Mobile Crowdsensing in Software Defined Opportunistic Networks

Mobile crowdsensing is a new paradigm that sharing sensing data collected by mobile devices such as smartphones and tablets. As mobile devices are usually connected by an opportunistic network for data transferring, it is hard to acknowledge the contribution of each mobile user in network forwarding then find a sustainable incentive mechanism. In this paper, we propose a software defined opportunistic network (SDON) scheme for mobile crowdsensing. We design a centralized control structure to manage the opportunistic network and mobile crowdsensing. By the centralized structure, we also design an incentive mechanism for data forwarding and collection in an SDON and solve the optimal decision of mobile devices and the sensing service provider. From the extensive simulation results, our incentive mechanism performs better than original solutions

Understanding collaboration in volunteer computing systems

Volunteer computing is a paradigm in which devices participating in a distributed environment share part of their resources to help others perform their activities. The effectiveness of this computing paradigm depends on the collaboration attitude adopted by the participating devices. Unfortunately for software designers it is not clear how to contribute with local resources to the shared environment without compromising resources that could then be required by the contributors. Therefore, many designers adopt a conservative position when defining the collaboration strategy to be embedded in volunteer computing applications. This position produces an underutilization of the devices’ local resources and reduces the effectiveness of these solutions. This article presents a study that helps designers understand the impact of adopting a particular collaboration attitude to contribute with local resources to the distributed shared environment. The study considers five collaboration strategies, which are analyzed in computing environments with both, abundance and scarcity of resources. The obtained results indicate that collaboration strategies based on effort-based incentives work better than those using contribution-based incentives. These results also show that the use of effort-based incentives does not jeopardize the availability of local resources for the local needs.Peer ReviewedPostprint (published version

Mobile crowdsensing for road sustainability: exploitability of publicly-sourced data

ABSTRACTThis paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the parti..

Towards User Behavior Forecasting in Mobile Crowdsensing Applications

Mobile crowdsensing has rapidly become an interesting and useful methodology to collect data in modern smart cities, thanks to the pervasiveness of users mobile devices. Although there are many different proposals, opportunistic and participatory mobile crowdsensing are the most popular ones. They share a common goal, but require a different effort from the user, which often results in increased costs for the service provider. In this work we forecast user participation in mobile crowdsensing by leveraging a large dataset obtained from a real world application, which is key to understand whether there are areas in a city which need additional data obtained through raised incentives for participants or by other means. We then build a custom regressor trained on the dataset we have, which spans across several years in different cities in Italy, to predict the amount of reports in a given area at a given time. This allows service providers to preventively issue participatory tasks for workers in areas which do not meet a minimum number of measurements. Our results indicate that our model is able to predict the number of reports in an area with an average mean error depending on the precision needed, in the order of 10% for areas with a low number of reports

A Blockchain-Based Reward Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS

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

A survey of urban drive-by sensing: An optimization perspective

Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has an inherent transport component, as the spatial-temporal distribution of the sensors are closely related to the mobility patterns of their hosts, which may include third-party (e.g. taxis, buses) or for-hire (e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is therefore essential to understand, assess and optimize the sensing power of vehicle fleets under a wide range of urban sensing scenarios. To this end, this paper offers an optimization-oriented summary of recent literature by presenting a four-step discussion, namely (1) quantifying the sensing quality (objective); (2) assessing the sensing power of various fleets (strategic); (3) sensor deployment (strategic/tactical); and (4) vehicle maneuvers (tactical/operational). By compiling research findings and practical insights in this way, this review article not only highlights the optimization aspect of drive-by sensing, but also serves as a practical guide for configuring and deploying vehicle-based urban sensing systems.Comment: 24 pages, 3 figures, 4 table