No Arabic abstract
The mounting content sharing among users has resulted in a considerable rise in wireless data traffic, pressurizing the cellular networks to undergo a suitable upheaval. A competent technology of the fifth-generation networks (5G) for efficiently supporting proximity-based applications is Device-to-Device (D2D) communication, underlaying cellular networks. Significant advances have been made till date, for allocating resources to D2D users in cellular networks, such that sharing of spectral resources between cellular and D2D users is carried out in a coordinated manner. In this paper, a sector-based radio resource allocation (SBRRA) algorithm for resource block allocation to D2D pairs has been proposed, where the number of resource blocks (RBs) is allocated to each D2D pair in an adaptive manner, based on the demanded application by each pair. Different applications demand a varying number of RBs, in accordance with their priority. This algorithm focusses on the use of sectored antennas at the base station, for a better performance and low complexity. Extensive simulations are carried out, considering real-time scenario, for ensuring satisfactory Quality of Service (QoS) and Quality of Experience (QoE) by the users. The efficiency of the proposed scheme is proved by comparing it with the RB allocation using Hidden Markov Model (HMM).
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.
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.
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.
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.
We provide a novel solution for Resource Discovery (RD) in mobile device clouds consisting of selfish nodes. Mobile device clouds (MDCs) refer to cooperative arrangement of communication-capable devices formed with resource-sharing goal in mind. Our work is motivated by the observation that with ever-growing applications of MDCs, it is essential to quickly locate resources offered in such clouds, where the resources could be content, computing resources, or communication resources. The current approaches for RD can be categorized into two models: decentralized model, where RD is handled by each node individually; and centralized model, where RD is assisted by centralized entities like cellular network. However, we propose LORD, a Leader-based framewOrk for RD in MDCs which is not only self-organized and not prone to having a single point of failure like the centralized model, but also is able to balance the energy consumption among MDC participants better than the decentralized model. Moreover, we provide a credit-based incentive to motivate participation of selfish nodes in the leader selection process, and present the first energy-aware leader selection mechanism for credit-based models. The simulation results demonstrate that LORD balances energy consumption among nodes and prolongs overall network lifetime compared to decentralized model.