Internet of Things (IoT) paradigm links physical objects in the real world to cyber world and enables the creation of smart environments and applications. A physical object is the fundamental building block of the IoT, known as a Smart Device , that can monitor the environment. These devices can communicate with each other and have data processing abilities. When deployed, smart devices collect real-time data and publish the gathered data on the Web. The functionality of smart devices can be abstracted as a service and an IoT application can be built by combining the smart devices with these services that help to address challenges of day-to-day activities. The IoT comprises billions of these intelligent communicating devices that generate enormous amount of data, and hence performing analysis on this data is a significant task. Using search techniques, the size and extent of data can be reduced and limited, so that an application can choose just the most important and valuable data items as per its necessities. It is, however, a tedious task to effectively seek and select a proper device and/or its data among a large number of available devices for a specific application. Search techniques are fundamental to IoT and poses various challenges like a large number of devices, dynamic availability, restrictions on resource utilization, real time data in various types and formats, past and historical monitoring. In the recent past, various methods and techniques have been developed by the research community to address these issues. In this paper, we present a review of the state-of-the-art search methods for the IoT, classifying them according to their design principle and search approaches as: IoT data and IoT object-based techniques. Under each classification, we describe the method adopted, their advantages and disadvantages. Finally, we identify and discuss key challenges and future research directions that will allow the next generation search techniques to recognize and respond to user queries and satisfy the information needs of users.

Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions

Comprehensive and systematic review of the service composition mechanisms in the cloud environments

Cloud computing and Internet of Things (IoT) are computing technologies that provide services to consumers and businesses, allowing organizations to become more agile and flexible. Therefore, ensuring quality of service (QoS) through service-level agreements (SLAs) for such cloud-based services is crucial for both the service providers and service consumers. As SLAs are critical for cloud deployments and wider adoption of cloud services, the management of SLAs in cloud and IoT has thus become an important and essential aspect. This paper investigates the existing research on the management of SLAs in IoT applications that are based on cloud services. For this purpose, a systematic mapping study (a well-defined method) is conducted to identify the published research results that are relevant to SLAs. This paper identifies 328 primary studies and categorizes them into seven main technical classifications: SLA management, SLA definition, SLA modeling, SLA negotiation, SLA monitoring, SLA violation and trustworthiness, and SLA evolution. This paper also summarizes the research types, research contributions, and demographic information in these studies. The evaluation of the results shows that most of the approaches for managing SLAs are applied in academic or controlled experiments with limited industrial settings rather than in real industrial environments. Many studies focus on proposal models and methods to manage SLAs, and there is a lack of focus on the evolution perspective and a lack of adequate tool support to facilitate practitioners in their SLA management activities. Moreover, the scarce number of studies focusing on concrete metrics for qualitative or quantitative assessment of QoS in SLAs urges the need for in-depth research on metrics definition and measurements for SLAs.

Management of Service Level Agreements for Cloud Services in IoT: A Systematic Mapping Study

Survey of agent-based cloud computing applications

An efficient social-like semantic-aware service discovery mechanism for large-scale Internet of Things

The spectrum of cloud service providers has become large these days with increasing popularity of cloud computing as a service. These services appear to be similar in their approaches excepting that they vary in their key attributes such as price, computational power, and storage policy. As of now, there are no standardized specifications defined for a cloud service provider. Different cloud service providers use different vocabulary to specify similar operations. A number of quality of service (QoS) parameters may be associated with a service, and thus, identification of the appropriate parameter and its management is the vital issue that needs to be addressed by the service provider. In this context, the search engines such as Google, MSN, etc., do not represent the most effective and most efficient tools in order for determining the appropriate cloud service that could optimally meet the service requirements of a customer. It remains as a challenging issue for a customer requesting for a cloud service. Thus, there arises the necessity of a reasoning mechanism for service discovery that is capable of resolving the similarities among variety of services. In this paper, we address a semantic-based service description and discovery framework using multi-agent approach, where the cloud service descriptions that are automated based on shared ontology, contribute to optimal discovery of appropriate services as requested by consumers.

A Multi-agent-Based Framework for Cloud Service Description and Discovery Using Ontology

Heart Disease, one of the deadliest human diseases worldwide, should be properly diagnosed in time and treatments should be carried out accordingly. To predict Heart Diseases, decision making systems based on classification techniques have been widely proposed in various studies. In this paper, an Integrated Decision Making System (IDMS) has been introduced for prediction of heart disease. In addition, it uses Principal Component Analysis (PCA) for dimensionality reduction, Agglomerative hierarchical clustering technique for clustering and Random Forest (RF) for classification purpose. Then, the results are compared with other six conventional classification techniques. Some experiments are performed using Cleveland Heart Disease Dataset (CHDD) sourced from UCI-ML repository and Python language concluding that the proposed system provides better results comparing with other conventional methods. The proposed integrated decision making system will help out the doctors to diagnose the heart patients professionally and it may be useful for further investigation and predictions using different datasets and resulting valuable knowledge on Heart Disease.

IDMS: An Integrated Decision Making System for Heart Disease Prediction

As cloud computing is getting matured day by day, there has been overwhelming interest among the users to avail a plethora of cloud services. Often, these services appear identical in terms of their functionality though they differ in pricing models, computational power, storage policies and Quality-of-Service parameters making the process of service discovery and selection an intricate task. In the absence of any standard specifications, cloud service providers continue to use their own vocabulary and this further complicates the selection process. Even popular search engines like Google and MSN are not efficient enough to properly identify the most appropriate cloud service that can meet customer requirements. Thus, in the presence of multiple selection parameters and constraints, selecting a required cloud service is a daunting task. In order to address this issue, we work toward developing a reasoning mechanism to optimally resolve the similarities across cloud services by using cloud ontology. A multi-agent-based framework has been proposed for effective cloud service discovery and selection with the help of a standardized service registry and by employing semantically guided searching process.

A multi-agent-based framework for cloud service discovery and selection using ontology

In today’s Internet world, end users need everything as a service (EaaS) being available to them throughout the world. Keeping users in the centre, developers are emplaning cloud computing environment which can satisfy needs of users virtually. Cloud computing is a collection of Web-accessible resources (i.e. Web services) that should be dynamically composed between service providers and brokers and virtualized based on consumer’s needs on an on-demand basis. Mapping of the users’ requirements should be done in an automated manner. But, distributed and constantly changing cloud computing environments pose new challenges to automated service composition such as: (i) dealing with incomplete information regarding cloud resources (e.g. location and providers), and (ii) dynamics of service providers, which set service fees on a supply-and-demand basis. To address these issues, we have proposed a multi-agent-based approach to compose services in multi-cloud environments for different types of cloud services: one-time virtualized services, persistent virtualized services, vertical services, and horizontal services. Cloud participants and resources are implemented and instantiated by agents. Previously the researchers have proposed self-organizing agents those make use of Service Capability Table and the semi-recursive contract net protocol (SR-CNP) to evolve and adapt cloud service compositions. To the existing work, we have planned to modify some of agents’ behaviours, as a result we can reduce number of message passing by half in order to increase overall performance. Also we are planning a 2-layered (3 levels of multi-agents) self-organizing MAS that will establish a cloud service composition.

Service Composition Using Efficient Multi-agents in Cloud Computing Environment

Diagnosis and Prediction of Diabetes, a general chronic disease as well as a major threat to public health, can lead to improved treatment at its early stage. Classification techniques are widely used for the same. While many of the researchers have developed techniques using Machine Learning (ML) and Data Mining (DM) for the prediction of chronic diseases like diabetes, heart diseases, and cancers etc. considering the existing datasets as well as personally collected datasets, but still more research is continuing in this regard. In this paper, an Integrated Approach for Diabetes Prediction (IADP) has been introduced for diabetes prediction based on Hierarchical Agglomerative Clustering (HAC), Linear Discriminant Analysis (LDA) and Random Forests (RF) classifier. Some experiments are performed using Pima Indian Diabetes Dataset (PIDD) sourced from the UCI-ML repository with Python language concluding that the proposed approach provides better results in comparison with other conventional classification models. The proposed integrated approach will help out doctors to diagnose patients with diabetes professionally. Furthermore, it may be useful for investigations and predictions using different datasets, in different fields also, and resulting in valuable knowledge. 

Manoranjan Parhi

Papers

A Multi-agent-Based Framework for Cloud Service Description and Discovery Using Ontology

IDMS: An Integrated Decision Making System for Heart Disease Prediction

A multi-agent-based framework for cloud service discovery and selection using ontology

Service Composition Using Efficient Multi-agents in Cloud Computing Environment

IADP: An Integrated Approach for Diabetes Prediction Using Classification Techniques