Krešimir Pripužić

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.

Top-k/w publish/subscribe: finding k most relevant publications in sliding time window w

Existing content-based publish/subscribe systems are designed assuming that all matching publications are equally relevant to a subscription. As we cannot know in advance the distribution of publication content, the following two unwanted situations are highly possible: a subscriber either receives too many or only few publications. In this paper we present a new publish/subscribe model which is based on the sliding window computation model. Our model assumes that publications have different relevance to a subscription. In the model, a subscriber receives k most relevant publications published within a time window w, where k and w are parameters defined per each subscription. As a consequence, the arrival rate of incoming relevant publications per subscription is predefined, and does not depend on the publication rate. Since all relevant objects (i.e. publications in our case) cannot be kept in main memory, existing solutions immediately discard less relevant objects, and store only a small representative set for subsequent delivery. In this paper we develop a probabilistic criterion to decide upon the arrival of a new object whether it may become the top-k object at some future point in time and should thus be stored in a special publications queue. We show that by accepting typically very small probability of error, the queue length is reasonably small and does not significantly depend on publication rate. Furthermore, we experimentally evaluate our approach to demonstrate its applicability in practice.

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.

Publish/subscribe middleware for energy-efficient mobile crowdsensing

In this paper we focus on mobile crowdsensing applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe and measure phenomena over a larger geographic area. Such applications, e.g., environmental monitoring or crowdsourced traffic monitoring, involve numerous individuals that on the one hand continuously contribute sensed data to application servers, and on the other hand consume the information of interest to observe a phenomenon typically in their close vicinity. Energy-efficient and context-aware orchestration of the sensing process with data transmission from sensors through mobile devices into the cloud, as well as from the cloud to mobile devices such that information of interest is served to users in real-time, is essential for such applications, primarily due to battery limitations of both mobile devices and wearable sensors. In addition, the latency of data propagation represents their key quality measure from the users perspective. Publish/subscribe middleware offers the mechanisms to deal with those challenges: It enables selective real-time acquisition and filtering of sensor data on mobile devices, efficient continuous processing of large data volumes within the cloud, and near real-time delivery of notifications to mobile devices. This paper presents our implementation of a publish/subscribe middleware system which is tailored to the requirements of mobile and resource-constrained environments with a goal to reduce the overall energy consumption in such environments, and proposes a general architecture for mobile crowdsensing applications. We demonstrate the usability of both the architecture and middleware through our application for air quality monitoring, and discuss the energy footprint of the proposed solution.

Time- and Space-Efficient Sliding Window Top-k Query Processing

A sliding window top-k (top-k/w) query monitors incoming data stream objects within a sliding window of size w to identify the k highest-ranked objects with respect to a given scoring function over time. Processing of such queries is challenging because, even when an object is not a top-k/w object at the time when it enters the processing system, it might become one in the future. Thus a set of potential top-k/w objects has to be stored in memory while its size should be minimized to efficiently cope with high data streaming rates. Existing approaches typically store top-k/w and candidate sliding window objects in a k-skyband over a two-dimensional score-time space. However, due to continuous changes of the k-skyband, its maintenance is quite costly. Probabilistic k-skyband is a novel data structure storing data stream objects from a sliding window with significant probability to become top-k/w objects in future. Continuous probabilistic k-skyband maintenance offers considerably improved runtime performance compared to k-skyband maintenance, especially for large values of k, at the expense of a small and controllable error rate. We propose two possible probabilistic k-skyband usages: (i) When it is used to process all sliding window objects, the resulting top-k/w algorithm is approximate and adequate for processing random-order data streams. (ii) When probabilistic k-skyband is used to process only a subset of most recent sliding window objects, it can improve the runtime performance of continuous k-skyband maintenance, resulting in a novel exact top-k/w algorithm. Our experimental evaluation systematically compares different top-k/w processing algorithms and shows that while competing algorithms offer either time efficiency at the expanse of space efficiency or vice-versa, our algorithms based on the probabilistic k-skyband are both time and space efficient.

Early Forest Fire Detection with Sensor Networks: Sliding Window Skylines Approach

Wireless sensor networks are widely used in environmental applications, like forest fire detection. Although forest fires occur relatively rarely, their number is increasing in Europe in the last years, so their manifestation must be early detected in order to prevent higher damages. To minimize needless communication between the sensor nodes for this usage, new data suppression technique using sliding window skylines is described in this paper. We experimentally evaluate our algorithm for continuous sliding windows skylines computation, and show its usability in practice.

Distributed processing of continuous sliding-window k-NN queries for data stream filtering

A sliding-window k-NN query (k-NN/w query) continuously monitors incoming data stream objects within a sliding window to identify k closest objects to a query. It enables effective filtering of data objects streaming in at high rates from potentially distributed sources, and offers means to control the rate of object insertions into result streams. Therefore k-NN/w processing systems may be regarded as one of the prospective solutions for the information overload problem in applications that require processing of structured data in real-time, such as the Sensor Web. Existing k-NN/w processing systems are mainly centralized and cannot cope with multiple data streams, where data sources are scattered over the Internet. In this paper, we propose a solution for distributed continuous k-NN/w processing of structured data from distributed streams. We define a k-NN/w processing model for such setting, and design a distributed k-NN/w processing system on top of the Content-Addressable Network (CAN) overlay. An extensive evaluation using both real and synthetic data sets demonstrates the feasibility of the proposed solution because it balances the load among the peers, while the messaging overhead within the P2P network remains reasonable. Moreover, our results clearly show the solution is scalable for an increasing number of queries and peers.

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.

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.

Adaptable secure communication for the Cloud of Things

Cloud of Things (CoT) is a novel concept driven by the synergy of the Internet of Things (IoT) and cloud computing paradigm. The CoT concept has expedited the development of smart services resulting in the proliferation of their real world deployments. However, new research challenges arise because of the transition of research‐driven and proof‐of‐concept solutions to commercial offerings, which need to provide secure, energy‐efficient, and reliable services. An open research issue in the CoT is to provide a satisfactory level of security between various IoT devices and the cloud. Existing solutions for secure CoT communication typically use devices with pre‐loaded and pre‐configured parameters, which define a static setup for secure communication. In contrast to existing pre‐configured solutions, we present an adaptable model for secure communication in CoT environments. The model defines six secure communication operations to enable CoT entities to autonomously and dynamically agree on the security protocol and cryptographic keys used for communication. Further on, we focus on device agreement and present an original solution, which uses the Agile Cryptographic Agreement Protocol in the context of CoT. We verify our solution by a prototype implementation of CoT device agreement based on required security level, which takes into account the capabilities of communicating devices. Our experimental evaluation compares the average processing times of the proposed secure communication operations demonstrating the viability of the proposed solution in real‐world deployments. Copyright