Intelligent Resource Management and Dynamic Adaptation in a Distributed Real-time and Embedded Sensor Web System

Kinnebrew, John S.; Otte, William R.; Shankaran, N.; Biswas, Gautam; Schmidt, Douglas C.

doi:10.1109/isorc.2009.43

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adaptation is, generally, characteristic of decentralised sensor web systems, composed of agents or actors with local visions. In SEAMONSTER [36], autonomic adaptation is performed by local agents to meet ‘mission objectives’. In IrisNet [25], another agent‐based sensor web solution, developers can add logic to sensing agents, writing pieces of code (‘senselets’) to process RD and provide QoO adaptation.…”

Section: Requirements For Future Solutionssupporting

confidence: 60%

See 1 more Smart Citation

Survey on Quality of Observation within Sensor Web systems

Auger

Expósito

Lochin

2017

IET wirel. sens. syst.

View full text Add to dashboard Cite

The Sensor Web vision refers to the addition of a middleware layer between sensors and applications. To bridge the gap between these two layers, Sensor Web systems must deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Managing such diversity at the application level can be complex and requires high levels of expertise from application developers. Moreover, as an information-centric system, any Sensor Web should provide support for Quality of Observation (QoO) requirements. In practice, however, only few Sensor Webs provide satisfying QoO support and are able to deliver high-quality observations to end consumers in a specific manner.This survey aims to study why and how observation quality should be addressed in Sensor Webs. It proposes three original contributions. First, it provides important insights into quality dimensions and proposes to use the QoO notion to deal with information quality within Sensor Webs. Second, it proposes a QoO-oriented review of 29 Sensor Web solutions developed between 2003 and 2016, as well as a custom taxonomy to characterise some of their features from a QoO perspective. Finally, it draws four major requirements required to build future adaptive and QoO-aware Sensor Web solutions.

show abstract

Section: Requirements For Future Solutionssupporting

confidence: 60%

“…SEAMONSTER [36] is a concrete sensor web deployment for glacier and watershed monitoring in Alaska. This solution is based on a multi‐agent architecture for coordinated, responsive observations platform.…”

Section: Evaluation Of Existing Sensor Web Solutionsmentioning

confidence: 99%

Survey on Quality of Observation within Sensor Web systems

Auger

Expósito

Lochin

2017

IET wirel. sens. syst.

View full text Add to dashboard Cite

show abstract

“…Information in DRE systems must therefore be delivered according to stringent quality-of-service (QoS) needs, despite failures and resource limitations [13], [15], [12].…”

Section: James Edmondson Completed Work While At Vanderbilt Universitymentioning

confidence: 99%

Approximation Techniques for Maintaining Real-Time Deployments Informed by User-Provided Dataflows within a Cloud

Edmondson

Gokhale

Schmidt

2012

2012 IEEE 31st Symposium on Reliable Distributed Systems

View full text Add to dashboard Cite

Abstract-Distributed applications are increasingly developed by composing many participants, such as services, components, and objects. When deploying distributed applications into a mobile ad hoc cloud, the locality of application participants that communicate with each other can affect latency, power/battery usage, throughput, and whether or not a cloud provider can meet service-level agreements (SLA). Optimization of important communication links within a distributed application is particularly important when dealing with mission-critical applications deployed in a distributed real-time and embedded (DRE) scenario, where violation of SLAs may result in loss of property, cyber infrastructure, or lives.To complicate the optimization process, the underlying cloud environment can change during operation and an optimal deployment of the distributed application may degrade over time due to hardware failures, overloaded hosts, and other issues that are beyond the control of distributed application developers. To optimize performance of distributed applications in dynamic environments, therefore, the deployment of participants may need adapting and revising according to the requirements of application developers and the resources available in the underlying cloud environment. This paper present two contributions to the study of dynamic optimizations of user-provided deployments within a cloud. First, we present a dataflow description language that allows developers to designate key communication paths between participants within their distributed applications. Second, we describe heuristics that use this dataflow representation to identify optimal configurations for initial deployments and/or subsequent redeployments within a cloud. An experiment is presented to validate the heuristic approaches.

show abstract

“…Moreover, they must be able to adapt the local system in light of resource constraints and fluctuations throughout the sensor web to maintain efficient and correct operation of the overall system. Prior work [9] describes how MACRO addresses these challenges by combining the planning and resource management services of its server agents with the template plan schemas of its field agents. This paper extends our prior work by focusing on the following unexplored challenges associated with providing a flexible deployment infrastructure to support system management and dynamic adaptation of the SEAMONSTER field nodes.…”

Section: Distributed Deployment and Adaptation Challenges In Sea-monstermentioning

confidence: 99%

A flexible infrastructure for distributed deployment in adaptive sensor webs

Otte

Kinnebrew

Schmidt

et al. 2009

2009 IEEE Aerospace Conference

Self Cite

View full text Add to dashboard Cite

Abstract-Distributed sensor webs typically operate in dynamic environments where operating conditions, transient phenomena, availability of resources, and network connection quality change frequently and unpredictably. Often these changes can neither be completely anticipated nor accurately described during development or deployment. Our prior work has described how we developed agents and services that are capable of monitoring these changing conditions and adapting system parameters using the CORBA Component Model (CCM) deployment infrastructure as part of the Multiagent Architecture for Coordinated Responsive Observations (MACRO) platform.Our recent application of MACRO to the South East Alaska MOnitoring Network for Science, Telecommunications, Education, and Research (SEAMONSTER) project has identified new distributed deployment infrastructure challenges common to computationally constrained field environments in adaptive sensor webs. These challenges include standardized execution of low-level hardware-dependent actions and on-going data tasks, automated provisioning of agents for heterogeneous field hardware, and minimizing deployment infrastructure overhead. This paper describes how we extended MACRO to address these sensor web challenges by creating an action/effector framework standardizing the execution of lightweight actions and providing for automated provisioning of MACRO agents, in addition to footprint optimizations to the underlying CCM infrastructure.

show abstract

Intelligent Resource Management and Dynamic Adaptation in a Distributed Real-time and Embedded Sensor Web System

Cited by 3 publications

References 21 publications

Survey on Quality of Observation within Sensor Web systems

Survey on Quality of Observation within Sensor Web systems

Approximation Techniques for Maintaining Real-Time Deployments Informed by User-Provided Dataflows within a Cloud

A flexible infrastructure for distributed deployment in adaptive sensor webs

Contact Info

Product

Resources

About