Service Composition In Internet Of Things Research Articles

IoT service composition based on improved Shuffled Frog Leaping Algorithm

In the complex Internet of Things (IoT) environment, a plethora of IoT services with akin functions but varying qualities of service exist. To meet diverse customer needs and drive widespread application, service composition optimization becomes crucial. In the current era of rapid development in artificial intelligence, intelligent algorithms play a significant role in optimizing service composition. However, algorithms applied to IoT service composition optimization face common challenges of low search efficiency and insufficient optimization precision, including the Shuffled Frog Leaping Algorithm (SFLA) and Genetic Algorithm (GA). Therefore, this study seeks to enhance the perception of service quality in IoT service composition. It proposes an improved SFLA (ISFLA) based on the original SFLA. The algorithm integrates chaos theory and reverse learning theory for the acquisition of the initial population. It utilizes Euclidean distance to partition the population into groups and employs Gaussian mutation to optimize the optimal individual of each group. Finally, the entire population undergoes evolution through a local update method based on two strategies. Simulated experiments were conducted to search for optimal IoT service composition solutions of different scales. The results indicate that, compared to the SFLA, GA, ISFLA*, IGSFLA and SFLAGA, ISFLA achieves superior fitness values, better composition solutions, and exhibits faster convergence, higher stability, and greater overall operational efficiency.

Optimizing Service Redeployment in Migration-Oriented IoT Networks

The Internet of Things (IoT) paradigm has established an effective platform to promote the collaboration of resource-limited and duty-cycle IoT nodes, in order to support relative complex service requests that can hardly be achieved by any single IoT node. The functionalities of IoT nodes are typically encapsulated into IoT services, and the satisfaction of service requests is implemented as IoT service composition. Generally, IoT nodes work in turn in terms of their pre-specific working cycles, and IoT network topology is constantly varied due to their active/sleeping behaviour switching, causing unscheduled response latency. IoT service composition instantiation should be dynamically adjusted and partially re-deployed on-demand for supporting request processing efficiently. To remedy this issue, this paper proposes a Migration-oriented Service re-Deployment (MSrD) mechanism by migrating certain IoT services from their hosted IoT nodes to neighboring ones, in order to support functionally continuous availability.We formulate this problem as a game-theoretic approach, which is reduced to a potential game, where a Nash equilibrium solution is searched for optimizing this service re-deployment game. Extensive experiments are conducted, and numerical results show that our MSrD mechanism is promising, compared with the state-of-art techniques, in achieving service re-deployment optimization with efficient energy consumption and timely response latency simultaneously.

Formal modelling and verification of scalable service composition in IoT environment

A system based on the internet of things (IoT) consists of services deployed across several devices that collaborate to fulfil IoT system goals. The growth in the number of IoT services that has occurred concurrently with the growth in the number of IoT devices is posing a significant difficulty for the process of service composition. In order to satisfy increasing demand and rapid expansion while keeping a certain level of quality of service, IoT systems need a scalable IoT service composition. However, building the correct scalable service composition is not guaranteed in IoT systems. This paper proposes formal modeling and verification of the scalability in IoT service composition at design time based on Event-B formal method. To fulfil the requirements of IoT service composition, the proposed model addresses more key qualities, mainly availability and interoperability. Further, by relying on the refinement technique, we create our model sequentially from the requirements analysis level to the target level. Finally, we validate and verify the correctness of the proposed formal model using of the Rodin platform and several proof obligations. Our verified model of the scalable IoT service composition has met its 44 proof obligations, and the Rodin prover was responsible for automatically addressing all of these proof obligations.

Formal Analysis of Trust and Reputation for Service Composition in IoT

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP's trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers' (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.

An efficient clustering mechanism towards large scale service composition in IoT

Internet of things (IoT) applications hinder heterogeneity at different levels, such as device level, usage level, and communication level. In this context, service-oriented architecture (SOA) facilitates limited supports for handling heterogeneity in IoT-based applications. This article proposes a clustered hypergraph colouring (CHC) approach for designing large-scale SOA (LSS). Two noble approaches (service clustering and composition) are discussed in LSS for satisfying the service consumers (SCs) requirements. A service clustering approach is presented for the clustering of homogeneous services. This mechanism enables to reduce the search time for the service composition mechanism. A service composition approach is described with some parameters. Further, the proposed approach is validated through an experimental setup by considering the factors such as the number of services, execution time, and memory size. The execution time of LSS is minimised as compared to normal or medium-scale SOA (NSS) due to the service clustering mechanism. The novelty of this work is to minimise the execution time in the large service domain during service composition.

An approach based on genetic algorithms and neural networks for QoS-aware IoT services composition

In the Internet of Things (IoT) environment, the services provided by the connected objects are published as services through the web. This allows to machines to interact between them and, makes the IoT services composition possible. However, the vast proliferation of smart object generates services with the same functionalities but different in terms of quality of services (QoS) proprieties. This makes the satisfaction of the user requirements often complex and a NP-hard problem. Indeed, respecting the QoS constraints (user preferences in terms of QoS) is a challenge, due to the high number of candidate services for the composition. This challenge consists of selecting the most appropriate services so that the composite service must meet both the functional and the non-functional requirements of the user. To deal with this challenge, we propose an approach based on Genetic Algorithms (GA) and Neural Networks (NN) for QoS-aware IoT Services Composition in the context of large-scale environments. The combination between GA and NN allows finding the quasi-optimal IoT service composition. This latter is based on global QoS optimization. To reach this objective, the QoS intervals are decomposed into M QoS-levels to engage them into the theoretical composition. Then, the proposed first GA is used to obtain the ideal theoretical composition with an overall QoS optimization. Afterward, the proposed NN is used to eliminate the inappropriate concrete IoT services, and retain only the services having the same categories of the atomic theoretical services composing the ideal theoretical composition. This allows us to optimize the search space as well as the execution time. Finally, we apply the second GA on the concrete services of the retained categories, in order to obtain the IoT service concrete composition with an overall QoS optimization. The simulation results show that the proposed approach has the best composition time, the best Hypervolume indicator and the best compositional optimality compared to SC-FLA, Improved GA and MGA approaches. On another side, it has almost the same performances compared to TS-QCA and, it finds the near-optimal composition in a very short time compared to PSA, which is an optimal approach. Thus, the obtained results show the effectiveness of our approach.

Multi-objective metaheuristic search algorithms for service composition in IoT

The basic idea behind the Internet of Things (IoT) is to connect every physical Thing to the Internet. IoT adds ability in Things to sense and communicate with other Things. One of the main concerns in IoT is forming an optimal service composition to fulfil the user requirements while balancing the quality of service (QoS) parameters. So, in this paper, the service composition problem in IoT has been addressed using multi-objective optimisation. An optimal solution to this problem has been provided through proposed novel hybrid and proposed algorithm Multi-Objective Hybrid Hyper-Heuristic Flower Pollination Algorithm (MOHHFPA). The superiority of this algorithm is proved by empirically and statistically comparing it with existing multi-objective algorithms, namely the Non-dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Flower Pollination Algorithm (MOFPA), and the Multi-Objective Hyper-Heuristic Search Algorithm (MOHypEA). The proposed algorithm is empirically evaluated using a real-world case study.

Automatic incremental recomposition algorithm for QoS-aware internet of things service composition

In the near future, the internet-of-things (IoT) technology will improve dramatically our daily life as a new pervasive computing paradigm. For the IoT computing, various devices and wireless networks are the hardware infrastructure, and service-oriented architecture (SOA) is a valuable software system that allows heterogeneous devices to interoperate each other. Even though IoT researchers have tackled a number of challenges for service composition, the orchestration techniques on IoT are rarely studied yet. Given a set of IoT services and a goal, the QoS-aware IoT service composition problem constructs a composite IoT service with the optimal accumulated QoS value, which satisfies the given goal specification. However, in the IoT environment, frequent changes happen inherently - for instance, temporary machine down, heavy system workload, and network failure. If the solution we have constructed is not valid anymore due to the changes, we should solve a new problem again. In this paper, we propose a novel incremental recomposition algorithm, which does not solve the new composition problem from scratch but explores only the changed space. In the experiment, our incremental recomposition algorithm can deal with the composition problem much faster than the original algorithm to solve from scratch.

A fast energy-centered and QoS-aware service composition approach for Internet of Things

In recent years, many scholars have paid more attention to the service-oriented computing for Internet of Things (IoT). With the increasing smart devices are deployed in IoT, it has become a challenging problem that how to quickly combine the services provided by IoT components into a comprehensive composite service to balance energy and quality of service (QoS). In this article, a fast energy-centered and QoS-aware service composition approach (FSCA-EQ) is proposed for IoT service composition. The FSCA-EQ employs the idea of hierarchical optimization. First, the compromise ratio method (CRM) is used to pre-select services satisfying user’s QoS requirements. Then, to reduce the energy consumption and extend the lifetime of IoT devices, the concept of relative dominance is applied to select the optimal service as the final composite service. The relative dominance of the service depends on its energy profile, QoS attributes, and user’s preferences. The simulation results show that the proposed algorithm performs better in terms of selection time, energy consumption, and optimality.

TOWARDS TRULY SMART CITY SERVICE PROVIDERS: A VIEW ON ON-DEMAND EVERYTHING AS A SERVICES

Internet of Things (IoT) connects a large number of devices/objects, and these devices are being increased that it is foretold their number will reach billions in the near future, and hence increasing the number of IoT-based services. IoT is built on and integrated with many existing technologies (e.g., wireless sensors, mobile computing, and cloud) which support its spreading and scalability and help it to overcome its known challenges like limited resources. Moreover, service composition supports IoT in the rapid development of new IoT applications leveraging the existing services. These paradigms and techniques are fundamental building blocks and enablers of smart cities. IoT service composition and IoT integration with the above-stated technologies have been addressed in research and industry works. In this paper, we review several types compositions and give our view on future smart city service providers. We also propose a location-aware architecture for integrating IoT services and mobile application services. The proposal is illustrated based on shopping mall scenario, and the high-level architecture is presented in this work.

Service Composition in IoT using Genetic algorithm and Particle swarm optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

Evaluating IoT service composition mechanisms for the scalability of IoT systems

The Internet of Things (IoT) is an emerging paradigm where practically every (physical and virtual) thing will be interconnected through innovative distributed services. Since the number of connected things is rapidly growing, IoT systems will require the composition of plenty of services into complex workflows. Thus, scalability in terms of the size of IoT systems becomes a significant concern. In this paper, we review and evaluate the fundamental semantics of existing IoT service composition mechanisms to determine how well they fulfil the scalability requirements of IoT systems. We identify scalability desiderata and, accordingly, our findings show that dataflows, orchestration and choreography do not fully satisfy such desiderata, unlike a novel composition mechanism called DX-MAN.

Multi-objective Optimization using NSGA II for service composition in IoT

Internet of Things (IoT) provides an innovative worldwide view to the things which are fit for communicating with one another. Each thing like temperature sensor, pressure sensor provides its functionality in the form of web service on the Internet. As the number of things connecting to the IoT network are increasing, thus one of the important challenges is to address the optimal selection of the web service to fulfil the user request. As the user requirements cannot be fulfilled using a single service so various services are composed to serve the IoT based system applications. This paper presents the usage of multi-objective metaheuristic search algorithm to provide optimal solution to Service Composition Problem (SCP) in IoT. Furthermore, this paper illustrates the use of one of the popular algorithms namely Non-dominated Sorting Genetic Algorithm (NSGA II). This paper looks at the way and methodology utilized to solve SCP in IoT. QoS parameters have become an important criterion for optimization. This algorithm is utilized to solve Quality of Service (QoS) based SCP. The results obtained are evaluated by analysing the no. of points plotted by pareto optimal curve. NSGA II is used to obtain the pareto fronts.

Service composition approaches for Internet of Things: a review

The internet of things (IoT) is the integration of information space and physical space, becoming more and more popular in several places. In order to answer the complicated user request, most IoT devices do not work alone, a multiple service composition must be made, and it is defined as the services composition. A number of approaches to IoT's service composition have been proposed in the literature. We have noticed a lack of studies that review the different works of the service composition in IoT. Thereby, in this paper, we present a review of existing approaches for service composition in IoT. It describes and compares them among each other with respect to some criteria, which we have defined based on the different challenges of service composition in IoT. Finally, we conclude by discussing open research issues and avenues for further work.

A Collaborative Internet of Things Architecture for Smart Cities and Environmental Monitoring

The collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as smart cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such Internet of Things (IoT) scenarios need in order to be pervasive. In this paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, mobile crowdsensing management for environmental data, information representation, and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.

Hyper-heuristic approach for Service Composition in Internet of Things

The state-of-the-art on internet of things (IoT), deals with its definition, its architecture, ontology of its components, networking and middleware. IoT contains a large number of devices which are placed around the world. Every device provides an IoT service. As devices joining the IoT are increasing, the services are also rising proportionately. In order to meet the user requirements for the complex application, we need a collection of suitable services, also known as service composition. Identifying the optimal service composition in IoT is a challenging task. This paper presents a hyper-heuristic approach, a latest trend in the field of stochastic optimisation, for the solution of service composition problem in internet of things. The efficacy of the hyper-heuristic approach is tested on 25 test data instances and the results are compared with genetic algorithm, the most widely used global optimisation technique.

Bi-Objective Optimization for Energy Aware Internet of Things Service Composition

In recent years, service-oriented-based Internet of Things (IoT) has received massive attention from research and industry. Integrating and composing smart objects functionalities or their services is required to create and promote more complex IoT applications with advanced features. When many smart objects are deployed, selecting the most appropriate set of smart objects to compose a service by considering both energy and quality of service (QoS) is an essential and challenging task. In this paper, we reduced the problem of finding an optimal balance between QoS level and the consumed energy of the IoT service composition to a bi-objective shortest path optimization (BSPO) problem and used an exact algorithm named pulse to solve the problem. The BSPO has two objectives, minimizing the QoS including execution time, network latency, and service price, and minimize the energy consumption of the composite service. Experimental evaluations show that the proposed approach has short execution time in various complex service profiles. Meanwhile, it can obtain good performance in energy consumption and thus network lifetime while maintaining a reasonable QoS level.

An Approach to Iot Service Optimal Composition for Mass Customization on Cloud Manufacturing

For implementing mass customization on cloud manufacturing (CMfg), Internet of Things (IoT)-enabled service optimal composition (ISOC) is a key technology used to effectively composite a manufacturing cloud service with selected IoT services to satisfy the users’ customized production requirements. The solving of the ISOC problem is done inefficiently in the context of the growing scale of IoT services and increasing sophistication of the ISOC execution path, being partly due to insufficient investigation of accumulated empirical knowledge (EK) of IoT services. In this paper, we propose an EK-oriented genetic algorithm (EK-GA) for the large-scale IoT service composition. First, by fully considering the distinctive features of IoT service and service domain features, the EK of IoT services are richly explored to divide the service space. Second, EK-oriented optimization strategies in the initial population, operators, and fitness function are presented to improve the local and global search abilities of the genetic algorithm for solving ISOC problems. Finally, the effectiveness of EK-GA for solving real-world ISOC problems in a private CMfg is verified through three types of experiments. By exploiting EK of IoT services for ISOC problems, this work makes novel contributions for mass customization on CMfg and enriches the practice of EK-oriented intelligence optimization.

Experimental Characterization of Mobile IoT Application Latency

The Internet of Things (IoT) emerges as a myriad of devices and services that interact to build complex distributed applications. Interoperability and standardization are imperative for the realization of this vision. Machine-to-machine (M2M) communications standards can be the middleware that glues together the IoT. However, standards are highly complex and require a large amount of interpretation, deployments are currently scarce, and performance evaluations simplistic or speculative. In this paper, we focus on the experimental evaluation of latency in IoT service composition with mobile gateways (GWs). We measure latency between system components and quantify application protocol overheads to assess the capabilities and limitations of a standard M2M middleware. We designed and implemented a mobile e-health use case on top of ETSI M2M and openEHR standards. We ran a pilot remote monitoring ten people for three weeks, collecting nearly 480 h of data. Our results show that while the latency added by a broker lies around 25 ms, the cellular network often exceeds 1 s, becoming a problem for interactive applications. Moreover, we observe that latencies between a smartphone GW and cloud hosted services vary largely depending on the user mobility, and on the promotion delay of the used wireless network.

An energy-aware service composition algorithm for multiple cloud-based IoT applications

There has been a shift in research towards the convergence of the Internet-of-Things (IoT) and cloud computing paradigms motivated by the need for IoT applications to leverage the unique characteristics of the cloud. IoT acts as an enabler to interconnect intelligent and self-configurable nodes “things” to establish an efficient and dynamic platform for communication and collaboration. IoT is becoming a major source of big data, contributing huge amounts of streamed information from a large number of interconnected nodes, which have to be stored, processed, and presented in an efficient, and easily interpretable form. Cloud computing can enable IoT to have the privilege of a virtual resources utilization infrastructure, which integrates storage devices, visualization platforms, resource monitoring, analytical tools, and client delivery. Given the number of things connected and the amount of data generated, a key challenge is the energy efficient composition and interoperability of heterogeneous things integrated with cloud resources and scattered across the globe, in order to create an on-demand energy efficient cloud based IoT application. In many cases, when a single service is not enough to complete the business requirement; a composition of web services is carried out. These composed web services are expected to collaborate towards a common goal with large amount of data exchange and various other operations. Massive data sets have to be exchanged between several geographically distributed and scattered services. The movement of mass data between services influences the whole application process in terms of energy consumption. One way to significantly reduce this massive data exchange is to use fewer services for a composition, which need to be created to complete a business requirement. Integrating fewer services can result in a reduction in data interchange, which in return helps in reducing the energy consumption and carbon footprint.This paper develops a novel multi-cloud IoT service composition algorithm called (E2C2) that aims at creating an energy-aware composition plan by searching for and integrating the least possible number of IoT services, in order to fulfil user requirements. A formal user requirements translation and transformation modelling and analysis is adopted for the proposed algorithm. The algorithm was evaluated against four established service composition algorithms in multiple cloud environments (All clouds, Base cloud, Smart cloud, and COM2), with the results demonstrating the superior performance of our approach.