Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing “do-operations”. The obtained CBN could then be employed for causal inference. The main challenges of this approach included: “non-doable variables” and limited scalability. To address these issues, we propose three extensions: i) early pruning weakly correlated relations to reduce the number of required do-operations; ii) introducing aggregate variables that summarize relations between weakly-coupled sub-systems; iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. We illustrate and evaluate the efficiency of these contributions via examples from the smart home and power grid domain. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way towards applications in large CPS.
The concept of pulse-coupled oscillators for self-organized synchronization has been applied to wireless systems. Putting theory into practice, however, faces certain obstacles, particularly in radio technologies that cannot implement pulses but use common messages for interactions between nodes. This raises the question of how to deal with interference between messages. We show that interference can disturb the synchronization process and propose low-complex, randomization-based techniques to address this issue. First, we demonstrate that randomly switching between two transmit power levels (without increasing the average power) can expedite synchronization. The high-power transmissions temporarily boost network connectivity with negligible impact on the average interference. Second, we reduce interference by blindly distributing the messages over the entire oscillator cycle. Instead of using a fixed oscillator phase at which the pulses are sent, each node chooses its own, randomly selected phase to send a synchronization message. This node-specific “fire phase” is contained in the message to permit others to compute the timing. Third, we suggest that such interference management can also be beneficial for other synchronization techniques and validate this claim using Glossy as an example. Our insights may contribute to feasible solutions for self-organized wireless synchronization. Further work is needed to gain a comprehensive understanding of the effects of randomization and to develop algorithms for the adaptability of local parameters.
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