Why energy matters? Profiling energy consumption of mobile crowdsensing data collection frameworks

Abstract

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.

Performance evaluation of hybrid crowdsensing systems with stateful CrowdSenSim 2.0 simulator

“…1 , parameters such as the algorithms used as well as the number of runs can be specified in the Simulation Configuration file. The simulator implements DDF, PCS, and PDA algorithms as in [27] , in particular, PDA has been re-designed in its global stateful version [28] (Section 5 ). Note that, in order for the algorithmic-level parallelism to take place, all the different simulation configurations must observe the same list of events, which means that the mobility of each user must be unaltered.…”

“…In order to efficiently validate CrowdSenSim 2.0, we referred to an energy consumption analysis of the DDF data collection algorithm originally proposed in [26] that was implemented and practically evaluated in [27] . DDF is a locally stateful data collection algorithm in which participants keep on generating data up to a certain threshold of energy consumption depending on their battery capacity.…”

“…In this section, we outline our Asymptotic Opportunistic algorithm for Joint Fairness and Satisfaction index (AO-JFS) that was initially proposed in [28] along with its simplified versions AO-F and AO-S. Both variants are implemented in CrowdSenSim 2.0 as members of a family of algorithms called Probabilistic Distributed Algorithms (PDA) [27] . Originally, performance evaluation was conducted using an ad-hoc simulator.…”

“…A similar version of such algorithm was implemented in the original CrowdSenSim [27] and called PDA, although CrowdSenSim 2.0 for its stateful approach would have been required to assess AO-JFS properly. Indeed, AO-JFS requires the central entity to be aware of all data delivered by the participants at each instant of time in order to correctly calculate the .…”

Locally reconfigurable Self Organizing Feature Map for high impact malicious tasks submission in Mobile Crowdsensing

“…As stated in [19] , batteries are critical components of MCS systems whereas sensing, processing and storage resources of mobile devices are of the paramount importance as well. Given these, since users are allowed to create and submit sensing tasks to an MCS platform, MCS platforms and participants are vulnerable to Denial of Service (DoS)-like attacks initiated by malicious users who submit fake tasks which interfere with the normal execution of other tasks in the same MCS system [20] .…”