Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper

Requirements for a Flexible and Generic API Enabling Mobile Crowdsensing mHealth Applications

Presently, mHealth becomes increasingly important in supporting patients in their everyday life. For example, diabetes patients can monitor themselves by the use of their smartphones. On the other, clinicians as well as medical researchers try to exploit the advantages of mobile technology. More specifically, mHealth applications can gather data in everyday life and are able to easily collect sensor or context data (e.g., the current temperature). Compared to clinical trials, these advantages enable mHealth applications to gather more data in a rather short time. Besides, humans often behave atypically in a clinical environment and, hence, mHealth applications collect data in a setting that reflects the daily behavior more naturally. Hitherto, many technical solutions emerged to deal with such data collection settings. Mobile crowdsensing is one prominent example in this context. We utilize the latter technology in a multitude of large-scale projects to gather data of several chronic disorders. In the TrackYourTinnitus project, for example, we pursue the goal to reveal new medical insights to the tinnitus disorder. We learned in the realized projects that a sophisticated API must be provided to cope with the requirements of researchers from the medical domain. Notably, the API must be able to flexibly deal with requirement changes. The work at hand presents the elicited requirements and illustrate the pillars on which our flexible and generic API is built on. Although we identified that the maintenance of such an API is a challenging endeavor, new data evaluation opportunities arise that are promising in the context of chronic disorder management

Mobile crowd sensing architectural frameworks: A comprehensive survey

Mobile Crowd Sensing has emerged as a new sensing paradigm, efficiently exploiting human intelligence and mobility in conjunction with advanced capabilities and proliferation of mobile devices. In order for MCS applications to reach their full potentials, a number of research challenges should be sufficiently addressed. The aim of this paper is to survey representative mobile crowd sensing applications and frameworks proposed in related research literature, analyze their distinct features and discuss on their relative merits and weaknesses, highlighting also potential solutions, in order to take a step closer to the definition of a unified MCS architectural framework

Cost-aware compressive sensing for networked sensing systems

Compressive Sensing is a technique that can help reduce the sampling rate of sensing tasks. In mobile crowdsensing applications or wireless sensor networks, the resource burden of collecting samples is often a major concern. Therefore, compressive sensing is a promising approach in such scenarios. An implicit assumption underlying compressive sensing - both in theory and its applications - is that every sample has the same cost: its goal is to simply reduce the number of samples while achieving a good recovery accuracy. In many networked sensing systems, however, the cost of obtaining a specific sample may depend highly on the location, time, condition of the device, and many other factors of the sample. In this paper, we study compressive sensing in situations where different samples have different costs, and we seek to find a good trade-off between minimizing the total sample cost and the resulting recovery accuracy. We design CostAware Compressive Sensing (CACS), which incorporates the cost-diversity of samples into the compressive sensing framework, and we apply CACS in networked sensing systems. Technically, we use regularized column sum (RCS) as a predictive metric for recovery accuracy, and use this metric to design an optimization algorithm for finding a least cost randomized sampling scheme with provable recovery bounds. We also show how CACS can be applied in a distributed context. Using traffic monitoring and air pollution as concrete application examples, we evaluate CACS based on large-scale real-life traces. Our results show that CACS achieves significant cost savings, outperforming natural baselines (greedy and random sampling) by up to 4x

A Collaborative Mobile Crowdsensing System for Smart Cities

Nowadays words like Smart City, Internet of Things, Environmental Awareness surround us with the growing interest of Computer Science and Engineering communities. Services supporting these paradigms are definitely based on large amounts of sensed data, which, once obtained and gathered, need to be analyzed in order to build maps, infer patterns, extract useful information. Everything is done in order to achieve a better quality of life. Traditional sensing techniques, like Wired or Wireless Sensor Network, need an intensive usage of distributed sensors to acquire real-world conditions. We propose SenSquare, a Crowdsensing approach based on smartphones and a central coordination server for time-and-space homogeneous data collecting. SenSquare relies on technologies such as CoAP lightweight protocol, Geofencing and the Military Grid Reference System

Extending queuing networks to assess mobile crowdsensing application performance

Copyright © 2016 EAI. The widespread and pervasive adoption of smart devices is boosting Internet of Things and contribution-based paradigms. In particular, Mobile Crowdsensing (MCS), due to its big potential of sharing and collecting large population of contributors-devices, is acquiring interest. Devices such as smartphones and smart boards are equipped with different sensors and actuators able to probe data about the physical environment. In a typical MCS scenario, data produced by sensors are sent to the remote server, where they are collected and processed by the applications. To exploit the MCS paradigm in large-scale business contexts the quality of service of MCS applications must be monitored and guaranteed. Therefore, techniques and tools able to represent and evaluate MCS system quality attributes such as performance and energy consumption are required. However, modeling MCS system is quite challenging since not only the number of users but also the number of contributors may vary. In this paper, we propose to adopt queuing networks, a well-known formalism able to deal with large number of requests, to address this issue. In particular we introduce and implement a new policy allowing the number of server to be variable. The proposed model is then adopted in the evaluation of an example, providing interesting insights on contribution, provisioning and usage impacts in terms of some performance and energy consumption metrics

Characterization and evaluation of mobile crowdsensing performance and energy indicators

Mobile Crowdsensing (MCS) is a contribution-based paradigm involving mobiles in pervasive application deployment and operation, pushed by the evergrowing and widespread dissemination of personal devices. Nevertheless, MCS is still lacking of some key features to become a disruptive paradigm. Among others, control on performance and reliability, mainly due to the contribution churning. For mitigating the impact of churning, several policies such as redundancy, over-provisioning and checkpointing can be adopted but, to properly design and evaluate such policies, specific techniques and tools are required. This paper attempts to fill this gap by proposing a new technique for the evaluation of relevant performance and energy figures of merit for MCS systems. It allows to get insights on them from three different perspectives: end users, contributors and service providers. Based on queuing networks (QN), the proposed technique relaxes the assumptions of existing solutions allowing a stochastic characterization of underlying phenomena through general, non exponential distributions. To cope with the contribution churning it extends the QN semantics of a service station with variable number of servers, implementing proper mechanisms to manage the memory issues thus arising in the underlying process. This way, a preliminary validation of the proposed QN model against an analytic one and an in depth investigation also considering checkpointing have been performed through a case study

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure