Abstract-Smartphones can unfold the full potential of crowdsourcing, allowing users to transparently contribute to complex and novel problem solving. We present the intrinsic characteristics of smartphones, a taxonomy that classi es the emerging eld of mobile crowdsourcing and three in-house applications that optimize location-based search and similarity services over data generated by a crowd: (i) SmartTrace + enables similarity matching between a given pattern and the trajectories of smartphone users, keeping the target trajectories private; (ii) Crowdcast enables location-based interaction by ef ciently calculating the k nearest neighbors for each user at all times; (iii) SmartP2P optimizes energy, time and recall of search in a mobile social community for objects generated by a crowd. We show how these applications can be deployed on SmartLab, a novel cloud of 40+ Android devices deployed at University of Cyprus, providing an open testbed that facilitates research and development of applications on smartphones at a massive scale.

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a b s t r a c tThe K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing the network coverage and lifetime objectives under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem-specific knowledge for increasing its efficiency. In a constrained Multi-objective Optimization Problem (such as K-connected DPAP), the search space is divided into feasible and infeasible regions. Therefore, problem-specific operators are designed for MOEA/D to direct the search into optimal, feasible regions of the space. Namely, a DPAP-specific population initialization that seeds the initial solutions into promising regions, problem-specific genetic operators (i.e. M-tournament selection, adaptive crossover and mutation) for generating good, feasible solutions and a DPAP-specific Repair Heuristic (RH) that transforms an infeasible solution into a feasible one and maintains the MOEA/D's efficiency simultaneously. Simulation results have shown the importance of each proposed operator and their interrelation, as well as the superiority of the DPAP-specific MOEA/D against the popular constrained NSGA-II in several WSN instances.