Today's "smart'" domains are driven by lightweight battery operated devices carried by people and embedded in environments. Many applications rely on continuous neighbor discovery, i.e., the ability to detect other nearby devices. Application uses for neighbor discovery are widely varying, but they all rely on a protocol in which devices exchange periodic beacons containing device identifiers. Many applications also ultimately involve assessing and adapting to context information sensed about the physical world and the device's situation in that world (e.g., its location or speed, the ambient temperature or sound, etc.). In this paper, we define Proactive Implicit Neighborhood Context Heuristics (PINCH), which leverages unused payload in periodic neighbor discovery beacons to opportunistically distribute context information in a local area. PINCH's self-organizing algorithms use limited local views of the state of a one-hop network neighborhood to determine the most useful type of context information for a device to sense and share. In this paper, we develop the algorithms, integrate an implementation of PINCHwith a smart city simulator, and benchmark the tradeoffs of self-organized local context sharing with 2.4GHz neighbor discovery beacons.
{"title":"PINCH: Self-Organized Context Neighborhoods for Smart Environments","authors":"Chenguang Liu, C. Julien, A. Murphy","doi":"10.1109/SASO.2018.00023","DOIUrl":"https://doi.org/10.1109/SASO.2018.00023","url":null,"abstract":"Today's \"smart'\" domains are driven by lightweight battery operated devices carried by people and embedded in environments. Many applications rely on continuous neighbor discovery, i.e., the ability to detect other nearby devices. Application uses for neighbor discovery are widely varying, but they all rely on a protocol in which devices exchange periodic beacons containing device identifiers. Many applications also ultimately involve assessing and adapting to context information sensed about the physical world and the device's situation in that world (e.g., its location or speed, the ambient temperature or sound, etc.). In this paper, we define Proactive Implicit Neighborhood Context Heuristics (PINCH), which leverages unused payload in periodic neighbor discovery beacons to opportunistically distribute context information in a local area. PINCH's self-organizing algorithms use limited local views of the state of a one-hop network neighborhood to determine the most useful type of context information for a device to sense and share. In this paper, we develop the algorithms, integrate an implementation of PINCHwith a smart city simulator, and benchmark the tradeoffs of self-organized local context sharing with 2.4GHz neighbor discovery beacons.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information system security certification involves guaranteeing that mechanisms are deployed to comply with selected security controls, such as those in the NIST SP800-53, at acceptable levels of confidence and risk. When a system can self-adapt at runtime, it may alter its functional behavior to address a defect or anomaly. This functional change can impact associated security controls, potentially making the adapted system vulnerable to security threats. Performing security control assurance adaptation along with functional adaptation would allow both compliance confidence and risk analysis to accompany functional adaptation analysis. The need for this dual assessment implies security control compliance should be expressed such that an adaptation can be reflected as part of its compliance status. In this paper, we represent security controls and their deployed mechanisms in terms of security assurance cases. We define a template using Goal Structuring Notation (GSN) that follows the NIST SP800-53 control statement structure. We define three adaptation operators to dictate how and where a change impacts relevant assurance cases. The objective is to express and manage the controls and adaptation operators so that changes to a security assurance case can be embedded and traced within the executing system to make it security aware. We illustrate the approach using a small case study and a security control for systems and communications protection, taken from the NIST SP800-53.
{"title":"Self-Adaptation Strategies to Maintain Security Assurance Cases","authors":"Sharmin Jahan, Allen Marshall, R. Gamble","doi":"10.1109/SASO.2018.00031","DOIUrl":"https://doi.org/10.1109/SASO.2018.00031","url":null,"abstract":"Information system security certification involves guaranteeing that mechanisms are deployed to comply with selected security controls, such as those in the NIST SP800-53, at acceptable levels of confidence and risk. When a system can self-adapt at runtime, it may alter its functional behavior to address a defect or anomaly. This functional change can impact associated security controls, potentially making the adapted system vulnerable to security threats. Performing security control assurance adaptation along with functional adaptation would allow both compliance confidence and risk analysis to accompany functional adaptation analysis. The need for this dual assessment implies security control compliance should be expressed such that an adaptation can be reflected as part of its compliance status. In this paper, we represent security controls and their deployed mechanisms in terms of security assurance cases. We define a template using Goal Structuring Notation (GSN) that follows the NIST SP800-53 control statement structure. We define three adaptation operators to dictate how and where a change impacts relevant assurance cases. The objective is to express and manage the controls and adaptation operators so that changes to a security assurance case can be embedded and traced within the executing system to make it security aware. We illustrate the approach using a small case study and a security control for systems and communications protection, taken from the NIST SP800-53.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Following a tradition of alternating venues between the United States and Europe, SASO 2018 is organised in Trento, Italy. Past editions have travelled to Boston, Venice, San Francisco, Budapest, Ann Arbor, Lyon, Philadelphia, London, Boston, Augsburg and Tucson. In Trento, SASO is jointly organized by the Bruno Kessler Foundation (FBK) and the University of Trento, two of the most important research and academic institutions in Italy. Trento is a beautiful city in the heart of the Dolomites Alps, near Lake Garda. The center has a strong Renaissance mark with several beautiful buildings adorned by frescoes, some built to accommodate delegates to the Council of Trent (15451563). The surroundings of Trento offer beautiful naturalistic tracks on top of mountains (e.g., Monte Bondone), around the numerous lakes or nearby ancient castles.
{"title":"Message from the SASO 2018 General Chairs","authors":"","doi":"10.1109/saso.2018.00005","DOIUrl":"https://doi.org/10.1109/saso.2018.00005","url":null,"abstract":"Following a tradition of alternating venues between the United States and Europe, SASO 2018 is organised in Trento, Italy. Past editions have travelled to Boston, Venice, San Francisco, Budapest, Ann Arbor, Lyon, Philadelphia, London, Boston, Augsburg and Tucson. In Trento, SASO is jointly organized by the Bruno Kessler Foundation (FBK) and the University of Trento, two of the most important research and academic institutions in Italy. Trento is a beautiful city in the heart of the Dolomites Alps, near Lake Garda. The center has a strong Renaissance mark with several beautiful buildings adorned by frescoes, some built to accommodate delegates to the Council of Trent (15451563). The surroundings of Trento offer beautiful naturalistic tracks on top of mountains (e.g., Monte Bondone), around the numerous lakes or nearby ancient castles.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123960998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Mera-Gómez, Francisco Ramírez, R. Bahsoon, R. Buyya
A multi-tenant Software as a Service (SaaS) application is a highly configurable software that allows its owner to serve multiple tenants, each with their own workflows, workloads and Service Level Objectives (SLOs). Tenants are usually organizations that serve several users and the application appears to be a different one for each tenant. However, in practice, multi-tenant SaaS applications limit the diversity of tenants by clustering them in a few categories (e.g. premium, standard) with predefined SLOs. Additionally, this coarse-grained clustering reduces the advantage of these multi-tenant ecosystems over single tenant architectures to share dynamically virtual resources between tenants based on their own workload profile and elasticity adaptation decisions. To address this limitation, we propose a multi-agent elasticity management where each tenant is represented by a reinforcement learning agent that performs elasticity adaptations based on a new technical debt perspective, and make use of debt attributes (i.e. amnesty, interest) to form autonomous coalitions that minimise the effect of the unavoidable imperfections in any elasticity management approach. We extended CloudSim and Burlap to evaluate our approach. The simulation results indicate that our debt-aware multi-agent elasticity management preserves the diversity of tenants and reduces SLO violations without affecting the aggregate utility of the application owner.
{"title":"A Multi-Agent Elasticity Management Based on Multi-Tenant Debt Exchanges","authors":"C. Mera-Gómez, Francisco Ramírez, R. Bahsoon, R. Buyya","doi":"10.1109/SASO.2018.00014","DOIUrl":"https://doi.org/10.1109/SASO.2018.00014","url":null,"abstract":"A multi-tenant Software as a Service (SaaS) application is a highly configurable software that allows its owner to serve multiple tenants, each with their own workflows, workloads and Service Level Objectives (SLOs). Tenants are usually organizations that serve several users and the application appears to be a different one for each tenant. However, in practice, multi-tenant SaaS applications limit the diversity of tenants by clustering them in a few categories (e.g. premium, standard) with predefined SLOs. Additionally, this coarse-grained clustering reduces the advantage of these multi-tenant ecosystems over single tenant architectures to share dynamically virtual resources between tenants based on their own workload profile and elasticity adaptation decisions. To address this limitation, we propose a multi-agent elasticity management where each tenant is represented by a reinforcement learning agent that performs elasticity adaptations based on a new technical debt perspective, and make use of debt attributes (i.e. amnesty, interest) to form autonomous coalitions that minimise the effect of the unavoidable imperfections in any elasticity management approach. We extended CloudSim and Burlap to evaluate our approach. The simulation results indicate that our debt-aware multi-agent elasticity management preserves the diversity of tenants and reduces SLO violations without affecting the aggregate utility of the application owner.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121596320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies spatial motion of the agents over time, and retrieves agent-level parameters required to execute that motion. A framework for reproducing emergent behavior, given an abstract demonstration, is discussed in existing work. Each query to the framework is independent of previous queries. Our work addresses this information communication deficit and incorporates a feedback mechanism to iteratively improve the quality of the reproduced behavior. This is explored by variation of regression parameters and data points used. Using data point selection to improve demonstration replication is established as a means of iterative optimization. Using optimization also shows potential for improved demonstration replication capability for the framework.
{"title":"Implementing Feedback for Programming by Demonstration","authors":"K. K. Budhraja, T. Oates","doi":"10.1109/SASO.2018.00028","DOIUrl":"https://doi.org/10.1109/SASO.2018.00028","url":null,"abstract":"Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies spatial motion of the agents over time, and retrieves agent-level parameters required to execute that motion. A framework for reproducing emergent behavior, given an abstract demonstration, is discussed in existing work. Each query to the framework is independent of previous queries. Our work addresses this information communication deficit and incorporates a feedback mechanism to iteratively improve the quality of the reproduced behavior. This is explored by variation of regression parameters and data points used. Using data point selection to improve demonstration replication is established as a means of iterative optimization. Using optimization also shows potential for improved demonstration replication capability for the framework.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124118312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pervasive sensing and actuation applications are increasingly being built using distributed devices connected with low-power wireless links. Most of these applications exploit anarchic protocols in which devices independently attempt to seize communication resources, supporting only best-effort applications as the communication they rely on cannot be guaranteed. For strict quality of service requirements, a few, non-anarchic, disciplined approaches exist in which nodes coordinate and resources are guaranteed to individual devices. Unfortunately, these solutions come at a considerable cost to form and conform to rigid communication schedules while considering the inherent volatility of the wireless environment. This work proposes Reins-MAC, a fully distributed solution that adapts to changes in the wireless environment and forms a flexible communication schedule able to support quality of service requirements. Inspired by pulse-coupled oscillators, the mathematical formulation of firefly flash synchronization, our approach forms and reserves communication slots of variable size in an online and adaptive manner. Reins-MAC tailors communication resources to network conditions that vary in time and space as well as to the explicit communication needs of devices by enabling distributed, dynamic changes to established schedules. Ultimately, Reins-MAC allows higher level abstractions to rein in the protocol anarchy, laying the foundation for reliable wireless applications.
{"title":"Reins-MAC: Firefly Inspired Communication Scheduling for Reliable Low-Power Wireless","authors":"M. Ceriotti, A. Murphy","doi":"10.1109/SASO.2018.00025","DOIUrl":"https://doi.org/10.1109/SASO.2018.00025","url":null,"abstract":"Pervasive sensing and actuation applications are increasingly being built using distributed devices connected with low-power wireless links. Most of these applications exploit anarchic protocols in which devices independently attempt to seize communication resources, supporting only best-effort applications as the communication they rely on cannot be guaranteed. For strict quality of service requirements, a few, non-anarchic, disciplined approaches exist in which nodes coordinate and resources are guaranteed to individual devices. Unfortunately, these solutions come at a considerable cost to form and conform to rigid communication schedules while considering the inherent volatility of the wireless environment. This work proposes Reins-MAC, a fully distributed solution that adapts to changes in the wireless environment and forms a flexible communication schedule able to support quality of service requirements. Inspired by pulse-coupled oscillators, the mathematical formulation of firefly flash synchronization, our approach forms and reserves communication slots of variable size in an online and adaptive manner. Reins-MAC tailors communication resources to network conditions that vary in time and space as well as to the explicit communication needs of devices by enabling distributed, dynamic changes to established schedules. Ultimately, Reins-MAC allows higher level abstractions to rein in the protocol anarchy, laying the foundation for reliable wireless applications.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124836086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SASO’s role as a venue that bridges conceptual and applied research in the areas of self-adaptive and selforganizing systems was once again reflected in the main technical program. Papers on the fundamentals of self-organization and self-adaptation were complemented by those on the topics of testing/analysis, cloud applications, and resource management. Socio-technical aspects also continued to provide a strong theme for the conference.
{"title":"Message from the SASO 2018 Program Committee Chairs","authors":"J. Beal, N. Bencomo, J. Botev","doi":"10.1109/saso.2018.00006","DOIUrl":"https://doi.org/10.1109/saso.2018.00006","url":null,"abstract":"SASO’s role as a venue that bridges conceptual and applied research in the areas of self-adaptive and selforganizing systems was once again reflected in the main technical program. Papers on the fundamentals of self-organization and self-adaptation were complemented by those on the topics of testing/analysis, cloud applications, and resource management. Socio-technical aspects also continued to provide a strong theme for the conference.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134225689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SASO 2018 Organizing Committee","authors":"","doi":"10.1109/saso.2018.00008","DOIUrl":"https://doi.org/10.1109/saso.2018.00008","url":null,"abstract":"","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122596016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Devising test strategies for specific test goals relies on predictions of the run-time behavior of the software system under test (SuT) based on specifications, models, or the code. For a system following a single strategy as run-time behavior, the test strategy can be fixed at design time. For an adaptive system, which may choose from several strategies due to environment changes, a combination of test strategies has to be found, which still can be achieved at design time provided that all system strategies and the switching policy are predictable. Self-adaptive systems, also adapting their system strategies and strategy switches according to the environmental dynamics, render such design-time predictions futile, but there also the test strategies have to be adapted at run time. We characterize the necessary interplay between system strategy adaptation of the SuT and test strategy adaptation of the tester as a Stochastic Game. We argue that the tester's part, formalized by means of a Markov Decision Process, can be automatically solved by the use of Reinforcement Learning methods where we discuss both model-based and model-free variants. Finally, we propose a particular framework inspired by Direct Future Prediction which, given a simulation of the SuT and its environment, autonomously finds good test strategies w.r.t. imposed quanti?able goals. While these goals, in general, can be initialized arbitrarily, our evaluation concentrates on risk-based goals rewarding the detection of hazardous failures.
{"title":"Risk-Based Testing of Self-Adaptive Systems Using Run-Time Predictions","authors":"André Reichstaller, Alexander Knapp","doi":"10.1109/SASO.2018.00019","DOIUrl":"https://doi.org/10.1109/SASO.2018.00019","url":null,"abstract":"Devising test strategies for specific test goals relies on predictions of the run-time behavior of the software system under test (SuT) based on specifications, models, or the code. For a system following a single strategy as run-time behavior, the test strategy can be fixed at design time. For an adaptive system, which may choose from several strategies due to environment changes, a combination of test strategies has to be found, which still can be achieved at design time provided that all system strategies and the switching policy are predictable. Self-adaptive systems, also adapting their system strategies and strategy switches according to the environmental dynamics, render such design-time predictions futile, but there also the test strategies have to be adapted at run time. We characterize the necessary interplay between system strategy adaptation of the SuT and test strategy adaptation of the tester as a Stochastic Game. We argue that the tester's part, formalized by means of a Markov Decision Process, can be automatically solved by the use of Reinforcement Learning methods where we discuss both model-based and model-free variants. Finally, we propose a particular framework inspired by Direct Future Prediction which, given a simulation of the SuT and its environment, autonomously finds good test strategies w.r.t. imposed quanti?able goals. While these goals, in general, can be initialized arbitrarily, our evaluation concentrates on risk-based goals rewarding the detection of hazardous failures.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132050161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julian Hanke, Oliver Kosak, Alexander Schiendorfer, W. Reif
Mobile robot systems usually are designed, built, and programmed for dedicated use cases. Consequently, especially for unmanned aerial vehicles diverse applications result in very heterogeneously designed robots. To overcome this need for specialization, we propose to dynamically adapt the robots' capabilities at run-time. This is done by connecting and disconnecting hardware modules providing those capabilities, i.e., re-allocating resources within the robot ensemble. Thereby, no longer individualized robots have to be designed for different tasks. Instead, the system is enabled to adapt its hardware configuration to changing requirements. For calculating necessary adaptations, i.e., solving the resource allocation problem, we propose a heuristic, market-based approach that exploits the possibility to decompose the resource allocation problem and distributively finds a solution. We show that our approach outperforms a centralized one especially when increasing the problem size in terms of agents, tasks, and relevant capabilities while providing the same quality.
{"title":"Self-Organized Resource Allocation for Reconfigurable Robot Ensembles","authors":"Julian Hanke, Oliver Kosak, Alexander Schiendorfer, W. Reif","doi":"10.1109/SASO.2018.00022","DOIUrl":"https://doi.org/10.1109/SASO.2018.00022","url":null,"abstract":"Mobile robot systems usually are designed, built, and programmed for dedicated use cases. Consequently, especially for unmanned aerial vehicles diverse applications result in very heterogeneously designed robots. To overcome this need for specialization, we propose to dynamically adapt the robots' capabilities at run-time. This is done by connecting and disconnecting hardware modules providing those capabilities, i.e., re-allocating resources within the robot ensemble. Thereby, no longer individualized robots have to be designed for different tasks. Instead, the system is enabled to adapt its hardware configuration to changing requirements. For calculating necessary adaptations, i.e., solving the resource allocation problem, we propose a heuristic, market-based approach that exploits the possibility to decompose the resource allocation problem and distributively finds a solution. We show that our approach outperforms a centralized one especially when increasing the problem size in terms of agents, tasks, and relevant capabilities while providing the same quality.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114987011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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