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This study 1 shows that appropriate human interaction can benefit a swarm of robots to achieve goals more efficiently. A set of desirable features for human swarm interaction is identified based on the principles of swarm robotics. Human swarm interaction architecture is then proposed that has all of the desirable features. A swarm simulation environment is created that allows simulating a swarm behavior in an indoor environment. The swarm behavior and the results of user interaction are studied by considering radiation source search and localization application of the swarm. Particle swarm optimization algorithm is slightly modified to enable the swarm to autonomously explore the indoor environment for radiation source search and localization. The emergence of intelligence is observed that enables the swarm to locate the radiation source completely on its own. Proposed human swarm interaction is then integrated in a simulation environment and user evaluation experiments are conducted. Participants are introduced to the interaction tool and asked to deploy the swarm to complete the missions. The performance comparison of the user guided swarm to that of the autonomous swarm shows that the interaction interface is fairly easy to learn and that user guided swarm is more efficient in achieving the goals. The results clearly indicate that the proposed interaction helped the swarm achieve emergence.

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This study proposes a new parameter for evaluating longevity of wireless sensor networks after showing that the existing parameters do not properly evaluate the performance of algorithms in increasing longevity. This study also proposes an ant inspired Collaborative Routing Algorithm for Wireless Sensor Network Longevity (CRAWL) that has scalability and adaptability features required in most wireless sensor networks. Using the proposed longevity metrics and implementing the algorithm in simulations, it is shown that CRAWL is much more adaptive to non-uniform distribution of available energy in sensor networks. The performance of CRAWL is compared to that of a non-collaborative algorithm.