Architectures for Heterogeneous Resource Allocation

Different modular approaches and architectures have been proposed for allocating het- erogeneous resource bundles to different tasks. In this section we present how many of these split the allocation process in two parts, by first considering the type(s) of resources required (i.e. qualitative), and then the actual resource instances matching the type(s) required (i.e. quantitative). We highlight similarities and differences with the architecture in Chapter 5, which in general adopts the same approach to divide the allocation process in qualitative-based matching, through explicit representation of sensing capabilties and task requirements, followed by quantitative-based matching

using the features of sensor instances (e.g. distance or energy remained).

In [81], the authors propose an agent-based architecture for dynamic formation of vir- tual organizations, which are composed of a number of individuals, departments or organizations each having a range of capabilities and resources at their disposal. This problem is related to our dynamic MSTA problem in heterogeneous environments, where also each sensor has a different range of capabilities. The mechanism delegates each task request to a Requirements Agent (RA), similar to the mission leader concept in [106], that then has the role to form the virtual organization by choosing the best Service Providers (SP), i.e. sensors in our case. The RA uses a Yellow Pages (YP) agent to match the type of service provided by each SP, e.g. movies or text messaging; it then sends a request for proposals to this subset of SPs, which then answer with the actual cost of the service provided (e.g. £25 per month). The SPs could decide also to partner together and send a joint proposal. The RA then decides which one is the best bundle of SPs to accept. This is similar to the problem and conceptual architec- ture proposed in Chapter 5, in fact we also use a knowledge-based component in order to find which type of sensor can potentially match the requirements of a certain task type. Differently although, given our distributed sensor network environment, we do not assume a central yellow pages agent like the authors do, and instead deploy this component in each of the users’ mobile devices. In the already cited [77], the archi- tecture proposed is essentially centralized and considers also a “yellow pages” central component similar to [81].

Closer to our approach, [32] proposes an ontology-based mechanisms for coalition formation in heterogeneous multi-robot systems. Their aim is to solve the MT-MR-IA problem for a multi-robot system for the USAR (Urban Search and Rescue) scenario [64]. The scheme they propose is similar to our conceptual architecture, and it is composed of three main components: an ontology-based reasoner, a search for “useful coalitions” and a coalition instantiation algorithm. The first is implemented using the Pellet reasoner which uses the task and robot ontology in [19] to find which kinds of

2.3 Multi Sensor Task Allocation & Taxonomy 52

robots are able to accomplish a certain task type. Then, given its recommendations, the mechanism searches for useful coalitions of robots which could potentially satisfy each tasks requirements. Finally, the coalition instantiation algorithm finds a feasible allocation of robot coalitions to tasks, extending the algorithm in [85]. Although this algorithm could be implemented in a distributed fashion, the authors in [32] propose an essentially centralized architecture. In addition, they do not explore the dynamic setting considering, instead, a static set of tasks on the field. Their approach confirms that in heterogeneous environments it is convenient to split the allocation process in at least two steps: one based on capability matching followed by one based on physical features of the robots in the field.

Similarly to [32], in one of our previous works [44], we investigated how to integrate an ontology-based reasoning component with an allocation algorithm supporting sensing tasks in a heterogeneous sensor network. We described a preliminary version of the architecture proposed in Chapter 5, discussing how the components might be integrated and work together. As in the different architectures surveyed above, we distinguish mainly two operations: first, sensor-task fitting based on capabilities provided and required, second, the actual allocation of sensor instances to tasks. Our distributed architecture extends the ontology-based reasoner with a bundle generator component which given the recommendation of the reasoner looks for feasible sensor coalitions, this idea is similar to the “search for useful coalitions” step in [32]. Note that differently from our distributed mechanism in Chapter 5, the architecture in [44] does not offer the possibility of using different utility models (e.g. non-additive) to evaluate different sensor bundle utilities, in general considering only individual sensor utility offers to tasks. From this point of view, the architecture discussed in Chapter 5 can be seen as an evolution of the one proposed in [44].

Finally, [114] is closely related to the architecture proposed in this thesis for the dy- namic settings. The authors consider both the ST-MS-IA-HE and MT-MS-IA-HE prob- lems for which they suggest a distributed agent-based approach. They develop an

extension of the semantic reasoner in [44] and integrate that with an extended distrib- uted version of our MRGAP algorithm, presented in Chapter 3. The extended reasoner is able to consider links among different tasks while instead the allocation algorithm considers both budget and threshold constraints being an extension of the MRGAP algorithm. The downside of this allocation system is that it considers only individual sensor utility offers, and combines those additively for sensors contributing to the same task. The distributed mechanism which we propose for the dynamic MSTA problem, instead, allows for both linear and non-linear utilities, although being limited to Single- Task sensors and not considering budget and threshold on demand constraints (which we instead consider only in the static setting in Chapter 3).