Martina Marjanovic

A mobile crowd sensing ecosystem enabled by CUPUS

Mobile crowd sensing (MCS) is a novel class of mobile Internet of Things (IoT) applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe phenomena over a large geographic area. The inherent device mobility and high sensing frequency has the capacity to produce dense and rich spatiotemporal information about our environment, but also creates new challenges due to device dynamicity and energy constraints, as well as large volumes of generated raw sensor data which need to be processed and analyzed to extract useful information for end users. The paper presents an ecosystem for mobile crowd sensing which relies on the CloUd-based PUblish/Subscribe middleware (CUPUS) to acquire sensor data from mobile devices in a flexible and energy-efficient manner and to perform near real-time processing of Big Data streams. CUPUS has unique features compared to other MCS platforms: It enables management of mobile sensor resources within the cloud, supports filtering and aggregation of sensor data on mobile devices prior to its transmission into the cloud based on global data requirements, and can push information of interest from the cloud to user devices in near real-time. We present our experience with implementation and deployment of an MCS application for air quality monitoring built on top of the CUPUS middleware. Our experimental evaluation shows that CUPUS offers scalable processing performance, both on mobile devices and within the cloud, while its data propagation delay is mainly affected by transmission delay on wireless links. A generic ecosystem for MCS services based on a publish/subscribe middleware.High-performance publish/subscribe processing middleware.Selective and data-driven acquisition of sensor data on mobile devices.Evaluation based on an MCS air quality monitoring campaign.

Energy-aware and quality-driven sensor management for green mobile crowd sensing

Mobile Crowd Sensing (MCS) is a novel class of Internet of Things applications which exploits the inherent mobility of wearable sensors and mobile devices to observe phenomena of common interest, typically over large geographical areas (e.g. traffic conditions, air pollution, noise in urban areas). Since MCS applications generate large amounts of sensed data which is collected and preprocessed by devices with limited energy supply, challenges arise with respect to sensor management to ensure an energy-aware and quality-driven data acquisition process. In this paper we present a framework for Green Mobile Crowd Sensing (G-MCS) which utilizes a quality-driven sensor management function to continuously select the k-best sensors for a predefined sensing task. Our G-MCS solution utilizes a cloud-based architecture centered around a publish/subscribe communication model to enable the interaction of mobile devices with the cloud for energy-aware MCS. In particular, it obviates redundant sensor activity while satisfying sensing coverage requirements and sensing quality, and consequently reduces the overall energy consumption of an MCS application. We present a model for G-MCS and evaluate its energy savings for different application requirements and geographical sensor distribution scenarios. Furthermore, our model evaluation on a real data set shows that in certain identified cases, significant energy consumption reductions can be achieved by utilizing the proposed framework, which opens the door for green solutions within the area of MCS applications. HighlightsA green Mobile Crowd Sensing (MCS) framework based on a cloud-based Internet of Things architecture.Quality-driven sensor management based on continuous top-k processing.Energy savings model for the Green MCS framework.Comparative evaluation of the Green MCS energy savings model with state-of-the-art solutions.

A Mobile Crowdsensing Ecosystem Enabled by a Cloud-Based Publish/Subscribe Middleware

We are witnessing the rise of a novel class of wearable devices equipped with various sensing capabilities as well as further miniaturization of sensing components that are nowadays being integrated into mobile devices. The inherent mobility of such devices has the capacity to produce dense and rich spatiotemporal information about our environment creating the mobile Internet of Things (IoT). The management of mobile resources to enable sensor discovery and seamless integration of mobile geotagged sensor data with cloud-based IoT platforms creates new challenges due to device dynamicity, energy constraints, and varying sensor data quality. The paper presents an ecosystem for mobile crowdsensing applications which relies on the CloUd-based PUblish/Subscribe middleware (CUPUS) to acquire sensor data from mobile devices in a context-aware and energy-efficient manner. The ecosystem offers the means for location management of mobile Internet-connected objects and adaptive data acquisition from such devices. In addition, our solution enables filtering of sensor data on mobile devices in the proximity of a data producer prior to its transmission into the cloud. Thus it reduces both the network traffic and energy consumption on mobile devices. We evaluate the performance of our mobile CUPUS application to investigate its performance on mobile phones in terms of scalability and CPU, memory and energy consumption under high publishing load.

Urban crowd sensing demonstrator: Sense the Zagreb Air

We demonstrate an urban crowd sensing application for monitoring air quality by use of specially-designed wearable sensors and mobile phones. The application is built upon the OpenIoT platform1 with the goal to support context-aware and energy-efficient acquisition and filtering of sensor data in mobile environments while ensuring adequate sensing coverage. We demonstrate how sensors and mobile devices jointly collect and share data of interest to measure air quality. In particular, we outline the main features of our wearable air quality sensors, present the data acquisition process as well as the user view of the system, which, in contrast to similar applications, provides a personalized real-time notification mechanism to mobile application users. The solution was used in an air quality measurement campaign “Sense the Zagreb Air” performed in the City of Zagreb, Croatia, in early July 2014 with 20 participants.

Comparison of the CUPUS middleware and MQTT protocol for smart city services

Publish/subscribe messaging pattern is often used as a communication mechanism in data-oriented applications and is becoming wide-spread, especially due to the expansion of the Internet of Things (IoT) services and applications. In addition to MQTT, which is one of the commonly used publish/subscribe protocols in the context of IoT, there are a number of other message queuing solutions, either open or proprietary. We have designed a CloUd-based PUblish/Subscribe (CUPUS) middleware solution within the framework of the FP7 project OpenIoT1 that has developed an open-source cloud platform for the IoT. CUPUS is one of the core OpenIoT components which enables flexible integration of wearable sensors and mobile devices as data sources within the OpenIoT platform. In this paper we compare MQTT and CUPUS in the context of smart city application scenarios. Smart city services pose different key-requirements on IoT publish/subscribe solutions and thus we propose a taxonomy to identify vital features of IoT publish/subscribe middleware. The comparison shows that CUPUS is more appropriate for mobile environments with frequent context changes, while it can filter out unrequired data on devices prior to being reported to backend cloud servers. The MQTT protocol proves to be suitable for Wireless Sensor Networks (WSNs) and heterogeneous environments due to its small code footprint, low bandwidth usage and standardized interfaces. Finally we evaluate the two solutions in terms of message footprint in a real-world scenario, latency and delivery semantics.

Edge Computing Architecture for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.

The OpenIoT Approach to Sensor Mobility with Quality-Driven Data Acquisition Management

Given the prominence of IoT applications integrating mobile Internet-connected objects (ICOs), e.g., wearable sensors and mobile devices with built-in sensors, novel solutions are required to discover and collect data from mobile sensors producing data streams from varying locations, while taking into account sensor accuracy, energy-efficiency, and potential data redundancy. The OpenIoT platform offers support for mobile sensors by means of its publish/subscribe middleware solution entitled CloUd-based Publish/Subscribe middleware for the IoT (CUPUS). The CUPUS publish/subscribe component is used to collect data from mobile ICOs in a flexible and energy-efficient manner and to provide preprocessed data into the OpenIoT cloud. Moreover, CUPUS in collaboration with a Quality of Service (QoS) Manager component enables mobility management of ICOs and quality-driven data acquisition from mobile sensors to satisfy the global sensing coverage requirements while taking into account data redundancy and ICO battery lifetime.

Modeling Aggregate Input Load of Interoperable Smart City Services

The Internet of Things (IoT) is expanding and reaching the maturity level beyond initial deployments. An integrative and interoperable IoT platform proves to be a suitable execution environment for Smart City services because users simultaneously use multiple services, while an IoT platform enables cross-service data sharing. A large number of various IoT and mobile devices as well as the corresponding services can generate tremendous input load on an underlying IoT platform. Thus, it is crucial to analyze the overall input rate on Smart City services to ensure predefined quality of service (e.g., low latency required by some IoT services). An aggregate input rate which characterizes a real world deployment can be used to check if a platform is able to adequately support multiple services running in parallel and to evaluate its overall performance. In this paper we review IoT-based Smart City services to identify key applications characterizing the domain, e.g., smart mobility, smart utilities, and citizen-driven mobile crowd sensing services. Next, we analyze the potential load which such applications pose on IoT services that continuously process the generated data streams. The analysis is used to create a model estimating an aggregate load generated by Smart City applications. We simulate a number of characteristic application compositions to provide insight about the aggregate input load and its potential impact on the performance of Smart City services. The proposed model is a first step towards predicting the processing load of Smart City services to facilitate the assessment and planning of required resources for continuous processing of sensor data in the context of Smart City services.

A high throughput processing engine for taxi-generated data streams

The ACM DEBS Grand Challenge 2015 focuses on real-time analytics over a high volume geospatial data stream composed of taxi trip reports from New York City. The goal of the challenge is to provide a solution which continuously identifies the most frequent routes (query 1) and most profitable areas (query 2) for taxis in New York City. The solution needs to process the incoming data stream in near real-time to provide valid information about taxi positions to end-users in a real-world deployment. We propose a modular processing engine design which is configured to offer efficient performance with a high data throughput and low processing latency. It consists of three main components: an input processor which pre-processes data objects to detect outliers, and two independent query processors tailored to the requirements of challenge queries. To efficiently compute query results, query processors use algorithms customized to the distribution of the taxi-generated data stream. Our experimental evaluation shows that the system can on average process 350,000 input events per second in a distributed mode, while achieving an average latency of less than 1 ms for both queries. Due to their excellent performance, the proposed algorithms are well suited for efficient tracking of a large number of vehicles that are present in modern urban areas.

Approaches to Fuse Fixed and Mobile Air Quality Sensors

Nowadays, air quality monitoring is identified as one of the key impacts in assessing the quality of life in urban areas. Traditional measuring procedures include expensive equipment in the fixed monitoring stations which is not suitable for urban areas because of the low spatio-temporal density of measurements. On the other hand, the technological development of small wearable sensor devices has created new opportunities for air pollution monitoring. Therefore, in this paper we discuss statistical approaches to fuse the data from fixed and mobile sensors for air quality monitoring.