Cooperative Sequential Hypothesis Testing in Multi-Agent Systems

Since the sequential inference framework determines the number of total samples in real-time based on the history data, it yields quicker decision compared to its fixed-sample-size counterpart, provided the appropriate early termination rule. This advantage is particularly appealing in the system where data is acquired in sequence, and both the decision accuracy and latency are of primary interests. Meanwhile, the Internet of Things (IoT) technology has created all types of connected devices, which can potentially enhance the inference performance by providing information diversity. For instance, smart home network deploys multiple sensors to perform the climate control, security surveillance, and personal assistance. Therefore, it has become highly desirable to pursue the solutions that can efficiently integrate the classic sequential inference methodologies into the networked multi-agent systems. In brief, this thesis investigates the sequential hypothesis testing problem in multi-agent networks, aiming to overcome the constraints of communication bandwidth, energy capacity, and network topology so that the networked system can perform sequential test cooperatively to its full potential. The multi-agent networks are generally categorized into two main types. The first one features a hierarchical structure, where the agents transmit messages based on their observations to a fusion center that performs the data fusion and sequential inference on behalf of the network. One such example is the network formed by wearable devices connected with a smartphone. The central challenges in the hierarchical network arise from the instantaneous transmission of the distributed data to the fusion center, which is constrained by the battery capacity and the communication bandwidth in practice. Therefore, the first part of this thesis is dedicated to address these two constraints for the hierarchical network. In specific, aiming to preserve the agent energy, Chapter 2 devises the optimal sequential test that selects the "most informative" agent online at each sampling step while leaving others in idle status. To overcome the communication bottleneck, Chapter 3 proposes a scheme that allows distributed agents to send only one-bit messages asynchronously to the fusion center without compromising the performance. In contrast, the second type of networks does not assume the presence of a fusion center, and each agent performs the sequential test based on its own samples together with the messages shared by its neighbours. The communication links can be represented by an undirected graph. A variety of applications conform to such a distributed structure, for instance, the social networks that connect individuals through online friendship and the vehicular network formed by connected cars. However, the distributed network is prone to sub-optimal performance since each agent can only access the information from its local neighborhood. Hence the second part of this thesis mainly focuses on optimizing the distributed performance through local message exchanges. In Chapter 4, we put forward a distributed sequential test based on consensus algorithm, where agents exchange and aggregate real-valued local statistics with neighbours at every sampling step. In order to further lower the communication overhead, Chapter 5 develops a distributed sequential test that only requires the exchange of quantized messages (i.e., integers) between agents. The cluster-based network, which is a hybrid of the hierarchical and distributed networks, is also investigated in Chapter 5.