Node (networking)

Abstract: This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks Wireless sensor networks use battery-operated computing and sensing devices A network of these devices will collaborate for a common application such as environmental monitoring We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 80211 in almost every way: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important S-MAC uses three novel techniques to reduce energy consumption and support self-configuration To reduce energy consumption in listening to an idle channel, nodes periodically sleep Neighboring nodes form virtual clusters to auto-synchronize on sleep schedules Inspired by PAMAS, S-MAC also sets the radio to sleep during transmissions of other nodes Unlike PAMAS, it only uses in-channel signaling Finally, S-MAC applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network We evaluate our implementation of S-MAC over a sample sensor node, the Mote, developed at University of California, Berkeley The experiment results show that, on a source node, an 80211-like MAC consumes 2-6 times more energy than S-MAC for traffic load with messages sent every 1-10 s

Abstract: This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online\footnote{\url{https://github.com/tangjianpku/LINE}}.

Abstract: This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.

Abstract: Sensor webs consisting of nodes with limited battery power and wireless communications are deployed to collect useful information from the field. Gathering sensed information in an energy efficient manner is critical to operate the sensor network for a long period of time. In W. Heinzelman et al. (Proc. Hawaii Conf. on System Sci., 2000), a data collection problem is defined where, in a round of communication, each sensor node has a packet to be sent to the distant base station. If each node transmits its sensed data directly to the base station then it will deplete its power quickly. The LEACH protocol presented by W. Heinzelman et al. is an elegant solution where clusters are formed to fuse data before transmitting to the base station. By randomizing the cluster heads chosen to transmit to the base station, LEACH achieves a factor of 8 improvement compared to direct transmissions, as measured in terms of when nodes die. In this paper, we propose PEGASIS (power-efficient gathering in sensor information systems), a near optimal chain-based protocol that is an improvement over LEACH. In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. Simulation results show that PEGASIS performs better than LEACH by about 100 to 300% when 1%, 20%, 50%, and 100% of nodes die for different network sizes and topologies.