Offloading Content with Self-organizing Mobile Fogs

Mobile users in an urban environment access content on the internet from different locations. It is challenging for the current service providers to cope with the increasing content demand from a large number of collocated mobile users. In-network caching to offload content at nodes closer to users alleviate the issue, though efficient cache management is required to find out who should cache what, when and where in an urban environment, given nodes limited computing, communication and caching resources. To address this, we first define a novel relation between content popularity and availability in the network and investigate a node's eligibility to cache content based on its urban reachability. We then allow nodes to self-organize into mobile fogs to increase the distributed cache and maximize content availability in a cost-effective manner. However, to cater rational nodes, we propose a coalition game for the nodes to offer a maximum "virtual cache" assuming a monetary reward is paid to them by the service/content provider. Nodes are allowed to merge into different spatio-temporal coalitions in order to increase the distributed cache size at the network edge. Results obtained through simulations using realistic urban mobility trace validate the performance of our caching system showing a ratio of 60-85% of cache hits compared to the 30-40% obtained by the existing schemes and 10% in case of no coalition

CODIE: Controlled Data and Interest Evaluation in Vehicular Named Data Networks

[EN] Recently, named data networking (NDN) has been proposed as a promising architecture for future Internet technologies. NDN is an extension to the content-centric network (CCN) and is expected to support various applications in vehicular communications [ vehicular NDN (VNDN)]. VNDN basically relies on naming the content rather than using end-to-end device names. In VNDN, a vehicle broadcasts an "Interest" packet for the required "content," regardless of end-to-end connectivity with servers or other vehicles and known as a "consumer." In response, a vehicle with the content replies to the Interest packet with a "Data" packet and named as a "provider." However, the simple VNDN architecture faces several challenges such as consumer/provider mobility and Interest/Data packet(s) forwarding. In VNDN, for the most part, the Data packet is sent along the reverse path of the related Interest packet. However, there is no extensive simulated reference available in the literature to support this argument. In this paper, therefore, we first analyze the propagation behavior of Interest and Data packets in the vehicular ad hoc network (VANET) environment through extensive simulations. Second, we propose the "CODIE" scheme to control the Data flooding/broadcast storm in the naive VNDN. The main idea is to allow the consumer vehicle to start hop counter in Interest packet. Upon receiving this Interest by any potential provider, a data dissemination limit (DDL) value stores the number of hops and a data packet needs to travel back. Simulation results show that CODIE forwards fewer copies of data packets processed (CDPP) while achieving similar interest satisfaction rate (ISR), as compared with the naive VNDN. In addition, we also found that CODIE also minimizes the overall interest satisfaction delay (ISD), respectively.This work was supported by the Ministry of Science, ICT and Future Planning, South Korea, under Grant IITP-2015-H8601-15-1002 of the Convergence Information Technology Research Center supervised by the Institute for Information and Communications Technology Promotion. The review of this paper was coordinated by Editors of CVS. (Corresponding author: Dongkyun Kim.)Ahmed, SH.; Bouk, SH.; Yaqub, MA.; Kim, D.; Song, H.; Lloret, J. (2016). CODIE: Controlled Data and Interest Evaluation in Vehicular Named Data Networks. IEEE Transactions on Vehicular Technology. 65(6):3954-3963. https://doi.org/10.1109/TVT.2016.2558650S3954396365

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig