PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International Conference on Smart Computing (SMARTCOMP-2018

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

SelCSP: A Framework to Facilitate Selection of Cloud Service Providers

With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients\u27 data. If loss of data occurs due to an outage, the customer\u27s business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider\u27s SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms

BioSmartSense: A Bio-Inspired Data Collection Framework for Energy-Efficient, QoI-Aware Smart City Applications

Recent years have seen a proliferation of intelligent (automated) decision support systems for various smart city applications such as energy management, transportation, healthcare, environment monitoring, and so on. A key enabler in the smart city paradigm is the Internet-of-Things (IoT) network of smart sensing and actuation devices assisting in real-time detection and monitoring of physical phenomena. The underlying IoT network must be energy-efficient for application sustainability and also quality of information (QoI)-aware for near-perfect device actuation. To this end, this paper proposes bioSmartSense, a novel bio-inspired distributed event sensing and data collection framework, based on the gene regulatory networks (GRNs) in living organisms. The idea is to make the sensing and reporting tasks energy-efficient through self-modulation of IoT device energy levels, analogous to the activation or repression of genes by the regulating proteins, called Transcription Factors (TFs). To support energy-efficient and QoI-aware information dissemination, we first customize a heuristic designed for the Maximum Weighted Independent Set problem encompassing both \u27quality\u27 and \u27quantity\u27 of sensed data, where the former depends on the device energy levels while the latter on the number of events sensed. We utilize the heuristic to propose a sub-optimal device selection mechanism constrained on the IoT network\u27s overall residual energy. Simulation experiments demonstrate that the bioSmartSense framework achieves better energy-efficiency while maximizing event reporting compared to a state-of-the-art data collection approach for smart city applications

BioMCS 2.0: A Distributed, Energy-Aware Fog-Based Framework for Data Forwarding in Mobile Crowdsensing

Mobile crowdsensing (MCS) paradigm enables users equipped with energy-constrained smart devices to participate in sensing and reporting of assigned tasks. To achieve seamless communication as well as effective energy and resource management, we leveraged the fog computing platform to propose a centralized, energy-efficient and robust data collection framework, called bioMCS, based on the topological properties of a biological network called transcriptional regulatory network. However, since MCS platforms may potentially entail a high number of mobile users and massive volumes of data traffic, we extend the current work under the name bioMCS 2.0 to conceive a distributed energy-aware data forwarding mechanism where the fog devices function as task data relay nodes. bioMCS 2.0 combines energy-awareness, abundance of subgraphs (called motifs) in the fog network and proximity to the base station to perform efficient task sensing and forwarding in a dynamic scenario where fog devices are both energy constrained and mobile. It also ensures quality of information by accepting task data from reliable smart devices. Extensive simulation on the map of New York City and realistic mobility models suggests that bioMCS 2.0 exhibits comparable performance in terms of data delivery, latency and energy efficiency in comparison with both random next hop (fog node) selection as well as centralized forwarding technique that rely on global network knowledge

Securing Loosely-Coupled Collaboration in Cloud Environment through Dynamic Detection and Removal of Access Conflicts

Online collaboration service has become a popular offering of present day Software-as-a-Service (SaaS) clouds. It facilitates sharing of information among multiple participating domains and accessing them from remote locations. Owing to loosely-coupled nature of such collaborations, access request from a remote user is made in the form of a set of permissions. The cloud vendor maps the requested permissions into appropriate local roles in order to allow resource access. However, coexistence of such multiple simultaneous role activation requests may introduce conflicts which violate the principle of security. In this paper, we propose a distributed secure collaboration framework which enables collaborating domains to detect and remove these conflicts. Two features of our framework are: (i) it requires only local information, and (ii) it detects and removes conflicts on-the-fly. Formal proofs have been provided to establish the correctness of our approach. Experimental results and qualitative comparison with related work demonstrate the efficacy of our approach in terms of response time, thus addressing the scalability requirement of cloud services

BioSmartSense+: A Bio-Inspired Probabilistic Data Collection Framework for Priority-Based Event Reporting in IoT Environments

Recent years have seen a widespread use of information and communication technology (ICT) in the implementation of smart city applications. A key enabler in the smart city paradigm is the Internet-of-Things (IoT), which facilitates automated real-time sensing, communication, and actuation, assisting in unmanned monitoring of physical phenomenon and supports intelligent decision making. Nevertheless, designing a smart and energy-efficient IoT network for sustainability and near-perfect device actuation is a major challenge. To address this, our preliminary work (Roy et al., 2019) proposed a gene regulatory network (GRN)-based distributed event sensing and data collection framework called bioSmartSense. It attempted to make sensing and reporting tasks energy-efficient through bio-inspired self-modulation of IoT device energy levels. In this paper we extend it, under the name bioSmartSense+, to conceive realistic sensing and reporting mechanisms by incorporating device heterogeneity, probabilistic sensing, and priority-based event reporting. For experimental study, we used both simulated and real data to evaluate energy and coverage-related performances. Experimental results establish the efficacy of our framework in terms of energy-efficiency and event reporting rate compared to a state-of-the-art data collection approach

Transcriptional Regulatory Network Topology with Applications to Bio-Inspired Networking: A Survey

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment - the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks

Leveraging Network Science for Social Distancing to Curb Pandemic Spread

COVID-19 has irreversibly upended the course of human life and compelled countries to invoke national emergencies and strict public guidelines. As the scientific community is in the early stages of rigorous clinical testing to come up with effective vaccination measures, the world is still heavily reliant on social distancing to curb the rapid spread and mortality rates. In this work, we present three optimization strategies to guide human mobility and restrict contact of susceptible and infective individuals. The proposed strategies rely on well-studied concepts of network science, such as clustering and homophily, as well as two different scenarios of the SEIRD epidemic model. We also propose a new metric, called contagion potential, to gauge the infectivity of individuals in a social setting. Our extensive simulation experiments show that the recommended mobility approaches slow down spread considerably when compared against several standard human mobility models. Finally, as a case study of the mobility strategies, we introduce a mobile application, MyCovid, that provides periodic location recommendations to the registered app users

Publish or Drop Traffic Event Alerts? Quality-Aware Decision Making in Participatory Sensing-Based Vehicular CPS

Vehicular cyber-physical systems (VCPS), among several other applications, may help address an everincreasing challenge of traffic congestion in large cities. Nevertheless, VCPS can be hindered by information falsification problem, resulting due to the wrong perception of a traffic event or deliberate faking by the participating vehicles. Such information fabrication causes the re-routing of vehicles and artificial congestion, leading to economic, safety, environmental, and health hazards. Thus, it is imperative to infer truthful traffic information in real-time to restore the operational reliability of the VCPS. In this work, we propose a novel reputation scoring and decision support framework, called Spoofed and False Report Eradicator (SAFE), which offers a cost-effective and efficient solution to handle information falsification problem in the VCPS domain. The framework includes humans in the sensing loop by exploiting the paradigm of participatory sensing, a concept of a mobile security agent (MSA) to nullify the effects of deliberate false contribution, and a variant of the distance bounding mechanism to thwart location-spoofing attacks. A regression-based model integrates these effects to generate the expected truthfulness of a participant\u27s contribution. To determine if any contribution is true or false, a generalized linear model is used to transform the expected truthfulness into a Quality of Contribution (QoC) score. The QoC of different reports is aggregated to compute user reputation. Such reputation enables classification of different participation behaviors. Finally, an Expected Utility Theory (EUT)-based decision model is proposed that utilizes the reputation score to determine if event-specific information should be published or dropped. To evaluate the SAFE framework through experimental study, we used both simulated and real data to compare its reputation-based user segregation performance with stateof- the-art frameworks. Experimental results exhibit that SAFE captures the fine differences in participants\u27 behavior through the quality and quantity of participation, and the accuracy of their informed location. It also significantly improves operational reliability through publishing the information of only legitimate events