Abstract-This paper describes an automatic mechanism for drawing metro maps. We apply multicriteria optimization to find effective placement of stations with a good line layout and to label the map unambiguously. A number of metrics are defined, which are used in a weighted sum to find a fitness value for a layout of the map. A hill climbing optimizer is used to reduce the fitness value, and find improved map layouts. To avoid local minima, we apply clustering techniques to the map -the hill climber moves both stations and clusters when finding improved layouts. We show the method applied to a number of metro maps, and describe an empirical study that provides some quantitative evidence that automatically-drawn metro maps can help users to find routes more efficiently than either published maps or undistorted maps. Moreover, we found that, in these cases, study subjects indicate a preference for automatically-drawn maps over the alternatives.

We describe a system to automatically generate metro maps using a multicriteria approach. We have implemented a hill climbing optimizer which uses a fitness score generated from a sum of several aesthetic metrics. This is used to move from the initial geographic layout of the map to a schematic layout that is intended to aid travellers' navigation. We describe the software and show its application to a number of real world metro maps.

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Abstract. In Wireless Sensor Network (WSN) applications, sensor nodes are often deployed in harsh environments. Routine maintenance, fault detection and correction is difficult, infrequent and expensive. Furthermore, for long-term deployments in excess of a year, a node's limited power supply tightly constrains the amount of processing power and long-range communication available.In order to support the long-term autonomous behaviour of a WSN system, a self-diagnostic algorithm implemented on the sensor nodes is needed for sensor fault detection. This algorithm has to be robust, so that sensors are not misdiagnosed as faulty to ensure that data loss is kept to a minimum, and it has to be light-weight, so that it can run continuously on a low power microprocessor for the full deployment period. Additionally, it has to be self-adapative so that any long-term degradation of sensors is monitored and the self-diagnostic algorithm can continuously revise its own rules to accomodate for this degradation. This paper describes the development, testing and implementation of a heuristically determined, robust, self-diagnostic algorithm that achieves these goals.