Service-oriented Internet Of Things Research Articles

A novel hierarchical edge-based architecture for service oriented IoT

IoT (Internet of Things) is becoming increasingly prevalent in everyday life, with many commercialized IoT platforms available for use. In this paper, we propose a novel hierarchical IoT platform under a single administrating entity to provide scalability to an edge-based service-oriented IoT platform. To enable communication and resource sharing between edges while preserving the privacy benefit of the edge-based platform, a new type of service, called super service is introduced. And the scheduling problem of service requests to devices and a proposed solution are presented. To evaluate the proposed platform, a simulation framework is developed. Through a smart campus test-bed and extensive experiments with the simulation framework, the viability of the proposed platform is demonstrated.

An Integrated Collaborative Filtering Framework With Location-Aware Graph Embedding in Intelligent Internet of Things Systems

Reliable Internet of Things (IoT) service discovery is a significant task in intelligent service-oriented IoT systems. Collaborative filtering (CF) turns out to be an effective solution to IoT service discovery. However, the traditional CF framework is facing the following challenges: inefficiency in learning the high-order interactions between users and IoT services and ineffectively making use of geographical location information. Moreover, deploying a CF framework in real-world distributed IoT systems poses another significant challenge in terms of reliability assurance and privacy protection. For bridging this gap, we propose an innovative integrated collaborative filtering framework (ICF) with incorporating the location-aware quality of IoT services into a heterogeneous graph embedding model. Meanwhile, a Geohash-based privacy-preservation mechanism is introduced to encoding the location information into a short string for protecting the sensitive location information. The proposed ICF framework is an integrated architecture combining advanced graph embedding learning and heterogeneous side information. And a joint objective optimization function is designed in the graph embedding learning to qualify automatic IoT service features. In this way, the hidden user-service interaction information and location-aware quality semantic features can be explored in an effective and reliable way. Furthermore, the learned location-aware quality embedding vector is incorporated to discover the reliable services among all IoT services with the mini-batch online clustering-based CF algorithm. The experimental findings illustrate the significant efficacy and reliability of our proposed ICF framework in large scale IoT service discovery scenarios.

A social qualitative trust framework for Fog computing

The field of Internet of Things (IoT) is evolving at an exponential rate where megabytes of data is being processed each passing minute. Fog computing is an emerging field, regarded as the new way forward to prevent the suspected data outburst of the exhausted IoT devices. Fogging is believed to reduce latency, and enhance efficiency, ease of deployment and flexibility, however, various security and privacy concerns hinder its deployment by major platforms. Among all the security concerns, lack of a proper trust management system is of primary importance. This paper explores the Service-oriented Internet of Things (SIoT) domain to study its similarity with Fogging and creates a trust management scheme (TMS). The paper further explores the necessary trust requirements for Fogging and incorporates them into a two-way trust management scheme based on Bayes model, the model allows both the service requestor and the service provider to validate each other before connecting. The model calculates the value of trust using these metrics and combines them in a unique way with Bayes trust to have an accurate trust value. The proposed scheme is simulated in Netlogo, an agent-based network simulator. The subject scheme is capable of effectively preventing a legitimate node from connecting with a malicious node. The results illustrates high accuracy and faster convergence and also shows resilience against trust-based network attacks. The system is compared against SIoT trust management models due to lack of similar trust management models in Fog Computing.

Decentralized Data Flows for the Functional Scalability of Service-Oriented IoT Systems

Abstract Horizontal and vertical scalability have been widely studied in the context of computational resources. However, with the exponential growth in the number of connected objects, functional scalability (in terms of the size of software systems) is rapidly becoming a central challenge for building efficient service-oriented Internet of Things (IoT) systems that generate huge volumes of data continuously. As systems scale up, a centralized approach for moving data between services becomes infeasible because it leads to a single performance bottleneck. A distributed approach avoids such a bottleneck, but it incurs additional network traffic as data streams pass through multiple mediators. Decentralized data exchange is the only solution for realizing totally efficient IoT systems, since it avoids a single performance bottleneck and dramatically minimizes network traffic. In this paper, we present a functionally scalable approach that separates data and control for the realization of decentralized data flows in service-oriented IoT systems. Our approach is evaluated empirically, and the results show that it scales well with the size of IoT systems by substantially reducing both the number of data flows and network traffic in comparison with distributed data flows.

User Integration in Two IoT Sustainable Services by Evaluation Grid Method

To meet the need for sustainable development, Taiwan has been spreading a network of micro-monitoring stations to measure the environmental quality in rivers and air to protect people from environmental pollution. As a result, more and more information technology companies develop Internet of Things (IoT) services for this job. However, most IoT services are screened out of the market because lacking design thinking. Therefore, including users’ desires in IoT products and services is a critical determinant for their survival in this permanently changing market. Thus, this study proposed a systematic framework to identify the users’ desires from different stakeholders to determine technological development. For this, we use the evaluation grid method (EGM) to explore the users’ desires by a series of in-depth interviews and visualize the user’s response as a hierarchical evaluation map of attraction. After that, an IoT prototype is built and used to capture the insightful feedback of respondents. Meanwhile, we adopt the minimum viable product (MVP) design principles to develop two prototypes that manage a wastewater treatment plant and household environment. Overall, this study proposes an applicable user integration procedure to help IT engineers develop the IoT for sustainable service. This study also confirms that the MVP method can help to accelerate user integration. We propose a service-oriented IoT architecture in technology development and develop a decision-making service of human dispatch in operating environmental facilities and a context-awareness service for environmental control.

Modeling-framework for model-based software engineering of complex Internet of things systems.

Internet of things (IoT) systems are composed of variety of units from different domains. While developing a complete IoT system, different professionals from different domains may have to work in collaboration. In this paper we provide a framework which allows using discrete and continuous time modeling and simulation approaches in combination for IoT systems. The proposed framework demonstrates on how to model Ad-hoc and general IoT systems for software engineering purpose. We demonstrate that model-based software engineering on one hand can provide a common platform to overcome communication gaps among collaborating stakeholders whereas, on the other hand can model and integrate heterogeneous components of IoT systems. While modeling heterogeneous IoT systems, one of the major challenges is to apply continuous and discrete time modeling on intrinsically varying components of the system. Another difficulty may be how to compose these heterogeneous components into one whole system. The proposed framework provides a road-map to model discrete, continuous, Ad-hoc, general systems along with composition mechanism of heterogeneous subsystems. The framework uses a combination of Agent-based modeling, Aspect-oriented modeling, contract-based modeling and services-oriented modeling concepts. We used this framework to model a scenario example of a service-oriented IoT system as proof of concept. We analyzed our framework with existing systems and discussed it in details. Our framework provides a mechanism to model different viewpoints. The framework also enhances the completeness and consistency of the IoT software models.

A Robust Energy Optimization and Data Reduction Scheme for IoT Based Indoor Environments Using Local Processing Framework

The extensive growth in popularity of Internet of Things (IoT) has led to the generation of massive amount of data from several heterogeneous sensory devices. This has also led to the increase in energy consumption by these connected devices. Smart buildings are one such platform which are equipped with several micro-controllers and sensors, generating a huge amount of redundant information at their data acquisition level. As a result, real-time applications may not be efficiently executed due to latency delays at the cloud service end. This requires several devices at cloud service end to execute the massive amount of data generated by these sensors, which does not satisfy green computing criteria. In this context, a novel local processing mechanism (LPM) is proposed, which favors an improved IoT service architecture for smart buildings. From the perspective of green computing, the proposed LPM framework facilitates reduction of manifolds at data acquisition level of sensor nodes. This paper also addresses the concept of optimal use of sensors in a wireless sensor network (WSN) and estimates costs corresponding to non-Poisson and Poisson arrival of data packets at local processor using the well-known queuing model. We also provide an efficient algorithm for smart buildings using our expert Markov switching (EMS) model, which is a well known probabilistic model in the field of artificial intelligence (AI) for subjectively validating real sensory data sets (viz., temperature, pressure, and humidity). Further, it has been analyzed that the proposed EMS algorithm outperforms several other algorithms conventionally used for determining the state of large-scale dynamic sensor networks. The service cost of proposed model has been compared with conventional model under various stress conditions viz., arrival rate, service rate, and number of clusters. It is observed that the proposed model operates well by leveraging green computing criteria. Thus, in the aforementioned context, this paper provides thing-centric, data-centric, and service-oriented IoT architecture.

AI-Empowered Maritime Internet of Things: A Parallel-Network-Driven Approach

As one of the key technologies for realizing a fully digitalized world, the Internet of Things (IoT) requires ubiquitous connections across both land and sea. However, due to lack of infrastructure such as optical fibers and base stations, maritime communications inevitably face a highly complex and heterogeneous environment, which greatly challenges the connection reliability and traffic steering efficiency for future service-oriented maritime IoT. With the recent burgeoning application of artificial intelligence (AI) in many fields, an AI-empowered autonomous network for maritime IoT is envisioned as a promising solution. However, AI typically involves training/learning processes, which require realistic data/environment in order to obtain valuable outcomes. To this end, this article proposes the parallel network, which can be regarded as the "digital twin" of the real network and is responsible for realizing four key functionalities: self-learning and optimizing, state inference and network cognition, event prediction and anomaly detection, and knowledge database and snapshots. We then explain how various AI methods can facilitate the operation of the parallel- network-driven maritime network. A case study is provided to demonstrate the idea. Research directions are also outlined for further studies.

Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing

This paper has proposed an integrated healthcare monitoring solution for the soldiers deployed in adverse environmental conditions, using the internet of things (IoT) with distributed computing. For these soldiers, the health parameters of every individual need to be monitored on a real-time basis and subsequent analysis of the dataset to be made for initiating appropriate medical support with the lowest possible delay. In this paper, a three-layer service-oriented IoT architecture has been proposed where the computational functionalities are distributed among all the layers. The proposed distributed computing mechanism has implemented two levels of filtration of redundant information that belongs to safe soldiers. The first level of filtering is done at the end-node using the Fuzzy classification approach and the second level of filtering is done at the intermediate node using the time-series pattern analysis approach. This layer-wise filtration process results in a reduction in data flooding and computational burden on the cloud due to which system response time improves to suit emergency applications. A prototype has been developed to validate the effectiveness of the proposed solution.

A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.

Service Embedding in IoT Networks

The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency.

QoS provisioning framework for service-oriented internet of things (IoT)

The proliferation of ubiquitous sensing technology is bringing a rising number of the innovative models that have unique characteristics of the utility computing. These models have offered great opportunities to improve IT industries and business processes through the convergence of cloud computing and internet of things (IoT). Although this convergence establishes seamless intelligent interaction among physical and virtual entities, it has difficulty not only to meet the required level of quality of service(QoS) but also to satisfy the user’s complex demands. As a result, the predisposition to create a dynamic service-oriented environment has become a fundamental design issue. The main objective of this study is to introduce a dynamic QoS provisioning framework (QoPF) for service-oriented IoT using backtracking search optimization algorithm (BSOA). The QoPF framework is proposed to maximize the composite service quality in IoT application layer by making a balance between service reliability and acceptable cost of the computational time. The effectiveness of the QoPF framework is evaluated using a number of performance metrics such as throughput, delay time, and jitter. The experimental results demonstrate that worthiness of the QoPF to meet QoS requirements more than other state-of-the-art techniques in the literature review.

Building edge intelligence for online activity recognition in service-oriented IoT systems

This paper presents the edge intelligence support for smart Internet of Things (IoT) using the service-oriented architecture. We propose an edge intelligence framework for building smart IoT applications. The proposed edge intelligence framework pushes the streaming processing capability from cloud core to edge devices, in order to better support timely and reliable streaming data analytics in smart IoT applications. We have designed annotation based programming primitives for developers to build online learning capabilities on edge devices. We have also implemented a user activity recognition engine, and compared its performances between running on either an edge device or cloud servers. Using our edge intelligence framework can improve the real-time and fault-tolerance performance significantly without degrading the activity recognition accuracy in a smart home.

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for Internet of Things (IoT). Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance (FT) in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering FT and communication delay as two conflicting objectives. An adapted nondominated sorting-based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, FT and delay computation, crossover and mutation, and nondominance-based sorting. Analytical and simulation-based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes FT for virtualization in WSNs.

Situation-Aware IoT Service Coordination Using the Event-Driven SOA Paradigm

Internet of Things (IoT) technology demands a complex, lightweight distributed architecture with numerous diverse components, including end devices and applications adapted for specific contexts. This paper proposes a situation-aware IoT services coordination platform based on the event-driven service-oriented architecture (SOA) paradigm. Focus is placed on the design of an event-driven, service-oriented IoT services coordination platform, for which we present a situational event definition language (SEDL), an automaton-based situational event detection algorithm, and a situational event-driven service coordination behavior model, which is based on an extended event-condition-action trigger mechanism. Moreover, we propose a reliable real-time data distribution model to support the effective dispatching sensory data between information providers and consumers, which is based on the grid quorum mechanism to organize those brokers into a grid overlay network to facilitate the asynchronous communication in a large-scale, distributed, and loosely coupled IoT applications environment. We also illustrate the various illustrations for IoT services coordination and alarming disposal process of coal mine safety monitoring and control automation scenarios, and also report the measurement and analysis of the platform’s performance.

SoIoT

An emerging issue in urban computing environments is the seamless selection, composition, and delivery of user-centric services that run over what is known as the Internet of Things (IoT). This challenge is about enabling services actuated by IoT devices to be delivered spontaneously from the perspective of users. To accomplish this goal, we propose the Service-Oriented Internet of Things (SoIoT), a user-centric IoT-based service framework, which integrates services that utilize IoT resources in an urban computing environment. This framework provides a task-oriented computing approach that enables the composition of IoT-based services in a spontaneous manner to accomplish a user task. Tasks can also be recommended to users based on the available IoT resources in an environment and on the contextual knowledge that is represented and managed in social, spatial, and temporal aspects. These tasks are then bound to a set of service instances and performed in a distributed manner. This final composition ensures the Quality of Service (QoS) requirements of the tasks and is assigned to multiple client devices for the efficient utilization of IoT resources. We prove the practicality of our approach by showing a real-case service scenario implemented in our IoT-based test-bed as well as experimental results.

A Distributed Consensus Algorithm for Decision Making in Service-Oriented Internet of Things

In a service-oriented Internet of things (IoT) deployment, it is difficult to make consensus decisions for services at different IoT edge nodes where available information might be insufficient or overloaded. Existing statistical methods attempt to resolve the inconsistency, which requires adequate information to make decisions. Distributed consensus decision making (CDM) methods can provide an efficient and reliable means of synthesizing information by using a wider range of information than existing statistical methods. In this paper, we first discuss service composition for the IoT by minimizing the multi-parameter dependent matching value. Subsequently, a cluster-based distributed algorithm is proposed, whereby consensuses are first calculated locally and subsequently combined in an iterative fashion to reach global consensus. The distributed consensus method improves the robustness and trustiness of the decision process.

QoS-Aware Scheduling of Services-Oriented Internet of Things

The Internet of Things (IoT) contains a large number of different devices and heterogeneous networks, which make it difficult to satisfy different quality of service (QoS) requirements and achieve rapid services composition and deployment. In addition, some services in service-oriented IoT are required to be reconfigurable and composable for QoS-aware services. This paper proposed a three-layer QoS scheduling model for service-oriented IoT. At application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service. At network layer, the model aims at dealing with scheduling of heterogeneous networks environment; at sensing layer, it deals with the information acquisition and resource allocation scheduling for different services. The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs.