Ben Baumer

Maximizing network lifetime on the line with adjustable sensing ranges

Given n sensors on a line, each of which is equipped with a unit battery charge and an adjustable sensing radius, what schedule will maximize the lifetime of a network that covers the entire line? Trivially, any reasonable algorithm is at least a

Set It and Forget It: Approximating the Set Once Strip Cover Problem

\frac{1}{2}

Changing of the guards: strip cover with duty cycling

-approximation, but we prove tighter bounds for several natural algorithms. We focus on developing a linear time algorithm that maximizes the expected lifetime under a random uniform model of sensor distribution. We demonstrate one such algorithm that achieves an average-case approximation ratio of almost 0.9. Most of the algorithms that we consider come from a family based on RoundRobin coverage, in which sensors take turns covering predefined areas until their battery runs out.

As Strong as the Weakest Link: Mining Diverse Cliques in Weighted Graphs

We consider the Set Once Strip Cover problem, in which n wireless sensors are deployed over a one-dimensional region. Each sensor has a fixed battery that drains in inverse proportion to a radius that can be set just once, but activated at any time. The problem is to find an assignment of radii and activation times that maximizes the length of time during which the entire region is covered. We show that this problem is NP-hard. In addition, we show that RoundRobin, the algorithm in which the sensors take turns covering the entire region, has a tight approximation guarantee of

Brief announcement: set it and forget it - approximating the set once strip cover problem

\frac{3}{2}

Star Search: Effective Subgroups in Collaborative Social Networks

32. This result also applies to the more general Strip Cover problem, in which each radius may be set finitely-many times. Moreover, we show that the more general class of duty cycle algorithms, in which groups of sensors take turns covering the entire region, can do no better. Finally, we give an

Social-Communication Composite Networks

O(n^2 \log {n})