A policy-based management architecture for mobile collaborative teams

Asmare, Eskindir; Gopalan, Anandha; Sloman, Morris; Dulay, Naranker; Lupu, Emil

doi:10.1109/percom.2009.4912765

Cited by 7 publications

(11 citation statements)

References 19 publications

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“…The management architecture [13] has three layers, namely, mission, team and communication, as shown in Fig. 1.…”

Section: Methodsmentioning

confidence: 99%

“…The commander is provided with a mission specification by its command base and assembles the required UAVs to perform the mission. The mission specification [13] defines how specific roles are assigned to UAVs based on their credentials and capabilities, as well as when and how to adapt the mission to changes in context or failures. The adaptive management of UAVs is achieved using policy-based techniques that allow dynamic modification of the management strategy relating to resources, task behaviour, communications and team management, without reloading the basic software within the UAV.…”

Section: Introductionmentioning

confidence: 99%

“…It is thus necessary to perform optimisation on the set of discovered UAVs and the mission roles in order to best utilise the available resources as first-come-first-served is suboptimal. This paper extends the architecture presented in [4,13,14] by elaborating the different components of the framework and presenting an optimisation scheme that optimally assigns available UAVs to roles based on their capabilities. The scheme considers both local assignments by the commander and global future assignments by other team members, so that the team will reach its optimal configuration provided UAVs with the necessary capabilities are available.…”

Section: Introductionmentioning

confidence: 99%

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Self-Management Framework for Mobile Autonomous Systems

et al. 2011

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The advent of mobile and ubiquitous systems has enabled the development of autonomous systems such as wireless-sensors for environmental data collection and teams of collaborating Unmanned Autonomous Vehicles (UAVs) used in missions unsuitable for humans. However, with these range of new application-domains come a new challenge -enabling self-management in mobile autonomous systems. Autonomous systems have to be able to manage themselves individually as well as to form self-managing teams which are able to recover or adapt to failures, protect themselves from attacks and optimise performance.This paper proposes a novel distributed policy-based framework that enables autonomous systems of varying scale to perform self-management individually and as a team. The framework allows missions to be specified in terms of roles in an adaptable and reusable way, enables dynamic and secure team formation with a utility-based approach for optimal role assignment, caters for communication link maintenance amongst team-members and recovery from failure. Adaptive management is achieved by employing a policy-based architecture to enable dynamic modification of the management strategy relating to resources, role behaviour, communications and team management, without interrupting the basic software within the system. Evaluation of the framework shows that it is scalable with respect to the number of roles, and consequently the number of autonomous systems participating in the mission. It is also shown to be optimal with respect to role assignments, and robust to intermittent communication link disconnections and permanent team-member failures.

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“…The management architecture [13] has three layers, namely, mission, team and communication, as shown in Fig. 1.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self-Management Framework for Mobile Autonomous Systems

et al. 2011

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“…• Missions have been introduced to support grouping of multiple policies for deployment to nodes and roles are introduced as placeholders in policy specifications to which nodes can be assigned, similarly to [1] • The initial Finger design for parsing policies used a minimal dispatcher for commands based on incoming events. Although this allowed for a lightweight response mechanism, it limited the strategies expressed by policies.…”

Section: Evolution From Fingermentioning

confidence: 99%

Starfish

Bourdenas

Sloman

2010

Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems

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Wireless sensor networks are a key aspect of many pervasive systems designed to aid people in their normal activities and adapt to their current context. However, these systems also need to be self-managing in discovering and configuring devices for services, detecting and responding to attacks, determining errors and faults and reconfiguring the system to mitigate these. In this paper we describe the Starfish framework for specifying and dynamically managing policies in sensor nodes. We discuss the components in the framework which include the Finger2 policy system for specifying dynamic adaptivity, a module library to simplify the programming the basic funtionality of nodes and a client side editor for managing policies. We describe policies for an adaptive healthcare body network then focus on policies for self-healing aspects of sensor networks and give examples of policy-based reconfigurations to deal with faults.

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“…Policy-based management has been successfully used in managing distributed systems and computer networks, and this success has been carried over to autonomous pervasive systems such as: body-sensor networks (BSNs) [6] and unmanned autonomous vehicles (UAVs) [7]. The management is usually based on Event-Condition-Action (ECA) policies that issue actions to managed objects.…”

Section: Introductionmentioning

confidence: 99%

Teleo-Reactive policies for managing human-centric pervasive services

Marinovic

Twidle

Dulay

et al. 2010

2010 International Conference on Network and Service Management

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Abstract-Event-Condition-Action (ECA) policies are often used to manage various aspects of adaptation and execution of pervasive systems. Such policies are well suited for services where: 1) given actions are reliably executed when they are requested, 2) there is no priority ordering amongst multiple available actions, and 3) execution is instantaneous with respect to the validity of conditions under which they were initiated. However, for a pervasive service that integrates human agents and human activities, these assumptions do not generally hold. Humans may misbehave by postponing the execution of certain actions or ignoring them all together. Performing an action may take a long time so that the action is no longer needed or more important actions may need to be executed. Managing such behaviours through ECA policies is complex and difficult to implement. This paper introduces a new management policy type, called a Teleo-Reactive policy, whose semantics are based on continuous monitoring of the environment and prioritising available actions. The semantics result in more flexible and concise formulation of management policies for human-centric pervasive services. We demonstrate how these policies can be applied in a real-world use case scenario set in a nursing home and describe the underlying implementation based on the Android's Java platform.

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A policy-based management architecture for mobile collaborative teams

Cited by 7 publications

References 19 publications

Self-Management Framework for Mobile Autonomous Systems

Self-Management Framework for Mobile Autonomous Systems

Starfish

Teleo-Reactive policies for managing human-centric pervasive services

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