While the Internet of Things (IoT) is growing at a staggering pace, the demand for context-driven intelligence will be a fundamental factor for IoT sustainability, growth, interoperability and acceptance. In this paper, we propose an operational platform called Context-as-a-Service (CoaaS) which enables smart things and IoT silos to discover, validate and share relevant and dependable context. The CoaaS platform consists of context query engine, context reasoning engine, and context storage management system for scalable and performance-oriented context management. We demonstrate features and functionality of CoaaS with a smart city use case “Smart parking and charging for electrical vehicles” partnering with a large vehicle manufacturing company.

As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between ‘things’, where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context; and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.

The ubiquity of mobile devices coupled with the advances in Internet of Things (IoT) technologies has led to the development of large-scale applications that can collect information about people and their environments in real-time. Such applications are referred to as Mobile Crowdsensing (MCS). In MCS, tasks are allocated to participants (mobile devices) by a remote server according to the application requirements. The key challenge is reducing the energy consumption of the participating mobile devices. One of the effective approaches to reduce energy consumption of MCS applications is to improve efficiency of task allocation. An efficient task allocation approach can optimize several aspects of MCS applications such as task coverage (minimum number of participants required for a MCS task), data quality, and sensing costs. In this paper, we propose a novel Context-Aware Task Allocation (CATA) approach that aims to allocate sensing tasks to the best participant set while improving energy efficiency in MCS applications. Another important feature of the proposed CATA approach is that it preserves the privacy of participants' by only disclosing the less sensitive data to the server. The proposed approach employs local and global task allocation methods to enable two levels of data sharing and privacy. We describe the series of experiments that were conducted to validate our proposed approach in terms of coverage and efficiency.

As the Internet of Things (IoT) is entering the System-of-Systems phase, the demand for effortless access to contextual information will be a fundamental factor for IoT expansion, sustainability, interoperability and acceptance. A number of context management platforms are competing in the area, while the standardization efforts on the interfaces are still in the early stage. In this paper, we extend our previous work on the Context-as-a-Service platform and its interface - Context Definition and Query Language (CDQL). We propose a generic approach to represent and query situations in IoT environment. Moreover, we show how the temporal relations between situations can be used in situation descriptions and queries to define highlevel situations.

As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.