Providing Long-Term Participation Incentive in Participatory Sensing

Providing an adequate long-term participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under (current) information asymmetry. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% to 90%) and social performance (e.g., increasing the social welfare by 15% to 80%).Comment: This manuscript serves as the online technical report of the article published in IEEE International Conference on Computer Communications (INFOCOM), 201

STR amplification of DNA mixtures: fidelity of contributor proportion when calculated from DNA profile data using known mixture samples

DNA mixtures are frequently encountered in forensic casework especially in cases of sexual assault. When evidence is recovered, the sample may have come from multiple contributors in different proportions. The first part of this study examines the fidelity of contributor proportions by using the residual to analyze known mixture samples. The coefficient of determination between the expected and observed proportions was also determined and used to assess the fidelity of mixture proportions. The second part of this study involved separating major and minor contributors in a mixture by characterizing the observed proportions. Results for the 2-person mixture show that as the mass of amplified DNA decreases, the number of allele dropouts increases. Furthermore, as mass decreases, the level of variation between the expected and observed proportions increases, as determined by the residuals and the coefficients of determination. In addition, as mixture proportions become more disparate the amount of variations between the expected and observed proportions are not as great as the mass. For the 3-person mixtures, as mass decreases, the residuals increase. Also, when the coefficient of determination of the 3-person mixtures were compared to those obtained with the 2-person mixtures, it was determined that the R2 were larger for the former. This was a result of higher total amplification masses. In mixture 1:2/2:1, major and minor proportions are not distinguishable In mixture 1:4/4:1, major and minor proportions can be distinguished at 1 ng. In mixture 1:9/9:1, proportions are distinguishable at 1, and 0.5 ng. Mixtures could not be distinguished at the 0.25 ng level, despite proportion and is the result of the increase in variation with decreasing mass

Distributed Time-Sensitive Task Selection in Mobile Crowdsensing

With the rich set of embedded sensors installed in smartphones and the large number of mobile users, we witness the emergence of many innovative commercial mobile crowdsensing applications that combine the power of mobile technology with crowdsourcing to deliver time-sensitive and location-dependent information to their customers. Motivated by these real-world applications, we consider the task selection problem for heterogeneous users with different initial locations, movement costs, movement speeds, and reputation levels. Computing the social surplus maximization task allocation turns out to be an NP-hard problem. Hence we focus on the distributed case, and propose an asynchronous and distributed task selection (ADTS) algorithm to help the users plan their task selections on their own. We prove the convergence of the algorithm, and further characterize the computation time for users' updates in the algorithm. Simulation results suggest that the ADTS scheme achieves the highest Jain's fairness index and coverage comparing with several benchmark algorithms, while yielding similar user payoff to a greedy centralized benchmark. Finally, we illustrate how mobile users coordinate under the ADTS scheme based on some practical movement time data derived from Google Maps

Social Capital, Rehabilitation, Tradition: Support for Restorative Justice in Japan and Australia

This paper investigates the attitudes and beliefs that the public hold about criminal behaviour in Japanese and Australian society, with a view to uncovering sources of resistance to, and support for, restorative justice. The study draws on a survey of 1,544 respondents from Japan and 1,967 respondents from Australia. In both societies, restorative justice met with greater acceptance among those who were (1) strong in social capital, (2) believed in offender reintegration and rehabilitation, (3) saw benefits for victims in forgiveness, and (4) were advocates for victims' voices being heard and amends made. The alternative 'just deserts' and deterrence models for dealing with crime were grounded in attitudes of punitiveness and fear of moral decay, and reservations about the value of reintegrating and rehabilitating offenders. Like restorative justice supporters, 'just deserts' and deterrence supporters expressed concern that victims' voices be heard and amends made. Winning public support for competing institutional arrangements may depend on who does best in meeting expectations for meeting the needs of victims