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Distributed sensor networks are an attractive area for research in agent systems. This is due primarily to the level of information available in applications where sensing technology has improved dramatically. These include energy systems and area coverage where it is desirable for sensor networks to have the ability to self-organize and be robust to changes in network structure. The challenges presented when investigating distributed sensor networks for such applications include the need for small sensor packages that are still capable of making good decisions to cover areas where multiple types of information may be present. For example in energy systems, singular areas in power plants may produce several types of valuable information, such as temperature, pressure, or chemical indicators.The approach of the work presented in this paper provides agent fitness functions for use with a neuro-evolutionary algorithm to address some of these challenges. In particular, we show that for self-organization and robustness to network changes, it is more advantageous to evolve individual policies, rather than a shared policy that all sensor units utilize. Further, we show that using a difference objective approach to the decomposition of system-level fitness functions provides a better target for evolving these individual policies. This is because the difference evaluation for fitness provides a cleaner signal, while maintaining vital information from the system level that implicitly promotes coordination among individual sensor units in the network.

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Payload examination using Deep Packet Inspection (DPI) offers (infrastructure) providers a whole new range of use cases, many of them with a potential to eavesdrop on non-public communication. Current research is almost exclusively concerned with raising this capabilities on a technological level. Critical voices about DPI's impact on the Internet with regard to privacy, net neutrality, and its other implications are raised, however often not within research communities but rather by politically interested groups. In fact, no definite method allowing detection of DPI is known. In this paper we present five different approaches targeting this problem. While starting points for DPI detection are given, including leakage of internal data or software errors, not all of of the presented approaches can be simulated or verified at all and none so far has been tested in real world settings.