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Register a new userSocial Network Enhanced Device-to-Device Communication Underlaying Cellular Networks
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Yanru Zhang
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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Device-to-device (D2D) communication has seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, we propose a social-aware approach for optimizing D2D communication by exploiting two layers: the social network and the physical wireless layers. First we formulate the physical layer D2D network according to users encounter histories. Subsequently, we propose an approach, based on the so-called Indian Buffet Process, so as to model the distribution of contents in users online social networks. Given the social relations collected by the Evolved Node B (eNB), we jointly optimize the traffic offloading process in D2D communication. In addition, we give the Chernoff bound and approximated cumulative distribution function (CDF) of the offloaded traffic. In the simulation, we proved the effectiveness of the bound and CDF. The numerical results based on real traces show that the proposed approach offload the traffic of eNBs successfully.
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Device-to-device (D2D) communications is seen as a major technology to overcome the imminent wireless capacity crunch and to enable novel application services. In this paper, we propose a novel, social-aware approach for optimizing D2D communications by exploiting two network layers: the social network and the physical, wireless network. First we formulate the physical layer D2D network according to users encounter histories. Subsequently, we propose a novel approach, based on the so-called Indian Buffet Process, so as to model the distribution of contents in users online social networks. Given the online and offline social relations collected by the Evolved Node B, we jointly optimize the traffic offload process in D2D communication. Simulation results show that the proposed approach offload the traffic of Evolved Node B successfully.
A constant need to increase the network capacity for meeting the growing demands of the subscribers has led to the evolution of cellular communication networks from the first generation (1G) to the fifth generation (5G). There will be billions of connected devices in the near future. Such a large number of connections are expected to be heterogeneous in nature, demanding higher data rates, lesser delays, enhanced system capacity and superior throughput. The available spectrum resources are limited and need to be flexibly used by the mobile network operators (MNOs) to cope with the rising demands. An emerging facilitator of the upcoming high data rate demanding next generation networks (NGNs) is device-to-device (D2D) communication. An extensive survey on device-to-device (D2D) communication has been presented in this paper, including the plus points it offers, the key open issues associated with it like peer discovery, resource allocation etc, demanding special attention of the research community, some of its integrant technologies like millimeter wave D2D (mmWave), ultra dense networks (UDNs), cognitive D2D, handover procedure in D2D and its numerous use cases. Architecture is suggested aiming to fulfill all the subscriber demands in an optimal manner. The Appendix mentions some ongoing standardization activities and research projects of D2D communication.
In this paper, we develop a comprehensive analytical framework for cache enabled cellular networks overlaid with coordinated device-to-device (D2D) communication. We follow an approach similar to LTE Direct, where the base station (BS) is responsible for establishing D2D links. We consider that an arbitrary requesting user is offloaded to D2D mode to communicate with one of its k closest D2D helpers within the macrocell subject to content availability and helper selection scheme. We consider two different D2D helper selection schemes: 1) uniform selection (US), where the D2D helper is selected uniformly and 2) nearest selection (NS), where the nearest helper possessing the content is selected. Employing tools from stochastic geometry, we model the locations of BSs and D2D helpers using independent homogeneous Poisson point processes (HPPPs). We characterize the D2D mode probability of an arbitrary user for both the NS and US schemes. The distribution of the distance between an arbitrary user and its ith neighboring D2D helper within the macrocell is derived using disk approximation for the Voronoi cell, which is shown to be reasonably accurate. We fully characterize the overall coverage probability and the average ergodic rate of an arbitrary user requesting a particular content. We show that significant performance gains can be achieved compared to conventional cellular communication under both the NS and US schemes when popular contents are requested and NS scheme always outperforms the US scheme. Our analysis reveals an interesting trade off between the performance metrics and the number of candidate D2D helpers k. We conclude that enhancing D2D opportunities for the users does not always result in better performance and the network parameters have to be carefully tuned to harness maximum gains.
Considering the energy-efficient emergency response, subject to a given set of constraints on emergency communication networks (ECN), this article proposes a hybrid device-to-device (D2D) and device-to-vehicle (D2V) network for collecting and transmitting emergency information. First, we establish the D2D network from the perspective of complex networks by jointly determining the optimal network partition (ONP) and the temporary data caching centers (TDCC), and thus emergency data can be forwarded and cached in TDCCs. Second, based on the distribution of TDCCs, the D2V network is established by unmanned aerial vehicles (UAV)-based waypoint and motion planning, which saves the time for wireless transmission and aerial moving. Finally, the amount of time for emergency response and the total energy consumption are simultaneously minimized by a multiobjective evolutionary algorithm based on decomposition (MOEA/D), subject to a given set of minimum signal-to-interference-plus-noise ratio (SINR), number of UAVs, transmit power, and energy constraints. Simulation results show that the proposed method significantly improves response efficiency and reasonably controls the energy, thus overcoming limitations of existing ECNs. Therefore, this network effectively solves the key problem in the rescue system and makes great contributions to post-disaster decision-making.
The growing popularity of Internet-of-Things (IoT) has created the need for network-based traffic anomaly detection systems that could identify misbehaving devices. In this work, we propose a lightweight technique, IoT-guard, for identifying malicious traffic flows. IoT-guard uses semi-supervised learning to distinguish between malicious and benign device behaviours using the network traffic generated by devices. In order to achieve this, we extracted 39 features from network logs and discard any features containing redundant information. After feature selection, fuzzy C-Mean (FCM) algorithm was trained to obtain clusters discriminating benign traffic from malicious traffic. We studied the feature scores in these clusters and use this information to predict the type of new traffic flows. IoT-guard was evaluated using a real-world testbed with more than 30 devices. The results show that IoTguard achieves high accuracy (98%), in differentiating various types of malicious and benign traffic, with low false positive rates. Furthermore, it has low resource footprint and can operate on OpenWRT enabled access points and COTS computing boards.
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