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

Broadcast scheduling with data bundles

Broadcast scheduling has been extensively studied in wireless environments, where a base station broadcasts data to multiple users. Due to the sole wireless channel's limited bandwidth, only a subset of the needs may be satisfiable, and so maximizing total (weighted) throughput is a popular objective. In many realistic applications, however, data are dependent or correlated in the sense that the joint utility of a set of items is not simply the sum of their individual utilities. On the one hand, substitute data may provide overlapping information, so one piece of data item may have lower value if a second data item has already been delivered; on the other hand, complementary data are more valuable than the sum of their parts, if, for example, one data item is only useful in the presence of a second data item. In this paper, we define a data bundle to be a set of data items with possibly nonadditive joint utility, and we study a resulting broadcast scheduling optimization problem whose objective is to maximize the utility provided by the data delivered

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

Addressing the reactiveness problem in sensor networks using rich task representation

Sensor networks are increasingly important in many domains, for example, environmental monitoring, emergency response, and military operations. There is a great interest in making these networks more flexible, so they can be more easily deployed to meet the needs of new tasks. The research problem is lack of reactiveness of a system utilising a sensor network in a dynamic real-time domain, where the state of sensors and tasks might change many times (e.g. due to a sensor malfunction, or a change in task requirements or priorities). In such domains (e.g. firefighting or the military) we want to minimise the time spent manually configuring the sensor network, as any delay dramatically endangers the outcome of a task or a delay’s effects might be unacceptable, e.g. the loss of a human life. The current way of deploying sensors in the problem context involves four consecutive steps: Direction, Collection, Processing and Dissemination (DCPD). These steps form a cycle, called the DCPD loop. Automating this loop as much as possible would be a big step towards solving the reactiveness problem. Service-Oriented Sensor Networks (SOSN), allow sensors to be discovered, accessed, and combined with other information-processing services, thus enabling an efficient sensor exploitation. They are only a partial solution to the problem, as they don’t employ explicit representations of a user’s information-requiring tasks. Therefore, a machine processable expression of a user’s task (task representation, TR), allowing automation of the DCPD steps, is needed. We showed that, currently, there is no TR that can completely automate the loop, but that we can create such a hybrid of current TRs (called HTR) that automates the loop more than the individual TRs. Our literature review revealed four TRs. Using the identified TRs, we formed three high level designs of task representations. None of them covered the loop completely thus by enrichment of one of the built HTRs with the missing concepts, we finally obtained one that covers the DCPD loop fully. We tested the four hybrids in a simulation run for four scenarios with distinctive likelihoods of change of task and platform states. It showed that significant benefits are gained just by reusing existing technologies and that the reactiveness problem can be effectively tackled by that approach, particularly visible in the emergency response scenario, characterised by low task and high platform changeability.EThOS - Electronic Theses Online ServiceGBUnited Kingdo