Peer-to-peer (P2P) networks aggregate enormous storage and processing resources while minimizing entry and scaling costs. Gnutella-like P2P networks are complex heterogeneous networks, in which the underlying overlay topology has a power-law node degree distribution. While scale-free networks have great robustness against random failures, they are vulnerable to deliberate attacks where highly connected nodes are eliminated. Since high degree nodes play an important role in maintaining the connectivity, this paper presents an algorithm based on random walks to locate high degree nodes in P2P networks. Simulations demonstrate that the algorithm performs well in various scenarios and that heterogeneous P2P networks are very sensitive to deliberate attacks.

Traffic forecasting has been an active research field in recent decades, and with the development of deep-learning technologies, researchers are trying to utilize deep learning to achieve tremendous improvements in traffic forecasting, as it has been seen in other research areas, such as speech recognition and image classification. In this study, we summarize recent works in which deep-learning methods were applied for geospatial data-based traffic forecasting problems. Based on the insights from previous works, we further propose a deep-learning framework, which transforms geospatial data to images, and then utilizes the state-of-the-art deep-learning methodologies such as Convolutional Neural Network (CNN) and residual networks. To demonstrate the simplicity and effectiveness of our framework, we present a formulation of the New York taxi pick-up/drop-off forecasting problem, and show that our framework significantly outperforms traditional methods, including Historical Average (HA) and AutoRegressive Integrated Moving Average (ARIMA).

Vehicular Delay Tolerant Networks (DTN) use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness (DAWN). DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes’ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.

This paper presents an in-depth analysis of the interference strength and required guardband width between coexistent users for distributed orthogonal frequency division multiple access (OFDMA). In dynamic spectrum access networks, the cross-band interference between spectrally adjacent users is considered harmful with frequency guardbands inserted between spectrum blocks to eliminate the interference. However, the strength of the cross-band interference depends heavily on the user heterogeneity in different OFDM configurations. The cross-band interference due to the three user heterogeneity artifacts of power heterogeneity, sampling rate heterogeneity, and symbol length heterogeneity is investigated to determine the required guardband width. Analytical and simulation results show that the greater user heterogeneity requires larger guardbands with the sampling rate heterogeneity having the greatest effect. These results can be used to assist the design of spectrum allocation strategies.