By Dominic Meier, Yvonne Anne Pignolet (auth.), Bhaskar Krishnamachari, Subhash Suri, Wendi Heinzelman, Urbashi Mitra (eds.)
The publication constitutes the refereed court cases of the 5th overseas convention on dispensed Computing in Sensor structures, DCOSS 2009, held in Marina del Rey, CA, united states, in June 2009.
The 26 revised complete papers awarded have been rigorously reviewed and chosen from 116 submissions. The examine contributions during this court cases span many points of sensor structures, together with strength effective mechanisms, monitoring and surveillance, task acceptance, simulation, question optimization, community coding, localization, program improvement, facts and code dissemination.
Read or Download Distributed Computing in Sensor Systems: 5th IEEE International Conference, DCOSS 2009, Marina del Rey, CA, USA, June 8-10, 2009. Proceedings PDF
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Extra resources for Distributed Computing in Sensor Systems: 5th IEEE International Conference, DCOSS 2009, Marina del Rey, CA, USA, June 8-10, 2009. Proceedings
Since y is a neighbor, either the y is also violating the bound (and thus must be constrained by a neighbor of its own at an earlier time), or else the execution at which the constraint is applied happens precisely once, at the time tx during the period (tf , tf + Δt ]. Assume the latter case (the former reduces to it by switching which device is under consideration). Because y constrains x at tx , y must not have been rising in its previous execution—otherwise the diﬀerence between y and x must have shrunk, meaning x is not constrained by y, or stayed the same, meaning x was rising also and will still not be constrained by y.
We extract the kernel of this problem as follows. Given are collections of sensors and tasks. Each task is to monitor and detect events, if they occur, in a certain location. The utility of a sensor to a task is the detection probability when the event occurs. Let Si → Tj indicate that sensor i is assigned to task j and let pj indicates Tj ’s profit. The objective function is then to maximize the sum of cumulative detection probabilities for tasks (weighted by task profits), given the probability eij that a single sensor Si detects an event for Tj : pj (1 − (1 − eij )) (1) j Si →Tj We call this the Cumulative Detection Probability maximization problem (M AX CDP).
This is determined as follows: if the utility sensor Si provides to the incoming task Tj weighted by Tj ’s profit pj is greater than that of the current task Tk then Si should be reassigned. More formally, if eij pj > eik pk then Si is reassigned. We allow both localization tasks and detection tasks to preempt detection tasks; neither type can preempt a localization task. To reduce both the interruption of ongoing tasks and the communication overhead, no cascading preemption is allowed. That is, if task Tj preempts task Tk , Tk will try to satisfy its demand only with available sensors rather than by preempting a third task.