Agent-based Sensor-Mission Assignment for Tasks Sharing Assets

(c) IFAAMASPeer reviewedPostprin

A Survey on Sensor Networks from a Multiagent Perspective

Sensor networks (SNs) have arisen as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical systems ease the development and proliferation of this kind of networks in a wide range of actual-world applications. Multiagent systems (MAS) have been identified as one of the most suitable technologies to contribute to the deployment of SNs that exhibit flexibility, robustness and autonomy. The purpose of this survey is 2-fold. On the one hand, we review the most relevant contributions of agent technologies to this emerging application domain. On the other hand, we identify the challenges that researchers must address to establish MAS as the key enabling technology for SNs.This work has been funded by projects IEA(TIN2006-15662-C02-01), Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010), EVE (TIN2009-14702-C02-01,TIN2009-14702-C02-02) and Generalitat de Catalunya under the gran t2009-SGR-1434. Meritxell Vinyals is supported by the Spanish Ministry of Education (FPU grant AP2006-04636)Peer Reviewe

Utility-based joint sensor selection and congestion control for task-oriented WSNs

Task-centric wireless sensor network environments are often characterized by the simultaneous operation of multiple tasks. Individual tasks compete for constrained resources and thus need resource mediation algorithms at two levels. First, different sensors must be allocated to different tasks based on the combination of sensor attributes and task requirements. Subsequently, sensor data rates on various data routes must be dynamically adapted to share the available wireless bandwidth, especially when links experience traffic congestion. In this paper we investigate heuristics for incrementally modifying the sensor-task matching process to incorporate changes in the transport capacity constraints or feasible task utility values

Flexible Resource Assignment in Sensor Networks : A Hybrid Reasoning Approach

Today, sensing resources are the most valuable assets of critical tasks (e.g., border monitoring). Although, there are various types of assets available, each with different capabilities, only a subset of these assets is useful for a specific task. This is due to the varying information needs of tasks. This gives rise to assigning useful assets to tasks such that the assets fully cover the information requirements of the individual tasks. The importance of this is amplified in the intelligence,surveillance, and reconnaissance (ISR) domain, especially in a coalition context. This is due to a variety of reasons such as the dynamic nature of the environment, scarcity of assets, high demand placed on available assets, sharing of assets among coalition parties, and so on. A significant amount of research been done by different communities to effciently assign assets to tasks and deliver information to the end user. However, there is little work done to infer sound alternative means to satisfy the information requirements of tasks so that the satisfiable tasks are increased. In this paper, we propose a hybrid reasoning approach (viz., a combination of rule-based and ontology-based reasoning) based on current Semantic Web technologies to infer assets types that are necessary and sufficient to satisfy the requirements of tasks in a flexible manner

Knowledge-Driven Agile Sensor-Mission Assignment

In this paper, we show how knowledge representation and reasoning techniques can support sensor-mission assignment, proceeding from a high-level specification of information requirements, to the allocation of assets such as sensors and platforms. In our previous work, we showed how assets can be matched to mission tasks by formalising the military missions and means framework in terms of an ontology, and using this ontology to drive a matchmaking process derived from the area of semantic Web services. The work reported here extends the earlier approach in two important ways: (1) by providing a richer and more realistic way for a user to specify their information requirements, and (2) by using the results of the semantic matchmaking process to define the search space for efficient asset allocation algorithms. We accomplish (1) by means of a rule-based representation of the NIIRS approach to relating sensed data to the tasks that data may support. We illustrate (2)by showing how the output of our matching process can drive a well-known efficient combinatorial auction algorithm (CASS). Finally, we summarise the status of our illustration-of-concept application, SAM (Sensor Assignment to Missions), and discuss various roles such an application can play in supporting sensormission assignment

Detection and Localization Sensor Assignment with Exact and Fuzzy Locations

Sensor networks introduce new resource allocation problems in which sensors need to be assigned to the tasks they best help. Such problems have been previously studied in simplified models in which utility from multiple sensors is assumed to combine additively. In this paper we study more complex utility models, focusing on two particular applications: event detection and target localization. We develop distributed algorithms to assign directional sensors of different types to multiple simultaneous tasks using exact location information. We extend our algorithms by introducing the concept of fuzzy location which may be desirable to reduce computational overhead and/or to preserve location privacy. We show that our schemes perform well using both exact or fuzzy location information

Instantaneous multi-sensor task allocation in static and dynamic environments

A sensor network often consists of a large number of sensing devices of different types. Upon deployment in the field, these sensing devices form an ad hoc network using wireless links or cables to communicate with each other. Sensor networks are increasingly used to support emergency responders in the field usually requiring many sensing tasks to be supported at the same time. By a sensing task we mean any job that requires some amount of sensing resources to be accomplished such as localizing persons in need of help or detecting an event. Tasks might share the usage of a sensor, but more often compete to exclusively control it because of the limited number of sensors and overlapping needs with other tasks. Sensors are in fact scarce and in high demand. In such cases, it might not be possible to satisfy the requirements of all tasks using available sensors. Therefore, the fundamental question to answer is: “Which sensor should be allocated to which task?", which summarizes the Multi-Sensor Task Allocation (MSTA) problem. We focus on a particular MSTA instance where the environment does not provide enough information to plan for future allocations constraining us to perform instantaneous allocation. We look at this problem in both static setting, where all task requests from emergency responders arrive at once, and dynamic setting, where tasks arrive and depart over time. We provide novel solutions based on centralized and distributed approaches. We evaluate their performance using mainly simulations on randomly generated problem instances; moreover, for the dynamic setting, we consider also feasibility of deploying part of the distributed allocation system on user mobile devices. Our solutions scale well with different number of task requests and manage to improve the utility of the network, prioritizing the most important tasks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Instantaneous multi-sensor task allocation in static and dynamic environments

A sensor network often consists of a large number of sensing devices of different types. Upon deployment in the field, these sensing devices form an ad hoc network using wireless links or cables to communicate with each other. Sensor networks are increasingly used to support emergency responders in the field usually requiring many sensing tasks to be supported at the same time. By a sensing task we mean any job that requires some amount of sensing resources to be accomplished such as localizing persons in need of help or detecting an event. Tasks might share the usage of a sensor, but more often compete to exclusively control it because of the limited number of sensors and overlapping needs with other tasks. Sensors are in fact scarce and in high demand. In such cases, it might not be possible to satisfy the requirements of all tasks using available sensors. Therefore, the fundamental question to answer is: “Which sensor should be allocated to which task?", which summarizes the Multi-Sensor Task Allocation (MSTA) problem. We focus on a particular MSTA instance where the environment does not provide enough information to plan for future allocations constraining us to perform instantaneous allocation. We look at this problem in both static setting, where all task requests from emergency responders arrive at once, and dynamic setting, where tasks arrive and depart over time. We provide novel solutions based on centralized and distributed approaches. We evaluate their performance using mainly simulations on randomly generated problem instances; moreover, for the dynamic setting, we consider also feasibility of deploying part of the distributed allocation system on user mobile devices. Our solutions scale well with different number of task requests and manage to improve the utility of the network, prioritizing the most important tasks

Frugal Sensor Assignment

When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensor-assignment problems motivated by frugality, i.e., the conservation of resources, for both static and dynamic settings. In general, the problems we study are NP-hard even to approximate, and so we focus on heuristic algorithms that perform well in practice. In the static setting, we propose a greedy centralized solution and a more sophisticated solution that uses the Generalized Assignment Problem model and can be implemented in a distributed fashion. In the dynamic setting, we give heuristic algorithms in which available sensors propose to nearby missions as they arrive. We find that the overall performance can be significantly improved if available sensors sometimes refuse to offer utility to missions they could help based on the value of the mission, the sensor’s remaining energy, and (if known) the remaining target lifetime of the network. Finally, we evaluate our solutions through simulations