Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

A semantic middleware architecture focused on data and heterogeneity management within the Smart Grid

There is an increasing tendency of turning the current power grid, essentially unaware of variations in electricity demand and scattered energy sources, into something capable of bringing a degree of intelligence by using tools strongly related to information and communication technologies, thus turning into the so-called Smart Grid. In fact, it could be considered that the Smart Grid is an extensive smart system that spreads throughout any area where power is required, providing a significant optimization in energy generation, storage and consumption. However, the information that must be treated to accomplish these tasks is challenging both in terms of complexity (semantic features, distributed systems, suitable hardware) and quantity (consumption data, generation data, forecasting functionalities, service reporting), since the different energy beneficiaries are prone to be heterogeneous, as the nature of their own activities is. This paper presents a proposal on how to deal with these issues by using a semantic middleware architecture that integrates different components focused on specific tasks, and how it is used to handle information at every level and satisfy end user requests

A context-aware encryption protocol suite for edge computing-based IoT devices

Heterogeneous devices are connected with each other through wireless links within a cyber physical system. These devices undergo resource constraints such as battery, bandwidth, memory and computing power. Moreover, the massive interconnections of these devices result in network latency and reduced speed. Edge computing offers a solution to this problem in which devices transmit the preprocessed actionable data in a formal way, resulting in reduced data traffic and improved speed. However, to provide the same level of security to each piece of information is not feasible due to limited resources. In addition, not all the data generated by Internet of things devices require a high level of security. Context-awareness principles can be employed to select an optimal algorithm based on device specifications and required information confidentiality level. For context-awareness, it is essential to consider the dynamic requirements of data confidentiality as well as device available resources. This paper presents a context-aware encryption protocol suite that selects optimal encryption algorithm according to device specifications and the level of data confidentiality. The results presented herein clearly exhibit that the devices were able to save 79% memory consumption, 56% battery consumption and 68% execution time by employing the proposed context-aware encryption protocol suite

Upscaling energy control from building to districts: current limitations and future perspectives

Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond

Semantic Description of IoT Security for Smart Grid

Master's thesis Information- and communication technology IKT590 - University of Agder 2017This research work proposed, developed and evaluated IoT Security ontology for smart home energy management system (SHEMS) in smart grids. The ontology description includes infrastructure, attacks, vulnerabilities and counter measures for the main components of SHEMS such as Smart Meter, Smart Appliance, Home Gateway, and Billing data. The ontology extends the SAREF energy management ontology with security features. We have two main reasons for selecting SAREF ontology to base our work on. First, SAREF is standardized by ETSI. Second, it is specifically designed for energy management and efficiency. We checked the correctness of our ontology by running SWRL rules and SPARQL queries. Our test results showed that our ontology is useful to analyse and infer IoT security for smart home and can be extended to more complex reasoning of IoT security features. Keyword: IoT, Security, Smart Grid, Smart Home, Ontology, Energy Managemen

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach

Ontology-driven development of web services to support district energy applications

Current urban and district energy management systems lack a common semantic referential for effectively interrelating intelligent sensing, data models and energy models with visualization, analysis and decision support tools. This paper describes the structure, as well as the rationale that led to this structure, of an ontology that captures the real-world concepts of a district energy system, such as a district heating and cooling system. This ontology (called eedistrict ontology) is intended to support knowledge provision that can play the role of an intermediate layer between high-level energy management software applications and local monitoring and control software components. In order to achieve that goal, the authors propose to encapsulate queries to the ontology in a scalable web service, which will facilitate the development of interfaces for third-party applications. Considering the size of the ee-district ontology once populated with data from a specific district case study, this could prove to be a repetitive and time-consuming task for the software developer. This paper therefore assesses the feasibility of ontology-driven automation of web service development that is to be a core element in the deployment of heterogeneous district-wide energy management software

Context-awareness and the smart grid: Requirements and challenges

New intelligent power grids (smart grids) will be an essential way of improving efficiency in power supply and power consumption, facilitating the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The delivery of efficiencies and advanced services in a smart grid will require both a comprehensive overlay communications network and flexible software platforms that can process data from a variety of sources, especially electronic sensor networks. Parallel developments in autonomic systems, pervasive computing and context-awareness (relating in particular to data fusion, context modelling, and semantic data) could provide key elements in the development of scalable smart grid data management systems and applications that utilise a multi-technology communications network. This paper describes: (1) the communications and data management requirements of the emerging smart grid, (2) state-of-the-art techniques and systems for context-awareness and (3) a future direction towards devising a context-aware middleware platform for the smart grid, as well as associated requirements and challenges