For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high energy consumption, commercialisation of battery-powered wireless acoustic sensor networks has been limited. To increase the network’s lifetime, this paper explores the joint use of decision-level fusion and dynamic sensor activation. Hereby adopting a topology where processing – including feature extraction and classification – is performed on a dynamic set of sensor nodes that communicate classification outputs which are fused centrally. The main contribution of this paper is the comparison of decision-level fusion with different dynamic sensor activation strategies on the use case of automatically classifying domestic activities. Results indicate that using vector quantisation to encode the classification output, computed at each sensor node, can reduce the communication per classification output to 8 bit without loss of significant performance. As the cost for communication is reduced, local processing tends to dominate the overall energy budget. It is indicated that dynamic sensor activation, using a centralised approach, can reduce the average time a sensor node is active up to 20% by leveraging redundant information in the network. In terms of energy consumption, this resulted in an energy reduction of up to 80% as the cost for computation dominates the overall energy budget.

在过去的几十年里，人们对无线传感器网络获取环境信息的兴趣日益浓厚。一种有趣的模态是音频，因为它对于包括语音识别、城市场景分类、城市监控、机器监听和家庭活动分类在内的众多应用具有很高的信息量。然而，由于它们以极高的能耗运行，电池供电的无线声学传感器网络的商业化受到限制。为了增加网络的寿命，本文探讨了决策级融合和动态传感器激活的联合使用。因此采用一种拓扑结构，其中处理（包括特征提取和分类）是在一组动态传感器节点上执行的，这些传感器节点传递集中融合的分类输出。本文的主要贡献是在自动分类家庭活动的用例上比较了决策级融合与不同动态传感器激活策略。结果表明，使用矢量量化对在每个传感器节点计算的分类输出进行编码，可以将每个分类输出的通信减少到 8 位，而不会损失显着的性能。随着通信成本的降低，本地处理往往会在整体能源预算中占主导地位。结果表明，使用集中式方法的动态传感器激活可以通过利用网络中的冗余信息将传感器节点的平均活动时间减少 20%。在能源消耗方面，由于计算成本在整体能源预算中占主导地位，这导致能源减少高达 80%。