Abstract-We study network optimization that considers energy minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of processing required for its carried traffic. Most existing research on speed scaling focuses on a single network element in isolation. We aim for a network-wide optimization.Specifically, we study a routing problem with the objective of provisioning guaranteed speed/bandwidth for a given demand matrix while minimizing energy consumption. Optimizing the routes critically relies on the characteristic of the energy curve f (s), which is how energy is consumed as a function of the processing speed s. If f is superadditive, we show that there is no bounded approximation in general for integral routing, i.e., each traffic demand follows a single path. This contrasts with the well-known logarithmic approximation for subadditive functions. However, for common energy curves such as polynomials f (s) = µs α , we are able to show a constant approximation via a simple scheme of randomized rounding.The scenario is quite different when a non-zero startup cost σ

We study network optimization that considers power minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of processing required for its carried traffic. Most existing research on speed scaling focuses on a single network element in isolation. We aim for a network-wide optimization. Specifically, we study a routing problem with the objective of provisioning guaranteed speed/bandwidth for a given demand matrix while minimizing power consumption. Optimizing the routes critically relies on the characteristic of the speed-power curve , which is how power is consumed as a function of the processing speed . If is superadditive, we show that there is no bounded approximation in general for integral routing, i.e., each traffic demand follows a single path. This contrasts with the well-known logarithmic approximation for subadditive functions. However, for common speed-power curves such as polynomials , we are able to show a constant approximation via a simple scheme of randomized rounding. We also generalize this rounding approach to handle the case in which a nonzero startup cost appears in the speed-power curve, i.e., if if . We present an -approximation, and we discuss why coming up with an approximation ratio independent of the startup cost may be hard. Finally, we provide simulation results to validate our algorithmic approaches.

Flexible pulse sensors that can detect subtle skin surface deformation caused by arterial pulses are key components for developing non‐invasive continuous pulse waveform monitoring systems that provide vital health status parameters. Piezoelectric pulse sensors (PPSs) offer a promising solution for flexible pulse sensors due to their relatively high sensitivity and stability, and low power consumption, when compared with conventional active pulse sensors. However, the reported high‐performance PPSs contain toxic lead, which limits their practical applications. In this study, a highly sensitive and flexible PPS that detects surface deflections on the micrometer scale is fabricated with single‐crystalline group III‐nitride thin film. This biocompatible flexible PPS is sensitive enough to detect pulse waveform with detailed characteristic peaks from most arterial pulse sites when attached to the skin surface without applying external pressure. Useful physiological parameters such as the pulse rate, artery augmentation index, and pulse wave velocity can be drawn from the as‐acquired pulse waveforms. The flexible PPS can also be used to continuously monitor the arterial pulse waveform.

We investigate a natural combinatorial optimization problem called the Label Cut problem. Given an input graph G with a source s and a sink t, the edges of G are classified into different categories, represented by a set of labels. The labels may also have weights. We want to pick a subset of labels of minimum cardinality (or minimum total weight), such that the removal of all edges with these labels disconnects s and t. We give the first non-trivial approximation and hardness results for the Label Cut problem. Firstly, we present an O(√ m)-approximation algorithm for the Label Cut problem, where m is the number of edges in the input graph. Secondly, we show that it is NP-hard to approximate Label Cut within 2 log 1−1/ log log c n n for any constant c < 1/2, where n is the input length of the problem. Thirdly, our techniques can be applied to other previously considered optimization problems. In particular we show that the Minimum Label Path problem has the same approximation hardness as that of Label Cut, simultaneously improving and unifying two known hardness results for this problem which were previously the best (but incomparable due to different complexity assumptions).

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Abstract. We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as Ω(n −p ) for any fixed positive integer p. The improved PageRank algorithm is crucial for computing a quantitative ranking of edges in a given graph. We will use the edge ranking to examine two interrelated problems -graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algorithms using PageRank vectors to derive an improved partitioning algorithm.

The network traffic pattern of continuous sensor data collection often changes constantly over time due to the exploitation of temporal and spatial data correlations as well as the nature of condition-based monitoring applications. This paper develops a novel TDMA schedule that is capable of efficiently collecting sensor data for any network traffic pattern and is thus well suited to continuous data collection with dynamic traffic patterns. Following this schedule, the energy consumed by sensor nodes for any traffic pattern is very close to the minimum required by their workloads given in the traffic pattern. The schedule also allows the base station to conclude data collection as early as possible according to the traffic load, thereby reducing the latency of data collection. Experimental results using realworld data traces show that, compared with existing schedules that are targeted on a fixed traffic pattern, our proposed schedule significantly improves the energy efficiency and time efficiency of sensor data collection with dynamic traffic patterns. I. INTRODUCTIONEnergy efficiency and time efficiency are two major considerations for sensor data collection in wireless sensor networks. Energy efficiency concerns the amount of energy spent in data collection. Since sensor nodes are normally powered by batteries, it is critical to conserve energy as much as possible to extend the lifetime of a sensor network [1], [2]. Time efficiency, on the other hand, refers to the latency of collecting data from sensor nodes to a base station (or a sink node). Sensor data are often required to be quickly gathered after acquisition for timely processing [1].TDMA is a promising MAC protocol for efficient data collection in wireless sensor networks [3]- [5]. TDMA is contention-free and eliminates collisions by scheduling only non-interfering transmissions to proceed in the same time slot. It avoids the energy cost and latency overhead required by contention-based MAC protocols to compete for channel access and to perform retransmissions upon collisions. In addition, TDMA allows sensor nodes to turn their radios off whenever they are not transmitting or receiving, further conserving energy at sensor nodes.Most existing TDMA schedules are constructed for a static network traffic pattern in which a fixed set of nodes report data to the base station at each sampling interval [3]-[5]. In practice, however, continuous sensor data collection often exhibits dynamically changing network traffic patterns over time due to energy conservation concerns and the nature of monitoring applications. For example:• To save energy, temporal and spatial correlations among sensor measurements are usually exploited to reduce the

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