To address this issue, in this paper we present a flexible notion of scoping in the context of a sensor network macroprogramming framework. Our approach enables the specification of complex interactions among system partitions, thus greatly simplifying the development process. Moreover, this is not detrimental to performance: our approach results reasonably close to an optimal solution computed with global system knowledge, while exhibiting a 70% gain w.r.t. baseline solutions.

We present a scalable parallel implementation for converting a Bayesian network to a junction tree, which can then be used for a complete parallel implementation for exact inference. We explore parallelism during the process of moralization, triangulation, clique identification, junction tree construction and potential table calculation. For an arbitrary Bayesian network with n vertices using p processors, the worst-case running time is shown to be O(n2w/p+-wrwn/p+n log p), where w is the clique width and r is the number of states of the random variables. Our algorithm is scalable over 1 les p les nw/log n. We have implemented our parallel algorithm using OpenMP and experimented with up to 128 processors. We consider three types of Bayesian networks: linear, balanced and random. While the state of the art PNL library implementation does not scale, we achieve speedups of 31, 29 and 24 for the above graphs respectively on the DataStar cluster at San Diego Supercomputing Center