Device-to-device (D2D) communication presents a new paradigm in mobile networking to facilitate data exchange between physically proximate devices. The development of D2D is driven by mobile operators to harvest short range communications for improving network performance and supporting proximity-based services. In this paper, we investigate two fundamental and interrelated aspects of D2D communication, security and privacy, which are essential for the adoption and deployment of D2D. We present an extensive review of the state-of-the-art solutions for enhancing security and privacy in D2D communication. By summarizing the challenges, requirements, and features of different proposals, we identify lessons to be learned from existing studies and derive a set of “best practices.” The primary goal of our work is to equip researchers and developers with a better understanding of the underlying problems and the potential solutions for D2D security and privacy. To inspire follow-up research, we identify open problems and highlight future directions with regard to system and communication design. To the best of our knowledge, this is the first comprehensive review to address the fundamental security and privacy issues in D2D communication.

Security and Privacy in Device-to-Device (D2D) Communication: A Review

Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.

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Crowdsourced Data Management: A Survey

This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks (WSNs) are the main components of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in machine-to-machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.

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Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar—all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives, and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.

Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques, and Assurance Actions

The explosive growth in social network content suggests that the largest "sensor network" yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a "sensor network" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.

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Using humans as sensors: an estimation-theoretic perspective

Urban trac gathers increasing interest as cities become bigger, crowded and \smart". We present a system for heterogeneous stream processing and crowdsourcing supporting intelligent urban trac management. Complex events related to trac congestion (trends) are detected from heterogeneous sources involving xed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, a crowdsourcing component handles and resolves sensor disagreement. Furthermore, to deal with data sparsity, a trac modelling component oers information in areas with low sensor coverage. We demonstrate the system with a real-world use-case from Dublin city, Ireland.

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Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management

Over the recent years, the proliferation of mobile networking and the increasing capabilities of smartphone devices have led to the development of “Participatory Sensing” applications, where users actively participate in data collection and sharing in a wide range of application domains from entertainment, to transportation, to environmental monitoring. One important challenge in these settings is privacy preservation for participatory sensing data, such as user trajectories. This paper develops a participatory sensing system for Android smartphones and proposes an efficient approach for privacy preservation which enables users to disclose their trajectory information without compromising their privacy. Existing approaches for privacy-aware data sharing operate under the assumption that user data is maintained in a centralized database. In our work we assume that user data is generated and stored locally on the individual smartphone devices. Our technique is distributed and low cost. We present detailed experimental results to illustrate that our approach is practical, efficient and with low overhead.

Privacy preservation for participatory sensing data

With the rapid growth of mobile smartphone users, several commercial mobile companies have exploited crowd sourcing as an effective approach to collect and analyze data, to improve their services. In a crowd sourcing system, "human workers" are enlisted to perform small tasks, that are difficult to be automated, in return for some monetary compensation. This paper presents our crowd sourcing system that seeks to address the challenge of determining the most efficient allocation of tasks to the human crowd. The goal of our algorithm is to efficiently determine the most appropriate set of workers to assign to each incoming task, so that the real-time demands are met and high quality results are returned. We empirically evaluate our approach and show that our system effectively meets the requested demands, has low overhead and can improve the number of tasks processed under the defined constraints over 71% compared to traditional approaches.

On Task Assignment for Real-Time Reliable Crowdsourcing

Urban data management is already an essential element of modern cities. The authorities can build on the variety of automatically generated information and develop intelligent services that improve citizens daily life, save environmental resources or aid in coping with emergencies. From a data mining perspective, urban data introduce a lot of challenges. Data volume, velocity and veracity are some obvious obstacles. However, there are even more issues of equal importance like data quality, resilience, privacy and security. In this paper we describe the development of a set of techniques and frameworks that aim at effective and efficient urban data management in real settings. To do this, we collaborated with the city of Dublin and worked on real problems and data. Our solutions were integrated in a system that was evaluated and is currently utilized by the city.

Intelligent Urban Data Monitoring for Smart Cities

This paper contributes to mobile crowdsourcing applications by developing a privacy preserving framework that enables users to contribute content to the community while controlling their privacy exposure. One fundamental challenge in such applications is how to preserve user privacy, as participants may end up revealing a great deal of user-identified, geo-located data, which can easily unfold user trajectories or sensitive locations (e.g., user's home or work location). In this paper we develop PROMPT, a highly efficient privacy preserving framework that runs locally on mobile devices. PROMPT relies on a novel geometric approximation approach to preserve user privacy, by evaluating the privacy exposure of users before sharing their geo-located data. Our detailed experimental evaluation using real-world datasets illustrates that our approach is effective, practical and has low overhead on smartphones.

Ioannis Boutsis

Papers

Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management

Privacy preservation for participatory sensing data

On Task Assignment for Real-Time Reliable Crowdsourcing

Intelligent Urban Data Monitoring for Smart Cities

Location privacy for crowdsourcing applications