This work addresses the question of whether collective sensing allows for the emergence of groups from a population of individuals without predetermined behaviors. Experiments are run in an agent-based evolutionary model of a foraging task, where the fitness of the agents depends on their foraging strategy. Agents compete for the same limited resources and neither the environment nor inter-group dynamics benefit groups over individuals. The foraging strategy of agents is determined by a model-free neural network, which leaves agent behavior unrestricted.

Experiments demonstrate that gregarious behavior is not the evolutionary-fittest strategy if resources are abundant, thus invalidating previous findings in a specific region of the parameter space. In other words, resource scarcity makes gregarious behavior so valuable as to make up for the increased competition over the few available resources. This result is obtained with a model-free approach which allows evolution to select from an unconstrained set of behavioral models. Furthermore, it is shown that a population of solitary agents can evolve gregarious behavior in response to a sudden scarcity of resources, thus individuating a possible mechanism that leads to gregarious behavior in nature.

Keywords: Collective Sensing, Foraging, Group Behavior, Neural Networks

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Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information open up tremendous opportunities for managing and regulating smart city infrastructures in a more efficient and sustainable way.

The privacy-enhancing aggregation of distributed sensor data such as residential energy consumption or traffic information, is the research focus and challenge tackled in this paper. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services.

A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared to a baseline scenario in which each individual shares data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario, while accuracy is preserved. Furthermore, if groups are large enough, privacy improves independently of the individual’s privacy choices. Intergroup effects such as the influence of individual choices on privacy of other group members are studied. Finally, several grouping strategies are evaluated and compared using real-world data from two smart city pilot projects. Implications for the design of incentive mechanisms are discussed.

Keywords: Privacy, Internet of Things, Smart City, Network, Sensor, Grouping, Agent, Aggregation

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