Abstract-We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public.

We consider the problem of finding efficient methods to update forwarding rules in Software Defined Networks (SDNs). While the original and updated set of rules might both be consistent, disseminating the rule updates is an inherently asynchronous process, resulting in potentially inconsistent states. We highlight the inherent trade-off between the strength of the consistency property and the dependencies it imposes among rule updates at different switches; these dependencies fundamentally limit how quickly the SDN can be updated. Additionally, we consider the impact of resource constraints and show that fast blackhole free migration of rules with memory limits is NPhard for the controller. For the basic consistency property of loop freedom, we prove that maximizing the number of loop free update rules is NP-hard for interval-based routing and longestprefix matching. We also consider the basic case of just one destination in the network and show that the greedy approach can be nearly arbitrarily bad. However, minimizing the number of not updated rules can be approximated well for destinationbased routing. For applying all updates, we develop an update algorithm that has a provably minimal dependency structure. We also sketch a general architecture for consistent updates that separates the twin concerns of consistency and efficiency, and lastly, evaluate our algorithm on ISP topologies.

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project's objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

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