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

Energy Efficient Data Transmission Scheme for Internet of Things Applications

Recently, there is a rapid increase in the use of Internet of Things (IoT) technology, and it is envisaged that in the forthcoming days, billions of devices and things are going to be interconnected among themselves with the help of Internet. To make this technology self-sustainable, there is a need for an incessant energy supply that can be achieved through green energy harvesting. IoT has attracted the attention of researchers as well as practitioners all over the globe by serving as an important architecture for communication systems, but the terminal devices used in IoT are resource-constrained, which results in low energy storage capacity and low computing power. Among several tasks that need to be performed at the level of IoT node, the data transmission is the most energy intensive phase. To provide continuous power to nodes used in IoT systems, it is imperative that the available energy source should be used judiciously and in an optimized manner. In this paper, an energy efficient data transmission scheme for IoT devices has been proposed. The result obtained through extensive experiments depicts that there is a high potential for saving energy during the process of data transmission

Experimental Evaluation of Indoor Localization Methods for Industrial IoT Environment

294-307With the evolution of new technology, GPS brings a lot of revolution in the Localization system but it is not an effective solution in Indoor Environment. This is because of the fact that the signals coming from the satellite are attenuated, absorbed, scattered by the walls, roofs, and other objects. Due to this, lots of errors may arise in the system. To overcome this problem sensor node based localization system is used for mobile IoT domain. Recently, in IoT based system, various sensor nodes have been used in different kinds of localization based applications such as Location-Based Services (LBS) and Proximity-Based Services (PBS). However, such sensor nodes may not be stable in every environment to provide an accurate positioning. Although there are different localization techniques are available to find out the location of any object, but there is a common challenge of finding best localization technique suitable for most of the environment. This work investigates different existing indoor localization methods for its practical suitability in Lab and actual Industrial environment for deployment on IoT nodes. A mobile application has also been developed to implement four state-of-the-art localization techniques for getting the position and calculating its accuracy in different environments. During the experiment, BLE Beacons are used as sensor nodes due to their ease of deployment, lower complexity, lower cost, and higher power consumption. The error in the accuracy of estimated position got calculated in terms of Average Error. According to the experimental result in real environments it has been revealed that the Weighted Centroid Localization technique provides better accuracy in industrial as well as laboratory environment

Energy Efficient Data Transmission Scheme for Internet of Things Applications

633-642Recently, there is a rapid increase in the use of Internet of Things (IoT) technology, and it is envisaged that in the forthcoming days, billions of devices and things are going to be interconnected among themselves with the help of Internet. To make this technology self-sustainable, there is a need for an incessant energy supply that can be achieved through green energy harvesting. IoT has attracted the attention of researchers as well as practitioners all over the globe by serving as an important architecture for communication systems, but the terminal devices used in IoT are resource-constrained, which results in low energy storage capacity and low computing power. Among several tasks that need to be performed at the level of IoT node, the data transmission is the most energy intensive phase. To provide continuous power to nodes used in IoT systems, it is imperative that the available energy source should be used judiciously and in an optimized manner. In this paper, an energy efficient data transmission scheme for IoT devices has been proposed. The result obtained through extensive experiments depicts that there is a high potential for saving energy during the process of data transmission

KITE: an efficient scheme for trust estimation and detection of errant nodes in vehicular cyber-physical systems

Advancement in sensing and networking technology enhances the capability of mechatronic systems by many folds and improves the way it interacts with its physical environment. However, the dependency on network technology introduces a new set of challenges. Vehicular cyber-physical system (VCPS) is one such example that came into existence with the objective of enhancing road safety and convenience. In VCPS, the safety applications rely on the correctness of the kinematics information received from other vehicles in the vicinity. Incorrect kinematics information may result in increase in road hazards and creates more confusion than convenience. Irrespective of the reasons, detection of such errant nodes in VCPS is imperative. Moreover, the trust estimation of the nodes can play an important role to classify them as genuine and errant. In this work, an efficient scheme is proposed for trust estimation and detection of those errant nodes that disseminate incorrect kinematics information in VCPS. Simulations using ns-2 and VanetMobiSim under realistic vehicular network environment are conducted to evaluate the performance of the proposed scheme. The results are encouraging and reveal that the proposed scheme is suitable for implementation in real vehicular environment for detection of such errant nodes with high accuracy

Experimental Evaluation of Indoor Localization Methods for Industrial IoT Environment

 With the evolution of new technology, GPS brings a lot of revolution in the Localization system but it is not an effective solution in Indoor Environment. This is because of the fact that the signals coming from the satellite are attenuated, absorbed, scattered by the walls, roofs, and other objects. Due to this, lots of errors may arise in the system. To overcome this problem sensor node based localization system is used for mobile IoT domain. Recently, in IoT based system, various sensor nodes have been used in different kinds of localization based applications such as Location-Based Services (LBS) and Proximity-Based Services (PBS). However, such sensor nodes may not be stable in every environment to provide an accurate positioning. Although there are different localization techniques are available to find out the location of any object, but there is a common challenge of finding best localization technique suitable for most of the environment. This work investigates different existing indoor localization methods for its practical suitability in Lab and actual Industrial environment for deployment on IoT nodes. A mobile application has also been developed to implement four state-of-the-art localization techniques for getting the position and calculating its accuracy in different environments. During the experiment, BLE Beacons are used as sensor nodes due to their ease of deployment, lower complexity, lower cost, and higher power consumption. The error in the accuracy of estimated position got calculated in terms of Average Error. According to the experimental result in real environments it has been revealed that the Weighted Centroid Localization technique provides better accuracy in industrial as well as laboratory environment

CAT: Consensus-assisted trust estimation of MDS-equipped collaborators in vehicular ad-hoc network

The trust establishment in highly mobile ad-hoc network is a challenging task. Vehicular ad-hoc network (VANET) poses trust estimation problem in dynamic network environment. Presence of misbehavior detection schemes (MDS), in the vehicular node, safeguards them up to a certain extent from misbehaving vehicles but existing MDS algorithms have their own limitations in terms of capability and accuracy. In this scenario, vehicles are supposed to collaborate among themselves for getting better assessment of the misbehaving peers. However, the trustworthiness of collaborators is always a questionable factor subject to verification. Trust estimation of the collaborating nodes is essentially required to provide supplementary information for more accurate decision about the adversaries. In this paper, algorithms have been proposed to estimate trustworthiness of the MDS-equipped collaborating nodes with the help of consensus mechanism. After comparison of different proposed trust-estimation algorithms in simulated environment, results reveal that the unsupervised k-means based consensus clustering algorithm is an effective solution for precise estimation of the trustworthiness of the collaborating node

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

Enhancing Reliability of Vehicular Participatory Sensing Network: A Bayesian Approach

Participatory sensing (PS) is an emerging socio-technological paradigm in which citizens voluntarily participate and contribute to a distributed information system using applications installed in their hand-held devices. It can be found in a number of real-life applications, viz. traffic monitoring, air/sound pollution, garbage monitoring, social networking, commodity pricing, and so on. In these systems, information sensed by the user helps the peers in decision making. Present work considers vehicular participatory sensing systems, where registered user senses (perceives) the traffic incident and submits its report(s) to a PS application server. PS application server in turn, broadcasts those reports as alerts to its subscribers. To promote the participation, the PS systems used to have incentive schemes for the participants. However, a common problem in participatory sensing is the generation of false reports either due to wrong perception of an event or to maliciously increase the degree of participation to gain undue incentives. Such false reports make the usage of the PS system unreliable and vulnerable to the illusion attack. This work proposes a novel approach to make PS applications more reliable by identifying and filtering out the falsely reported event through automated confidence assignment based on a probabilistic model. Waze traffic alerts have been used as the dataset to validate the proposed filtering mechanism. Finally, simulation-based experiments and performance evaluation have been done to demonstrate that the proposed approach is relatively accurate

PS-Sim: A framework for scalable data simulation and incentivization in participatory sensing-based smart city applications

The widespread penetration of smartphone has paved the way for a new paradigm of pervasive computing, known as the participatory sensing (PS). In PS, a human user explicitly performs the tasks of sensing and reporting, typically in lieu of incentives. One major limitation with PS is the sparsity of data owing to the lack of active participation, thus inhibiting large scale real-life experiments for the research community. In our preliminary work (Barnwal et al., 2018), we propose a spatio-temporal event occurrence and report generation based data simulation framework called PS-Sim. This paper extends the PS-Sim framework with a novel budget allocation mechanism for incentivizing participants. The allocation mechanism guarantees the presence of a threshold number of active participants and also ensures the reporting of the significant fraction of events for sustainability of PS applications. The simulation environment provided by the PS-Sim framework replicates real participation and event occurrence behaviors, which is expected to enable the domain experts to investigate and assess the requisites (benefits and challenges) of introducing smart city applications. As a part of the evaluation of the budget allocation mechanism, we study its performance under varying effects of reward and participation, and establish its fairness as far as the remuneration of active participants is concerned