In this paper, we present a protocol for dynamically maintaining a degree-bounded delay sensitive spanning tree in a decentralized way on overlay networks. The protocol aims at repairing the spanning tree autonomously even if multiple nodes' leave operations or failures (disappearances) occur simultaneously or continuously in a specified period. It also aims at maintaining the diameter (maximum delay) of the tree as small as possible. The simulation results using ns-2 have shown that the protocol could keep reasonable diameters compared with the existing centralized static algorithm even if many nodes' participations and disappearances occur frequently.

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This paper presents a scheduling algorithm for a set of wireless stations such as road-side access points for vehicular networks and outdoor WiFi stations, which are deployed in wide urban areas and may compete with each other for limited wireless resources. Different from a number of conventional approaches most of which consider detailed information on individual stations and signal interference among them, we focus more on geography of the areas of interest, and provide a novel algorithm that pursues the best balance among (i) optimality of resource utilization, (ii) robustness to new station installation and traffic demand, and (iii) scalability to the population of stations and area size. We have confirmed the performance by experimental simulations with several scenarios, and the applicability of approach has been testified by a case study on a scheduling problem for roadside access points of vehicular networks in cooperation with a manufacturing corporation.

In this paper, we deal with a sensor placement optimization problem for multi-point pedestrian flow monitoring systems, and provide an efficient algorithm. Our goal is to build a realistic and accurate model of the monitoring systems' pedestrian flow estimation performance in terms of their estimation errors. Also our model can represent sensor placement considering the number, types, locations and capabilities of sensors in urban scenarios. To this goal, we formulate the sub-problem of estimating accuracy achieved by a given monitoring system under a given placement of sensors. Using a solver for this sub-problem as a sub-module, we also design an algorithm to determine the optimal sensor placement. This algorithm employs a simulated annealing (SA) based approach and iteratively improve solutions to converge to near optimal solutions. Through performance evaluation using our HumanS simulator [1], which simulates human detection sensors, pedestrian behavior and floor structures altogether, we have verified that a derived sensor placement by our proposed method could detect pedestrian flows with high accuracy for an underground city and its estimation error was about 1 percent.