In-situ sensing devices need to be deployed in remote environments for long periods of time; minimizing their power consumption is vital for maximising both their operational lifetime and coverage. We introduce Terracorder -- a versatile multi-sensor device -- and showcase its exceptionally low power consumption using an on-device reinforcement learning scheduler. We prototype a unique device setup for biodiversity monitoring and compare its battery life using our scheduler against a number of fixed schedules; the scheduler captures more than 80% of events at less than 50% of the number of activations of the best-performing fixed schedule. We then explore how a collaborative scheduler can maximise the useful operation of a network of devices, improving overall network power consumption and robustness.

News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability.

Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video streams are complicated due to their unstructured data model and limit CEP systems to perform matching over them. This work introduces a graph-based structure for continuous evolving video streams, which enables the CEP system to query complex video event patterns. We propose the Video Event Knowledge Graph (VEKG), a graph driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. A CEP-based state optimization - VEKG-Time Aggregated Graph (VEKG-TAG) is proposed over VEKG representation for faster event detection. VEKG-TAG is a spatiotemporal graph aggregation method that provides a summarized view of the VEKG graph over a given time length. We defined a set of nine event pattern rules for two domains (Activity Recognition and Traffic Management), which act as a query and applied over VEKG graphs to discover complex event patterns. To show the efficacy of our approach, we performed extensive experiments over 801 video clips across 10 datasets. The proposed VEKG approach was compared with other state-of-the-art methods and was able to detect complex event patterns over videos with F-Score ranging from 0.44 to 0.90. In the given experiments, the optimized VEKG-TAG was able to reduce 99% and 93% of VEKG nodes and edges, respectively, with 5.19X faster search time, achieving sub-second median latency of 4-20 milliseconds.

With the emergence of IoT, there is a rising interest in applying Internet of Things (IoT) technology in the smart homes for making occupants' life more convenient. The convenience is underpinned by the principle of the least effort, i.e. the premise that humans would usually want to achieve goals with the least cognitive and physical efforts [2]. IoT refers to the networked interconnection of everyday things, which are augmented with capabilities such as sensing, actuating, and communication [21]. The availability of IoT devices including switch sensors, infrared motion sensors, pressure sensor, wearable sensors, accelerators, temperature, humidity, and light sensors have the potential to realize the convenience. It is a challenge that IoT devices are highly diverse in supporting infrastructure such as different programming language and communication protocols [5].

Social media analytics has been attracting considerable attention in both research and industry due to the increasing popularity of social media usage. As a subset, social media monitoring describes the process of continuous monitoring of a subject matter in social media. From our point of view, the key requirements for such systems are i) high throughput and real-time processing of incoming data, ii) a user-friendly way to define complex situations of interests that make use of formalized background knowledge and iii) capabilities to perform actions based on gained insights instead of a pure monitoring system. In this paper, we propose a system for (pro) active, real-time social media monitoring. Firstly, we describe the conceptual architecture of our system and necessary pre-processing steps. Secondly, we introduce our concept of semantic requests that is capable to extend event pattern definitions with background knowledge. Finally, we show the usefulness of this system in two different domains: Real-time political opinion tracking and proactive establishment of relationships with consumers in order to perform a new form of real-time marketing. The main advantage of our approach is a simplified, expressive way to formulate event patterns in social media applications.