The accuracy of target location and the coverage range of sensor network are two factors that affect each other in target tracking. When the flocking sensor network has a larger coverage area, it can increase the range of detecting target and the scope of environmental information. The network can also pass the information to a query source or other sensors which do not belong to the flocking network. However, the accuracy of measurements at sensors may be affected by the distances between the target and the sensors. We use mobile sensors as agents in flocking algorithm for target tracking. Every mobile sensor exchanges information with its neighbors, and keeps an appropriate separation distance with neighbors to maintain flocking. Flocking algorithm is a distributed control method for mobile sensor which can catch up the target and maintain flocking formation. In the thesis, we derive the cost function based on the accuracy of target positioning and range coverage. The proposed adaptive flocking algorithm combines the amount of information and the distance changes between neighbors based on the cost function. Each mobile sensor adaptively adjusts distance separation with all its neighbors within communication range. Sensors closer to the target shortens the separation distance between neighbors, therefore they will move toward the target and obtain better measurement. Kalman-consensus information filter is used for target positioning. The accuracy of target position can therefore be improved in the overall network. On the other hand, the sensors located far from the target will widen the distance separation between neighbors to expand the overall network area. In the thesis, we use Kalman-consensus information filter to estimate the state of a target, and use adaptive flocking algorithm for maintaining the formation of mobile sensors. Simulations show that adaptive flocking algorithm effectively improves location accuracy while maintaining approximate generally same coverage area when compared with other methods.

Participatory Sensing (PS) systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their cellular phones. Users agree to use their cellular phone resources to sense and transmit the data of interest because these data will be used to address a collective problem that otherwise would be very difficult to assess and solve. However, this new data collection paradigm has not been very successful yet mainly because of the lack of incentives for participation and privacy concerns. Without adequate incentive and privacy guaranteeing mechanisms most users will not be willing to participate. This thesis concentrates on incentive mechanisms for user participation in PS system. Although several schemes have been proposed thus far, none has used location information and imposed budget and coverage constraints, which will make the scheme more realistic and efficient. A recurrent reverse auction incentive mechanism with a greedy algorithm that selects a representative subset of the users according to their location given a fixed budget is proposed. Compared to existing mechanisms, GIA (i.e., Greedy Incentive Algorithm) improves the area covered by more than 60 percent acquiring a more representative set of samples after every round, i.e., reduces the collection of unnecessary (redundant) data, while maintaining the same number of active users in the system and spending the same budget.

The goal of this thesis is to analyze and to find solutions of optimization problems derived from automation of functional verification of hardware using artificial neural networks. Verification of any integrated circuit (so called Design Under Verification, DUV) using technique called coverage-driven verification and universal verification methodology (UVM) is carried out by sending stimuli inputs into DUV. The verification environment continuously monitors percentual coverage of DUV functionality given by the specification. In current context, coverage stands for measurable property of DUV, like count of verified arithemtic operations or count of executed lines of code. Based on the final coverage, it is possible to determine whether the coverage of DUV is high enough to declare DUV as verified. Otherwise, the input stimuli set needs to change in order to achieve higher coverage. Current trend is to generate this set by technique called constrained-random stimulus generation. We will practice this technique by using pseudorandom program generator (PNG). In this paper, we propose multiple solutions for following two optimization problems. First problem is ongoing modification of PNG constraints in such a way that the DUV can be verified by generated stimuli as quickly as possible. Second one is the problem of seeking the smallest set of stimuli such that this set verifies DUV. The qualities of the proposed solutions are verified on 32-bit application-specific instruction set processors (ASIPs) called Codasip uRISC and Codix Cobalt.