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Given an IP address, understanding its initial assignment,and predicting its potential usage can significantly help toward improving efficiency of many practical IP-based applications, such as Ad-recommendation, point of interest selection, fraud detection, and Internet service optimization. However, there are only a few prior studies conducted on predicting IP usage from its assignment. Surprisingly, there does not exist a benchmark dataset available to academia that can be used to investigate and develop IP usage predictions for different IP applications. In this work, we formulate the IP usage prediction problem; specifically, we collect large-scale, real-world data and extract salient features using sophisticated networking tools such as different network signals, trace route delay, IP block usage, and geographical landmarks. We showcase a series of tabular information retrieval methods to learn network signals and their interactions, and identify the IP usage scenarios. We believe our datasets and algorithms can benefit the community to facilitate relevant research in this domain, yielding more efficient and effective solutions to multiple categories of applications. [ABSTRACT FROM AUTHOR]

Utilizing device-to-device (D2D) connections among mobile devices is promising to meet the increasing throughput demand over cellular links. In particular, when mobile devices are in close proximity of each other and are interested in the same content, D2D connections such as Wi-Fi Direct can be opportunistically used to construct a cooperative (and jointly operating) cellular and D2D networking system. However, it is crucial to understand, quantify, and exploit the potential of network coding for cooperating mobile devices in the joint cellular and D2D setup. In this paper, we consider this problem, and: 1) develop a network coding framework, namely NCMI, for cooperative mobile devices in the joint cellular and D2D setup, where cellular and D2D link capacities are the same; and 2) characterize the performance of the proposed network coding framework, where we use packet completion time, which is the number of transmission slots to recover all packets, as a performance metric. We demonstrate the benefits of our network coding framework through simulations. [ABSTRACT FROM AUTHOR]

Reduced execution time and increased power efficiency are important objectives in the distributed execution of collaborative signal processing tasks over wireless sensor networks (WSNs). Meanwhile, Fourier transforms are among the most widely used frequency analysis tools in WSNs for studying the behavior of sensed phenomena. Several energy-efficient in-network Fourier transform computation algorithms have been proposed for WSNs. Most of these works assume that the sensors are equally spaced over a one-dimensional (1D) region. However, in practice, the sensors are usually randomly distributed over a two-dimensional (2D) plane. Consequently, the conventional 2D Fast Fourier Transform (FFT) designed for data sampled on a uniform grid is not applicable in such environments. We address this problem by designing a distributed hybrid structure consisting of local Non-equispaced Discrete Fourier Transform (NDFT) and global FFT computations. First, the NDFT method is applied within suitably selected clusters to obtain the initial uniform Fourier coefficients within allowable estimation error bounds. We investigate both classical linear and generalized interpolation methods for computing the NDFT coefficients within each cluster. Second, a separable 2D FFT is applied over all clusters using our proposed energy-efficient 1D FFT computation method, which reduces communication costs by employing a novel binary representation mapping strategy for data exchanges between sensors. The proposed techniques are implemented on the SIDnet-SWANS platform, and the tradeoffs between communication cost, execution time, and energy consumption are studied. [ABSTRACT FROM AUTHOR]

Consider a scenario in which a source broadcasts a common content to a group of cooperating mobile devices that are within proximity of each other. Devices in this group may receive only partial content from the source due to packet losses over wireless broadcast links and these packet losses may differ for different devices. The remaining content missing at each device can then be recovered, thanks to cooperation among the devices by exploiting device-to-device (D2D) connections. In this context, the minimum amount of time that guarantees a complete acquisition of the common content at every device is referred to as the “completion time”. It has been shown that instantly decodable network coding (IDNC) reduces the completion time as compared with no network coding in this scenario. However, for applications such as video streaming, not all packets have the same importance and not all devices are interested in the same quality of content. This problem becomes more interesting and challenging when additional, but realistic constraints, such as strict deadline, bandwidth, or limited energy are added in the problem formulation. We assert that direct application of IDNC in such a scenario yields poor performance in terms of content quality and completion time. In this paper, we propose a novel Content- and Loss-Aware IDNC scheme that improves content quality and network coding opportunities jointly by taking into account the contribution of each packet to the desired quality of service (QoS) as well as the channel losses over D2D links. Our proposed Content- and Loss-Aware IDNC (i) maximizes the quality under the completion time constraint, and (ii) minimizes the completion time under the quality constraint. We demonstrate the benefits of Content- and Loss-Aware IDNC through simulations. [ABSTRACT FROM AUTHOR]