Abstract-A wide range of applications for wireless ad hoc networks are time-critical and impose stringent requirement on the communication latency. This paper studies the problem Minimum-Latency Broadcast Scheduling (MLBS) in wireless ad hoc networks represented by unit-disk graphs. This problem is NP-hard. A trivial lower bound on the minimum broadcast latency is the radius R of the network with respect to the source of the broadcast, which is the maximum distance of all the nodes from the source of the broadcast. The previously best-known approximation algorithm for MLBS produces a broadcast schedule with latency at most 648R. In this paper, we present three progressively improved approximation algorithms for MLBS. They produce broadcast schedules with latency at most 24R − 23, 16R − 15, and R + O (log R) respectively.

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To understand user behavior, researchers have examined intention to use, and system usage through some common conceptualizations such as actual usage, reported usage, and assessed usage. Although this entire body of research has produced important findings, it has yet to appreciably advance our theoretical understanding of behavioral intention (BI) and usage constructs. To fill this gap, this paper critically examines and compares these core variables as well as their relationships with key technology acceptance determinants. We find that (1) BI has a much higher correlation with the determinants than does usage, and thus more variance in BI than in usage can be explained; (2) BI is not a good surrogate for usage; (3) among the three usage constructs, assessed usage is the most and actual usage is the least highly correlated with BI; and (4) researchers should examine both actual usage and assessed usage in their every single study to bring to light the true relationships between system usage and its antecedents. This study thus helps IS scholars expand their baseline knowledge of these core variables, interpret the important messages conveyed by the extant literature, and conduct more fruitful and illuminating future research on user behavior.

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