Adaptive Service Selection According to the Service Density in Multiple Qos Aspects

Cho, Jaehyun; Ko, Han-Gyu; Ko, In-Young

doi:10.1109/tsc.2015.2428251

Cited by 26 publications

(6 citation statements)

References 21 publications

(27 reference statements)

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“…The results of experiments present significant improvements when prediction is carried out. A heuristic approach with the benefits of local and global optimizations improves the performance of service selection process [16]. An abstract service with a number of candidate services is defined for reducing the selection complexity.…”

Section: Service Selectionmentioning

confidence: 99%

A QoS-aware service discovery and selection mechanism for IoT environments

Demir

2021

Sādhanā

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Internet of Things (IoT) enables the interconnection of computing devices embedded in everyday objects to create smart environments. In such an environment, an user-defined task can be performed composing multiple services provided by the physical objects. Discovering and selecting an appropriate service meeting Quality of Services (QoS) requirements among the large amount of dynamic IoT service provider nodes are significant issues. Existing approaches employ either query message broadcasting or regular service probing methods to discover the IoT services with near-real-time QoS values, which are both costly approaches. In particular, the centralized approach could suffer from single-point failure and scalability issues. To address these issues, we propose QoS and energy-aware service discovery and selection algorithms. The proposed discovery algorithm makes use of a peer-to-peer (P2P)-based resource discovery mechanism running on an overlay network constructed using users' devices. This structure paves the way for the exploration of near-real-time QoS values of the services, avoiding high message overhead as well as overcoming the availability and scalability shortcomings of the existing centralized and decentralized approaches. Furthermore, selecting the most convenient services concerning diverse QoS attributes among the services found becomes an optimization problem. We propose an optimization algorithm that reflects the likely QoS through assigning a dominance value to services. The performance of the proposed algorithms in terms of message overhead, latency, scalability, reliability and QoS accuracy is evaluated through extensive simulations on a highly realistic test-bed. Performance comparisons with the existing works clearly prove the effectiveness of the proposed idea.

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Section: Service Selectionmentioning

confidence: 99%

A QoS-aware service discovery and selection mechanism for IoT environments

Demir

2021

Sādhanā

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“…user preferences). However, user activities can dynamically change due to many factors such as user mobility and QoE [13,16,32]. For example, video content streaming function, in a user activity, needs to be fulfilled by the closest device to a user to improve user QoE.…”

Section: Access Control Quality Of Experience Awarementioning

confidence: 99%

Fine-grained Access Control for Internet of Things Smart Spaces Driven by User Inputs

Al-Shaboti¹

2021

Preprint

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The increasing use of Internet of Things (IoT) devices raises security and privacy concerns. In smart spaces, multiple IoT devices are simultaneously used to fulfil user activity functions. However, these devices exhibit several security vulnerabilities that can compromise smart space security and privacy. The ability of fine-grained control network access in IoT devices and application messages can significantly reduce the risk resulting from the exploitation of IoT vulnerabilities due to unauthorised access, thereby improving smart space security. A well-recognised approach in the literature for IoT access control is to use pre-defined access policies to allow the necessary connections for a device to function correctly. However, these policies allow access to all device functions (i.e. coarse-grained access) including those functions that are not used by any user activity. The overall goal of this thesis is to develop an access control framework and techniques to achieve fine-grained access policies by using user inputs. The user inputs will be utilised to select devices to fulfil user activities aiming to build an access policy from the minimum access required for each device function. In this thesis, the use of user inputs to meet user security and privacy requirements in single- and multi-user smart spaces is studied. The main contributions are as follows: first, an access control framework that enables users to tailor IoT device policies to meet their security and privacy requirements is proposed. Validation results of the framework show the effectiveness of integrating user access rules into the existing security countermeasures (i.e. pre-defined policies and intrusion detection systems – IDS) to enforce user security and privacy. Second, the problem of selecting preferable devices to fulfil user activity functions is formulated as an optimisation problem. The optimisation problem is then solved by local and global optimisation searching algorithms that are guided by a developed user preference quantified model. The results show that global optimisation search algorithms such as Genetic Algorithm (GA) find the solution more effectively and efficiently than local search algorithms such as simulated annealing and hill-climbing. Third, sharing access control for multi-user smart spaces is proposed. Traditional access control that considers a single user is not suitable for multi-user smart spaces, where users share their IoT devices. The sharing between multiple users poses challenges different than in single-user access control. For example, users may abuse using shared devices and use vulnerable ones. This thesis addresses these two challenges through two contributions. First, it proposes a novel sharing policy language that enables users to precisely define their sharing policy. Second, this thesis formulates the sharing policies as constraints in the context of an optimisation problem with the objective function that maximises the use of secure devices. Results show that the IoT sharing issue can naturally be translated into an integer linear programming (ILP) problem and effectively solved using off-the-shelf ILP solvers. Fourth, this thesis explores the feasibility and practicality of the fine-grained access policy enforcement through a smart home case study. A case study is built using a hub-based architecture that uses Web of Things (WoT) technology. WoT provides a device semantic description that includes device functions with the corresponding Uniform Resource Identifier (URI) which is used to build access control policies. The case study results show that policy enforcement can be effectively achieved by directing network traffic through a device proxy for each IoT device to enforce application access control without introducing statistically significant overhead on the user activity running time. In summary, this thesis studies the use of user inputs to derive fine-grained access control in smart spaces. For a single-user access control system, this thesis considers using manual rules and user preferences in small and dense smart spaces, respectively. For a multi-user access control system, this thesis proposes a secure sharing system supported by a sharing policy language to share and use IoT devices securely. For each scenario analysed, user input is utilised to derive fine-grained access policies. Enforcement of these policies has been explored by implementing a smart space case study using WoT technology. The overall results show that user preferences and sharing policies can be used to derive fine-grained access policies that are transparent to users and meet their security and privacy requirements.

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“…Moreover, the authors do not consider energy requirements, which are of paramount importance when we deal with large IoT systems. Multiple QoS parameters are addressed instead in [9]. The work focuses on the selection of services among a huge set of candidate services, each one characterized by different QoS parameters.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient IoT Service Brokering with Quality of Service Support

Tanganelli

Mingozzi

2019

Sensors

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The Internet of Things (IoT) is becoming real, and recent studies highlight that the number of IoT devices will significantly grow in the next decade. Such massive IoT deployments are typically made available to applications as a service by means of IoT platforms, which are aware of the characteristics of the connected IoT devices–usually constrained in terms of computation, storage and energy capabilities–and dispatch application’s service requests to appropriate devices based on their capabilities. In this work, we develop an energy-aware allocation policy that aims at maximizing the lifetime of all the connected IoT devices, whilst guaranteeing that applications’ Quality of Service (QoS) requirements are met. To this aim, we formally define an IoT service allocation problem as a non-linear Generalized Assignment Problem (GAP). We then develop a time-efficient heuristic algorithm to solve the problem, which is shown to find near-optimal solutions by exploiting the availability of equivalent IoT services provided by multiple IoT devices, as expected especially in the case of massive IoT deployments.

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Adaptive Service Selection According to the Service Density in Multiple Qos Aspects

Cited by 26 publications

References 21 publications

A QoS-aware service discovery and selection mechanism for IoT environments

A QoS-aware service discovery and selection mechanism for IoT environments

Fine-grained Access Control for Internet of Things Smart Spaces Driven by User Inputs

Energy-Efficient IoT Service Brokering with Quality of Service Support

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