Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented

The Internet of Things: the future or the end of mechatronics.

The advent and increasing implementation of user configured and user oriented systems structured around the use of cloud configured information and the Internet of Things is presenting a new range and class of challenges to the underlying concepts of integration and transfer of functionality around which mechatronics is structured. It is suggested that the ways in which system designers and educators in particular respond to and manage these changes and challenges is going to have a significant impact on the way in which both the Internet of Things and mechatronics develop over time. The paper places the relationship between the Internet of Things and mechatronics into perspective and considers the issues and challenges facing systems designers and implementers in relation to managing the dynamics of the changes required

Data Collection and Aggregation in Mobile Sensing

Nowadays, smartphones have become ubiquitous and are playing a critical role in key aspects of people\u27s daily life such as communication, entertainment and social activities. Most smartphones are equipped with multiple embedded sensors such as GPS (Global Positioning System), accelerometer, camera, etc, and have diverse sensing capacity. Moreover, the emergence of wearable devices also enhances the sensing capabilities of smartphones since most wearable devices can exchange sensory data with smartphones via network interfaces. Therefore, mobile sensing have led to numerous innovative applications in various fields including environmental monitoring, transportation, healthcare, safety and so on. While all these applications are based on two critical techniques in mobile sensing, which are data collection and data aggregation, respectively. Data collection is to collect all the sensory data in the network while data aggregation is any process in which information is gathered and expressed in a summary form such as SUM or AVERAGE. Obviously, the above two problems can be solved by simply collect all the sensory data in the whole network. But that will lead to huge communication cost. This dissertation is to reduce the huge communication cost in data collection and data aggregation in mobile sensing where the following two technical routes are applied. The first technical route is to use sampling techniques such as uniform sampling or Bernoulli sampling. In this way, an aggregation result with acceptable error can be can be calculate while only a small part of mobile phones need to submit their sensory data. The second technical rout is location-based sensing in which every mobile phone submits its geographical position and the mobile sensing platform will use the submitted positions to filter useless sensory data. The experiment results indicate the proposed methods have high performance

Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

We describe an approach for developing a campus-wide sensor network using commodity single board computers. We sketch various use cases for environmental sensor data, for different university stakeholders. Our key premise is that supersensors -- sensors with significant compute capability -- enable more flexible data collection, processing and reaction. In this paper, we describe the initial prototype deployment of our supersensor system in a single department at the University of Glasgow