Extending queuing networks to assess mobile crowdsensing application performance

Copyright © 2016 EAI. The widespread and pervasive adoption of smart devices is boosting Internet of Things and contribution-based paradigms. In particular, Mobile Crowdsensing (MCS), due to its big potential of sharing and collecting large population of contributors-devices, is acquiring interest. Devices such as smartphones and smart boards are equipped with different sensors and actuators able to probe data about the physical environment. In a typical MCS scenario, data produced by sensors are sent to the remote server, where they are collected and processed by the applications. To exploit the MCS paradigm in large-scale business contexts the quality of service of MCS applications must be monitored and guaranteed. Therefore, techniques and tools able to represent and evaluate MCS system quality attributes such as performance and energy consumption are required. However, modeling MCS system is quite challenging since not only the number of users but also the number of contributors may vary. In this paper, we propose to adopt queuing networks, a well-known formalism able to deal with large number of requests, to address this issue. In particular we introduce and implement a new policy allowing the number of server to be variable. The proposed model is then adopted in the evaluation of an example, providing interesting insights on contribution, provisioning and usage impacts in terms of some performance and energy consumption metrics

Characterization and evaluation of mobile crowdsensing performance and energy indicators

Mobile Crowdsensing (MCS) is a contribution-based paradigm involving mobiles in pervasive application deployment and operation, pushed by the evergrowing and widespread dissemination of personal devices. Nevertheless, MCS is still lacking of some key features to become a disruptive paradigm. Among others, control on performance and reliability, mainly due to the contribution churning. For mitigating the impact of churning, several policies such as redundancy, over-provisioning and checkpointing can be adopted but, to properly design and evaluate such policies, specific techniques and tools are required. This paper attempts to fill this gap by proposing a new technique for the evaluation of relevant performance and energy figures of merit for MCS systems. It allows to get insights on them from three different perspectives: end users, contributors and service providers. Based on queuing networks (QN), the proposed technique relaxes the assumptions of existing solutions allowing a stochastic characterization of underlying phenomena through general, non exponential distributions. To cope with the contribution churning it extends the QN semantics of a service station with variable number of servers, implementing proper mechanisms to manage the memory issues thus arising in the underlying process. This way, a preliminary validation of the proposed QN model against an analytic one and an in depth investigation also considering checkpointing have been performed through a case study

Extending queuing networks to assess mobile crowdsensing application performance

The widespread and pervasive adoption of smart devices is boosting Internet of Things and contribution-based paradigms. In particular, Mobile Crowdsensing (MCS), due to its big potential of sharing and collecting large population of contributors-devices, is acquiring interest. Devices such as smartphones and smart boards are equipped with different sensors and actuators able to probe data about the physical environment. In a typical MCS scenario, data produced by sensors are sent to the remote server, where they are collected and processed by the applications. To exploit the MCS paradigm in large-scale business contexts the quality of service of MCS applications must be monitored and guaranteed. Therefore, techniques and tools able to represent and evaluate MCS system quality attributes such as performance and energy consumption are required. However, modeling MCS system is quite challenging since not only the number of users but also the number of contributors may vary. In this paper, we propose to adopt queuing networks, a well-known formalism able to deal with large number of requests, to address this issue. In particular we introduce and implement a new policy allowing the number of server to be variable. The proposed model is then adopted in the evaluation of an example, providing interesting insights on contribution, provisioning and usage impacts in terms of some performance and energy consumption metrics

Extending queuing networks to assess mobile crowdsensing application performance

Copyright © 2016 EAI. The widespread and pervasive adoption of smart devices is boosting Internet of Things and contribution-based paradigms. In particular, Mobile Crowdsensing (MCS), due to its big potential of sharing and collecting large population of contributors-devices, is acquiring interest. Devices such as smartphones and smart boards are equipped with different sensors and actuators able to probe data about the physical environment. In a typical MCS scenario, data produced by sensors are sent to the remote server, where they are collected and processed by the applications. To exploit the MCS paradigm in large-scale business contexts the quality of service of MCS applications must be monitored and guaranteed. Therefore, techniques and tools able to represent and evaluate MCS system quality attributes such as performance and energy consumption are required. However, modeling MCS system is quite challenging since not only the number of users but also the number of contributors may vary. In this paper, we propose to adopt queuing networks, a well-known formalism able to deal with large number of requests, to address this issue. In particular we introduce and implement a new policy allowing the number of server to be variable. The proposed model is then adopted in the evaluation of an example, providing interesting insights on contribution, provisioning and usage impacts in terms of some performance and energy consumption metrics