Adaptive and context-aware service discovery for the Internet of Things

The Internet of Things (IoT) vision foresees a future Internet encompassing the realm of smart physical objects, which offer hosted functionality as services. The role of service discovery is crucial when providing application-level, end-to-end integration. In this paper, we propose trendy: a RESTful web services based Service Discovery protocol to tackle the challenges posed by constrained domains while offering the required interoperability. It provides a service selection technique to offer the appropriate service to the user application depending on the available context information of user and services. Furthermore, it employs a demand-based adaptive timer and caching mechanism to reduce the communication overhead and to decrease the service invocation delay. trendy’s grouping technique creates location-based teams of nodes to offer service composition. Our simulation results show that the employed techniques reduce the control packet overhead, service invocation delay and energy consumption. In addition, the grouping technique provides the foundation for group-based service mash-ups and localises control traffic to improve scalability

Provision of adaptive and context-aware service discovery for the Internet of Things

The IoT concept has revolutionised the vision of the future Internet with the advent of standards such as 6LoWPAN making it feasible to extend the Internet into previously isolated environments, e.g., WSNs. The abstraction of resources as services, has opened these environments to a new plethora of potential applications. Moreover, the web service paradigm can be used to provide interoperability by offering a standard interface to interact with these services to enable WoT paradigm. However, these networks pose many challenges, in terms of limited resources, that make the adaptability of existing IP-based solutions infeasible. As traditional service discovery and selection solutions demand heavy communication and use bulky formats, which are unsuitable for these resource-constrained devices incorporating sleep cycles to save energy. Even a registry based approach exhibits burdensome traffic in maintaining the availability status of the devices. The feasible solution for service discovery and selection is instrumental to enable the wide application coverage of these networks in the future. This research project proposes, TRENDY, a new compact and adaptive registry-based SDP with context awareness for the IoT, with more emphasis given to constrained networks, e.g., 6LoWPAN It uses CoAP-based light-weight and RESTful web services to provide standard interoperable interfaces, which can be easily translated from HTTP. TRENDY's service selection mechanism collects and intelligently uses the context information to select appropriate services for user applications based on the available context information of users and services. In addition, TRENDY introduces an adaptive timer algorithm to minimise control overhead for status maintenance, which also reduces energy consumption. Its context-aware grouping technique divides the network at the application layer, by creating location-based groups. This grouping of nodes localises the control overhead and provides the base for service composition, localised aggregation and processing of data. Different grouping roles enable the resource-awareness by offering profiles with varied responsibilities, where high capability devices can implement powerful profiles to share the load of other low capability devices. Thus, it allows the productive usage of network resources. Furthermore, this research project proposes APPUB, an adaptive caching technique, that has the following benefits: it allows service hosts to share their load with the resource directory and also decreases the service invocation delay. The performance of TRENDY and its mechanisms is evaluated using an extensive number of experiments performed using emulated Tmote sky nodes in the COOJA environment. The analysis of the results validates the benefit of performance gain for all techniques. The service selection and APPUB mechanisms improve the service invocation delay considerably that, consequently, reduces the traffic in the network. The timer technique consistently achieved the lowest control overhead, which eventually decreased the energy consumption of the nodes to prolong the network lifetime. Moreover, the low traffic in dense networks decreases the service invocations delay, and makes the solution more scalable. The grouping mechanism localises the traffic, which increases the energy efficiency while improving the scalability. In summary, the experiments demonstrate the benefit of using TRENDY and its techniques in terms of increased energy efficiency and network lifetime, reduced control overhead, better scalability and optimised service invocation time

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Goal-based Workflow Adaptation for Role-based Resources in the Internet of Things

In recent years, the Internet of Things (IoT) has increasingly received attention from the Business Process Management (BPM) community. The integration of sensors and actuators into Process-Aware Information Systems (PAIS) enables the collection of real-time data about physical properties and the direct manipulation of real-world objects. In a broader sense, IoT-aware workflows provide means for context-aware workflow execution involving virtual and physical entities. However, IoT-aware workflow management imposes new requirements on workflow modeling and execution that are outside the scope of current modeling languages and workflow management systems. Things in the IoT may vanish, appear or stay unknown during workflow execution, which renders their allocation as workflow resources infeasible at design time. Besides, capabilities of Things are often intended to be available only in a particular real-world context at runtime, e.g., a service robot inside a smart home should only operate at full speed, if there are no residents in direct proximity. Such contextual restrictions for the dynamic exposure of resource capabilities are not considered by current approaches in IoT resource management that use services for exposing device functionalities. With this work, we aim at providing the modeling and runtime support for defining such restrictions on workflow resources at design time and enabling the dynamic and context-sensitive runtime allocation of Things as workflow resources. To achieve this goal, we propose contributions to the fields of resource management, i.e., resource perspective, and workflow management in the Internet of Things (IoT), divided into the user perspective representing the workflow modeling phase and the workflow perspective representing the runtime resource allocation phase. In the resource perspective, we propose an ontology for the modeling of Things, Roles, capabilities, physical entities, and their context-sensitive interrelations. The concept of Role is used to define non-exclusive subsets of capabilities of Things. A Thing can play a certain Role only under certain contextual restrictions defined by Semantic Web Rule Language (SWRL) rules. At runtime, the existing relations between the individuals of the ontology represent the current state of interactions between the physical and the cyber world. Through the dynamic activation and deactivation of Roles at runtime, the behavior of a Thing can be adapted to the current physical context. In the user perspective, we allow workflow modelers to define the goal of a workflow activity either by using semantic queries or by specifying high-level goals from a Tropos goal model. The goal-based modeling of workflow activities provides the most flexibility regarding the resource allocation as several leaf goals may fulfill the user specified activity goal. Furthermore, the goal model can include additional Quality of Service (QoS) parameters and the positive or negative contribution of goals towards these parameters. The workflow perspective includes the Semantic Access Layer (SAL) middleware to enable the transformation of activity goals into semantic queries as well as their execution on the ontology for role-based Things. The SAL enables the discovery of fitting Things, their allocation as workflow resources, the invocation of referenced IoT services, and the continuous monitoring of the allocated Things as part of the ontology. We show the feasibility and added value of this work in relation to related approaches by evaluation within several application scenarios in a smart home setting. We compare the fulfillment of quantified criteria for IoT-aware workflow management based on requirements extracted from related research. The evaluation shows, that our approach enables an increase in the context-aware modeling of Things as workflow resources, in the query support for workflow resource allocation, and in the modeling support of activities using Things as workflow resources.:1 Introduction 15 1.1 Background 17 1.2 Motivation 17 1.3 Aim and Objective 19 1.3.1 Research Questions and Scope 19 1.3.2 Research Goals 20 1.4 Contribution 20 1.5 Outline 21 2 Background for Workflows in the IoT 23 2.1 Resource Perspective 24 2.1.1 Internet of Things 24 2.1.2 Context and Role Modeling 27 2.2 User Perspective 37 2.2.1 Goal Modeling 38 2.2.2 Tropos Goal Modeling Language 38 2.3 Workflow Perspective 39 2.3.1 Workflow Concepts 39 2.3.2 Workflow Modeling 40 2.3.3 Internet of Things-aware Workflow Management 43 2.4 Summary 44 3 Requirements Analysis and Approach 45 3.1 Requirements 45 3.1.1 IoT Resource Perspective 46 3.1.2 Workflow Resource Perspective 50 3.1.3 Relation to Research Questions 51 3.2 State of the Art Analysis 53 3.2.1 Fulfillment Criteria 54 3.2.2 IoT-aware workflow management 56 3.3 Discussion 65 3.4 Approach 70 3.4.1 Contribution to IoT-aware workflow management 71 3.5 Summary 73 4 Concept for Adaptive Workflow Activities in the IoT 75 4.1 Resource Perspective 75 4.1.1 Role-based Things 75 4.1.2 Semantic Modeling Concepts 79 4.1.3 SWRL Modeling Concepts 81 4.2 User Perspective 81 4.2.1 Semantic Queries in Workflow Activites 81 4.2.2 Goals for Workflow Activites 81 4.2.3 Mapping from Goals to Semantic Queries 82 4.3 Workflow Perspective 83 4.3.1 Workflow metamodel Extensions 83 4.3.2 Middleware for Dynamic Resource Discovery and Allocation 85 4.4 Summary 86 5 Modeling Adaptive Workflow Activities in the IoT 87 5.1 Resource Perspective 87 5.1.1 Role-based Modeling of Context-sensitive Things 87 5.1.2 Ontology Classes 90 5.1.3 Ontology Object properties 93 5.1.4 Ontology Data properties 99 5.1.5 DL-safe SWRL Rules 100 5.2 Discussion of Role Modeling Features 101 5.3 Example Application Scenario Modeling 102 5.3.1 Resource Perspective 102 5.3.2 User Perspective 105 5.3.3 Workflow Perspective 109 5.4 Summary 113 6 Architecture for Adaptive Workflow Activities in the IoT 115 6.1 Overview of the System Architecture 115 6.2 Specification of System Components 117 6.2.1 Resource Perspective 118 6.2.2 User Perspective 118 6.2.3 Workflow Perspective 118 6.3 Summary 123 7 Implementation of Adaptive Workflow Activities in the IoT 125 7.1 Resource Perspective 125 7.2 Workflow Perspective 125 7.2.1 PROtEUS 125 7.2.2 Semantic Access Layer 127 7.3 User Perspective 128 7.4 Summary 128 8 Evaluation 129 8.1 Goal and Evaluation Approach 129 8.1.1 Definition of Test Cases 130 8.2 Scenario Evaluation 134 8.2.1 Ambient Assisted Living Setting 135 8.2.2 Resource Perspective 135 8.2.3 User Perspective 137 8.2.4 Workflow Perspective 138 8.2.5 Execution of Test Cases 139 8.2.6 Discussion of Results 146 8.3 Performance Evaluation 148 8.3.1 Experimental Setup 148 8.3.2 Discussion of Results 151 8.4 Summary 152 9 Discussion 153 9.1 Comparison of Solution to Research Questions 153 9.2 Extendability of the Solutions 155 9.3 Limitations 156 10 Summary and Future Work 157 10.1 Summary of the Thesis 157 10.2 Future Work 159 Appendix 161 Example Semantic Context Model for IoT-Things 171 T-Box of Ontology for Role-based Things in the IoT 178 A-Box for Example Scenario Model 201 A-Box for Extended Example Scenario Model 21

A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things

The Internet of Things (IoT) is envisioned as a global network of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. However, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT systems and applications, it is key to orchestrate heterogeneous 'things' in a seamless, adaptive and dynamic manner, such that the system can exhibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users' requirements. To this end, contextualisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user's interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users' intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.Comment: In Proceedings of the Second International Workshop on the Internet of Agents @AAMAS201