A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

in IEEE Communications Surveys and Tutorials (2019), 21(3, thirdquarter 2019), 2419-2465

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices ... [more ▼]

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas. [less ▲]

Transforming Collaboration Data into Network Layers for Enhanced Analytics

Scientific Conference (2019, February 25)

We consider the problem of automatically generating networks from data of collaborating researchers. The objective is to apply network analysis on the resulting network layers to reveal supplemental ... [more ▼]

We consider the problem of automatically generating networks from data of collaborating researchers. The objective is to apply network analysis on the resulting network layers to reveal supplemental patterns and insights of the research collaborations. In this paper, we describe our data-to-networks method, which automatically generates a set of logical network layers from the relational input data using a linkage threshold. We, then, use a series of network metrics to analyze the impact of the linkage threshold on the individual network layers. Moreover, results from the network analysis also provide beneficial information to improve the network visualization. We demonstrate the feasibility and impact of our approach using real-world collaboration data. We discuss how the produced network layers can reveal insights and patterns to direct the data analytics more intelligently. [less ▲]

Security, reliability and regulation compliance in Ultrascale Computing System

in Zomaya, A. Y.; Carretero, J.; Jeannot, E. (Eds.) Ultrascale Computing Systems (2019)

Ultrascale Computing Systems (UCSs) are envisioned as large-scale complex systems joining parallel and distributed computing systems that will be two to three orders of magnitude larger than today’s ... [more ▼]

Ultrascale Computing Systems (UCSs) are envisioned as large-scale complex systems joining parallel and distributed computing systems that will be two to three orders of magnitude larger than today’s systems (considering the number of Central Process Unit (CPU) cores). It is very challenging to find sustainable solutions for UCSs due to their scale and a wide range of possible applications and involved technologies. For example, we need to deal with heterogeneity and cross fertilization among HPC, large-scale distributed systems, and big data management. One of the challenges regarding sustainable UCSs is resilience. Another one, which attracted less interest in the literature but becomes more and more crucial with the expected convergence with the Cloud computing paradigm, is the notion of regulation in such system to assess the Quality of Service (QoS) and Service Level Agreement (SLA) proposed for the use of these platforms. This chapter covers both aspects through the reproduction of two articles: [1] and [2]. [less ▲]

Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

in IEEE Access (2019), 7

Wearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the ... [more ▼]

Wearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications. [less ▲]

Configurable Cost-Quality Optimization of Cloud-based VoIP

in Journal of Parallel and Distributed Computing (2019), 133

In this paper, we formulate configurable cloud-based VoIP call allocation problem as a special case of dynamic multi-objective bin-packing. We consider voice quality influenced by CPU stress, cost ... [more ▼]

In this paper, we formulate configurable cloud-based VoIP call allocation problem as a special case of dynamic multi-objective bin-packing. We consider voice quality influenced by CPU stress, cost contributed by the number of billing hours for Virtual Machines (VMs) provisioning, and calls placed on hold due to under-provisioning resources. We distinguish call allocation strategies by the type and amount of information used for allocation: knowledge-free, utilization-aware, rental-aware, and loadaware. We propose and study a variety of strategies with static and dynamic policies of VM provisioning. To study realistic scenarios, we consider startup delays for VM provisioning, and three configurable parameters: utilization threshold, rental threshold, and prediction interval. They can be configured and dynamically adapted to cope with different objective preferences, workloads, and cloud properties. We conduct comprehensive simulation on the real workload of the MIXvoip company and show that the proposed strategies outperform ones currently in-use. [less ▲]

Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud.

in IEEE Access (2019), 7

Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan ... [more ▼]

Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multiobjective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGAIII is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NAGA-III alone, E-NAGA-III with Peer-to-Peer clustering and E-NAGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed. [less ▲]

Collaborative Data Delivery for Smart City-oriented Mobile Crowdsensing Systems

in IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE, 2018 (2018, December)

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing ... [more ▼]

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing infrastructures. In typical MCS systems, sensing tasks are allocated and reported on individual-basis. In this paper, we investigate on collaboration among users for data delivery as it brings a number of benefits for both users and sensing campaign organizers and leads to better coordination and use of resources. By taking advantage from proximity, users can employ device-to-device (D2D) communications like Wi-Fi Direct that are more energy efficient than 3G/4G technology. In such scenario, once a group is set, one of its member is elected to be the owner and perform data forwarding to the collector. The efficiency of forming groups and electing suitable owners defines the efficiency of the whole collaborative-based system. This paper proposes three policies optimized for MCS that are compliant with current Android implementation of Wi-Fi Direct. The evaluation results, obtained using CrowdSenSim simulator, demonstrate that collaborative-based approaches outperform significantly individual-based approaches. [less ▲]

Why Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks

in Pervasive and Mobile Computing (2018)

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data ... [more ▼]

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly. [less ▲]

PRESENCE: Performance Metrics Models for Cloud SaaS Web Services

in Proc. of the 11th IEEE Intl. Conf. on Cloud Computing (CLOUD 2018) (2018, July)

UL HPC Tutorial: Building [custom] software with EasyBuild on the UL HPC platform

Presentation (2018, June)