Maritime data transmission with unmanned aerial vehicles (UAVs) in maritime Internet of Things (MIoT) systems has received increasing attention due to its flexibility and low cost. To further improve the efficiency of maritime data transmission between the UAVs and the maritime buoys, optical communication is considered as a promising technique because of its low latency and high bandwidth. However, optical communication encounters the challenge of beam pointing alignment, particularly in maritime data transmission involving wave disturbance and drift of buoy, which deteriorates and even interrupts the line-of-sight (LOS) optical transmission. To tackle the challenge, this paper proposes DERLOC, a reliable data transmission solution based on deep reinforcement learning (DRL). We first provide the optimization analysis of reliable data transmission and formulate the data transmission procedure as a Markov decision process (MDP) aiming at maximizing the received signal intensity. Afterwards, we propose a beam pointing adjustment algorithm based on the soft actor-critic (SAC) approach to alleviate the performance deterioration caused by waves. Then, we analyze the drift characteristic of a buoy and develop a method which enables UAV to predict the position and determine an optimal movement control strategy for ensuring the effectiveness of beam pointing and maintaining stable LOS communication. Through extensive simulations and real-time data validation, the results demonstrate that DERLOC is effective and enables a reliable data transmission via optical links.

Abstract In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an individual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual. In this paper, we design a Treatment effect pattern (TEP) to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the Conditional Average Causal Effect (CATE) using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting individual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets.

With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships. Most existing methods, however, assume a known cause (e.g. a new drug) and focus on identifying from data the contexts of heterogeneous effects of the cause (e.g. patient groups with different responses to the new drug). There is no approach to efficiently detecting directly from observational data context specific causal relationships, i.e. discovering the causes and their contexts simultaneously. In this paper, by taking the advantages of highly efficient decision tree induction and the well established causal inference framework, we propose the Tree based Context Causal rule discovery (TCC) method, for efficient exploration of context specific causal relationships from data. Experiments with both synthetic and real world data sets show that TCC can effectively discover context specific causal rules from the data.

With more and more decisions being made by learnt algorithms from data, algorithmic discriminations have become a risk for civil rights. The detection of discrimination is a process of counterfactual reasoning. This paper proposes a general detection framework by combining a data mining method with a well established counterfactual reasoning framework, potential outcome model. The potential outcome model supports operational definitions of global and local discriminations and discriminations by combined factors, while a data mining method makes the detection efficient. The proposed method, instantiated by association rule mining with potential outcome model based causal effect estimation, is evaluated with four real world data sets and is compared with a Bayesian network (BN) based detection method. It is able to detect not only global discriminations that are detected by the BN based method, but also local and combined discriminations that the BN based method cannot find. The proposed method is efficient, and scales well with the data set size and the number of attributes.

Uncovering causal relationships in data is a major objective of data analytics. Currently, there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree (CDT) where nodes have causal interpretations. Our method follows a well-established causal inference framework and makes use of a classic statistical test to establish the causal relationship between a predictor variable and the outcome variable. At the same time, by taking the advantages of normal decision trees, a CDT provides a compact graphical representation of the causal relationships, and the construction of a CDT is fast as a result of the divide and conquer strategy employed, making CDTs practical for representing and finding causal signals in large data sets. Experiment results demonstrate that CDTs can identify meaningful causal relationships and the CDT algorithm is scalable.

Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real-world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables—a feature that other causal discovery methods do not possess.

Device-to-Device communication has been regarded as a promising technology to improve the cellular system efficiency, which allows mobile devices communicate directly with each other on the licensed frequency resources under the control of a base station (or eNB). However, the benefits of D2D using license exempt bands, such as ISM bands, have not been sufficiently taken into account yet. The main challenges arise from the coexistence of D2D and WLAN in the same frequency band and geographical area. In this paper, we propose a group-wise channel sensing and resource pre-allocation scheme for D2D user fairly contending and utilizing the ISM band resource with its WLAN rivals, where D2D pairs with different QoS or bandwidth requirements are grouped and pre-scheduled to approximately fill in an overall flexible resource block and then a representative D2D pair is appointed in each group for contending resource to avoid intra-group collisions. By using the proposed scheme, the resource on ISM band can be used more efficiently so that D2D applications can be applied to a much wider scope.

For guaranteeing quasi-error free delivery of realtime multicast services over wireless networks, an efficient greedy algorithm is proposed for optimizing the parameters of a HARQ scheme with rateless codes under strict delay and bandwidth constraints. We take systematic Raptor codes as an example for analyzing the optimum performance of the HARQ scheme. The results show that the optimum performance can be obtained by combining the optimum HARQ and FEC. Additionally, the simulation results show that the proposed optimization framework can be used for evaluating the tight lower bound performance of the HARQ, which indicates that it is suitable for designing optimum HARQ scheme with guaranteeing the target quasi-error free delivery requirements very well.