Demand-side energy management is becoming increasingly important owing to concerns related to global warming and energy shortages. In particular, as the development of Internet of Things (IoT) enables the precise control of home appliances, the demand for home energy management systems (HEMSs) is expected to increase. This article proposes an artificial intelligence-based HEMS (AI-HEMS) that provides both energy efficiency and resident satisfaction. To this end, we implemented three prediction mechanisms: 1) derivation of comfort temperature; 2) device-free sleep prediction; and 3) occupancy-probability-based outing prediction. Based on these mechanisms, we present four intelligent heater control strategies: 1) outing; 2) occupancy; 3) comfort; and 4) sleep-based control. For evaluation, an experimental testbed was constructed and measurements were taken. To increase the reliability of the evaluation by excluding natural energy fluctuation factors, a treatment group (100 households) and a control group (2281 households) were recruited to measure the energy savings of the treatment group compared to the control group. In a 48-day evaluation, AI-HEMS was found to provide an energy-saving rate of approximately 14% and resident satisfaction of approximately 91%. These results imply that the proposed system can save energy while maintaining a high level of satisfaction.

Both entity typing and relation extraction from text corpora are widely used to identify the semantic types of an entity and a relation in a knowledge graph (KG). Most existing approaches rely on a pre-defined set of entity types and relation types in a KG. They thus cannot map entity mentions (relation mentions) to unseen entity types (relation types). To fundamentally overcome the limitations, we should add new semantic types of entities and relations to a KG schema. However, schema expansion traditionally requires manual conceptualization through a user’s observation on the text corpus while assuming the existence of suitable target KG schemas. In this work, we propose anActive learning framework forKnowledge graphSchemaExpansion (AKSE), which can generate a new semantic type for KG schemas, without depending on a set of target schemas and human users’ observation. Specifically, a granularity based active learning algorithm determines whether a KG schema requires new semantic types or not. We also introduce a KG schema attention-based neural method which assigns semantic types to the entities and relationships extracted. To the best of our knowledge, our work is the first study to expand a KG schema with active learning.

This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). Unlike previous approaches, the proposed method focuses on an entity link, which consists of dependency edges between entities, and discourse relations between sentences. Specifically, the proposed model consists of two main sub-models. The first one encodes sentences with a higher weight on the entity link while considering the other edges with an attention mechanism. To consider various latent discourse relations between sentences, the second sub-model encodes discourse relations between adjacent sentences considering the contents of each sentence. Experiment results on the cross-sentence relation extraction dataset, PubMed, and the document-level relation extraction dataset, DocRED, show that the proposed model outperforms state-of-the-art methods of extracting relations across sentences. Furthermore, ablation study proves that both the two main sub-models have noticeable effect on the relation extraction task.

A large number of spatial knowledge graphs (SKGs) are available from spatially enriched knowledge bases, e.g., DBpedia and YAGO2. This provides a great chance to understand valuable information about the regions surrounding us. However, it is hard to comprehend SKGs due to the explosively growing volume and the complication of the graph structures. Thus we study the problem of similar region search (SRS), which is an easy-to-use but effective way to explore spatial data. The effectiveness of SRS highly depends on how to measure the region similarity. However, existing approaches cannot make use of the rich information contained in SKGs thus may lead to incorrect results. In this paper, we propose a spatial knowledge representation learning method for region similarity, namely SKRL4RS. SKRL4RS firstly encodes the spatial entities of an SKG into a vector space to make it easier to extract useful features. Then regions are represented by 3-D tensors using the spatial entity embeddings together with geographical information. Finally, region tensors are fed into the conventional triplet network to learn the feature vectors of regions. The region similarity measure learned by SKRL4RS can capture the hierarchical types, semantic relatedness, and relative locations of spatial entities inside a region. Experimental results on two real-world datasets show that our SKRL4RS outperforms the state-of-the-art by a significant margin in terms of the accuracy of measuring region similarity.

Improvement in the security and availability is important for the success of the Internet of Things (IoT). Given that recent IoT devices are likely to have multiple functionalities and support third-party applications, this goal becomes challenging to achieve. Through an in-depth investigation of existing IoT frameworks, we focused on two inherent security flaws in their design caused by their device-centric approaches: (1) coarse-grained access control and (2) lack of resource isolation. Because of the coarse-grained access control, IoT devices suffer from over-privileged applications. Furthermore, the lack of resource isolation allows the possibility of Denial-of-Service attacks.

Network protocols can be designed to enable a reduction in energy use of data servers. We architect a new HTTP timed redirection response for a GET request to be redirected to another server with a given delay. This redirection response can be used in a hybrid web server to coalesce GET requests and allow a server to periodically sleep. In such a hybrid web server, a small low-power ARM-based Assistant receives all incoming HTTP requests and redirects them to a Pentium-class Master server with a delay calculated to allow the Master to sleep in alternating intervals. There is a growing class of applications that are delay tolerant at short time scales for file downloads. Experimental evaluation of timed redirection shows significant savings for the Master server with added delay for some requests which may be acceptable for certain applications.

Web servers in small and medium enterprises (SME) consume a significant amount of energy. We consider how a hybrid SME web server based on two co-located platforms (one high performance and high power and the second low performance and low power) can be architected to appear as a single system image to clients. A prototype based on a Dell AMD Athlon x64-based PC and a Sheeva ARM-based plug computer is described and evaluated using the Apache ab benchmark. Using traffic traces from the KETI corporate web server, we show that for the majority of the time a low-performance platform can very likely meet the performance demands (during which time the high-power platform can sleep). We explore prediction of server load using network traffic analysis as a means to achieve a selection (or switching) policy between the two platforms. Using simple prediction methods, we show that we can achieve between 41% to 67% energy savings with minimal performance impact.

The Internet protocols were designed when there were relatively few devices connected to the Internet and these devices were in use most of the time. Studies show many of these computers especially in home have their power management features disabled in order to maintain their network presence and network connections. Past research proposes to use a low power proxy to “stand in” for a computer, allowing it to go to sleep and thus save power while still maintaining its network presence. This paper describes an experimental stand-by gateway that can be used to develop the requirements for such a proxy. With the stand-by gateway, we propose to develop a language to be used by applications to provide the necessary code to the proxy to maintain network connections and presence for a sleeping computer. And we investigate the possibility of putting various components on home gateway to sleep during periods of low traffic activity. Out results show that sleeping is indeed feasible in the home network