Performance of Caching in Wireless Small Cell Networks

In this paper, a fifth generation (5G) radio cellular system performance will be discussed based on a new architecture developed using small base stations (SBSs). This strategy takes into account SBS caching capability to alleviate the backhaul load and consequently satisfy users’ requests. Therefore, the effectiveness of future 5G networks will be maximized by offering good coverage with low latency. This is a new caching paradigm called proactive caching, which could be useful for the implementation in big data. Significant gains in reducing traffic on backhaul links and user satisfaction will be ensured. Customers are served by picking the content from local caches, stochastically distributed over the plane, as a formerly limited backhaul. Success probability expressions are obtained as a function of the signal-to-interference and noiseratio (SINR) and SBS density

The Effects of Mobility on the Hit Performance of Cached D2D Networks

International audienceA device-to-device (D2D) wireless network is considered , where user devices also have the ability to cache content. In such networks, users are mobile and communication links can be spontaneously activated and dropped depending on the users' relative position. Receivers request files from transmitters, these files having a certain popularity and file-size distribution. In this work a new performance metric is introduced, namely the Service Success Probability, which captures the specificities of D2D networks. For the Poisson Point Process case for node distribution and the SNR coverage model, explicit expressions are derived. Simulations support the analytical results and explain the influence of mobility and file-size distribution on the system performance, while providing intuition on how to appropriately cache content on mobile storage space. Of particular interest is the investigation on how different file-size distributions (Exponential , Uniform, or Heavy-Tailed) influence the performance

Age of Information minimization in UAV-aided data collection for WSN and IoT applications: A systematic review

The use of unmanned aerial vehicles (UAVs) for data gathering in wireless sensor networks (WSNs) and Internet of Things (IoT) applications has significantly gained interest in recent years. This shift is mainly attributed to the fast mobility and manoeuvrability of UAVs to deliver sensed data (by sensors and/or IoT devices) in remote, rural, or urban areas to the required destination (such as base stations or data centers). Age of Information (AoI) is a recent metric that measures the degree of freshness of information collected in data gathering applications, and in this context, data sensed by terrestrial sensing devices and transported by the UAV. Many researchers have thus focused on techniques to minimize AoI, mainly using machine learning and optimization methods. The research in this area is fast-growing, and some of the models studied are becoming more fine-grained, thus increasing the need for a comprehensive review of the proposed solutions, peculiar aspects, and diverse assumptions to draw lessons, identify challenges, and map out future considerations. This motivates the comprehensive study conducted in this paper, whereby 45 articles were meticulously selected from the Scopus database and systematically filtered to the 20 most relevant articles on AoI minimization for UAV-assisted data gathering in WSN and IoT applications. This paper provides a comprehensive review of problems and problem-solving solutions, detailed assumptions, algorithms, constraints, joint optimization objectives, metrics, and influencing factors on information freshness in UAV-assisted WSN/IoT. Optimal design of UAV trajectory, efficient UAV and sensor node/IoT device scheduling, and improved UAV energy source acquisition were also identified as some of the most preponderant themes surrounding AoI minimization. Consequently, a comprehensive discussion on AoI-optimal trajectory designs in different WSN/IoT architectural setups, namely clustered and non-clustered environments, has been presented. The outcomes of this systematic review include the categorization of the issues and proposed solutions, as well as tools and methods, joint optimization approaches, metrics, and UAV/SN-related assumptions from the reviewed articles. Finally, lessons learned, design considerations, challenges, and future directions have also been discussed

A Survey on the Design Aspects and Opportunities in Age-Aware UAV-Aided Data Collection for Sensor Networks and Internet of Things Applications

Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things (IoT) networks periodically transmit their sensed data to UAVs to be relayed to the base station (BS). UAVs have been widely deployed in time-sensitive or real-time applications, such as in disaster areas, due to their ability to transmit data to the destination within a very short time. However, timely delivery of information by UAVs in WSN/IoT networks can be very complex due to various technical challenges, such as flight and trajectory control, as well as considerations of the scheduling of UAVs and SNs. Recently, the Age of Information (AoI), a metric used to measure the degree of freshness of information collected in data-gathering applications, has gained much attention. Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. In this paper, we conduct a systematic literature review (SLR) to study past literature on age minimization in UAV-assisted data-gathering architecture to determine the most important design components. Three crucial design aspects in AoI minimization were discovered from analyzing the 26 selected articles, which focused on energy management, flight trajectory, and UAV/SN scheduling. We also investigate important issues related to these identified design aspects, for example, factors influencing energy management, including the number of visited sensors, energy levels, UAV cooperation, flight time, velocity control, and charging optimization. Issues related to flight trajectory and sensor node scheduling are also discussed. In addition, future considerations on problems such as traffic prioritization, packet delivery errors, system optimization, UAV-to-sensor node association, and physical impairments are also identified