Probabilistic Congestion Control for Non-Adaptable Flows

In this paper we present a TCP-friendly congestion control scheme for non-adaptable flows. The main characteristic of these flows is that their data rate is determined by an application and cannot be adapted to the current congestion situation of the network. Typical examples of non-adaptable flows are those produced by networked computer games or live audio and video transmissions where adaption of the quality is not possible (e.g., since it is already at the lowest possible quality). We propose to perform congestion control for non-adaptable flows by suspending them at appropriate times so that the aggregation of multiple non-adaptable flows behaves in a TCP-friendly manner. The decision whether a flow is to be suspended is based on random experiments. In order to allocate probabilities for these experiments, the data rate of the non-adaptable flow is compared to the rate that a TCP flow would achieve under the same conditions. We present a detailed discussion of the proposed scheme and evaluate it through extensive simulations with the network simulator ns-2

Adaptive delayed channel access for IEEE 802.11n WLANs

Abstract— In this paper we investigate potential benefits that an adaptive delayed channel access algorithm can attain for the next-generation wireless LANs, the IEEE 802.11n. We show that the performance of frame aggregation introduced by the 802.11n adheres due to the priority mechanism of the legacy 802.11e EDCA scheduler, resulting in a poor overall performance. Because high priority flows have low channel utilization, the low priority flows throughputs can be amerced further. By introducing an additional delay at the MAC layer, before the channel access scheduling, it will retain aggregate sizes at higher numbers and consequently a better channel utilization. Also, in order to support both UDP and TCP transport layer protocols, the algorithm’s operational conditions are kept adaptive. The simulation results demonstrate that our proposed adaptive delayed channel access outperforms significantly the current 802.11n specification and non-adaptive delayed channel access

A Voice for the Voiceless: Peer-to-peer Mobile Phone Networks for a Community Radio Service

We propose a new application for mobile ad-hoc networks (MANETs) – community radio. We argue how MANETS help overcome important limitations in how community radio is currently operationalized. We identify critical design elements for a MANET based community radio service and propose a broad architecture for the same. We then investigate a most critical issue– the choice of the network wide broadcast protocol for the audio content. We identify desired characteristics of a community radio broadcasting service. We choose and evaluate eight popular broadcasting protocols on these characteristics, to find the protocols most suited for our application.

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

New trends on urban goods movement: Modelling and simulation of e-commerce distribution

In this paper, a modelling framework to complete the recent scientific works on urban goods modelling is proposed. More precisely, we introduce a substitution procedure that estimates the number of trips and the corresponding travelled distances for shopping drive, home delivery and reception points' strategies. Moreover, an appraisal of scenarios is proposed in order to study how these three new forms of proximity delivery services impact on the overall urban goods movement distribution. Starting from four extreme situations, we introduce more realistic scenarios in order to find a suitable combination of delivery strategies. All the scenarios are simulated using the proposed framework, and the main traffic issues related to e-commerce distribution channel are discussed. The best realistic combination promotes the joint usage of home deliveries and proximity reception points and allows a reduction of about 13% of the road occupancy rates in urban areas.urban goods movement, modelling, shopping trips, e-commerce

Optimization of intersatellite routing for real-time data download

The objective of this study is to develop a strategy to maximise the available bandwidth to Earth of a satellite constellation through inter-satellite links. Optimal signal routing is achieved by mimicking the way in which ant colonies locate food sources, where the 'ants' are explorative data packets aiming to find a near-optimal route to Earth. Demonstrating the method on a case-study of a space weather monitoring constellation; we show the real-time downloadable rate to Earth

Equation-Based Congestion Control for Unicast and Multicast Data Streams

We believe that the emergence of congestion control mechanisms for relatively-smooth congestion control for unicast and multicast traffic can play a key role in preventing the degradation of end-to-end congestion control in the public Internet, by providing a viable alternative for multimedia flows that would otherwise be tempted to avoid end-to-end congestion control altogether. The design of good congestion control mechanisms is a hard problem, even more so for multicast environments where scalability issues are much more of a concern than for unicast. In this dissertation, equation-based congestion control is presented as an alternative form of congestion control to the well-known TCP protocol. We focus on areas of equation-based congestion control which were not yet well understood and for which no adequate solutions existed. Starting from a unicast congestion control mechanism which in contrast to TCP provides smooth rate changes, we extend equation-based congestion control in several ways. We investigate how it can work together with applications which can only operate in a very limited region of available bandwidth and whose rate can thus not be adapted to the network conditions in the usual way. Such a congestion control mechanism can also complement conventional equation-based congestion control in regimes where available bandwidth is too low for further rate reduction. When extending unicast congestion control to multicast, it is of paramount importance to ensure that changes in the network conditions anywhere in the multicast tree are reported back to the sender as quickly as possible to allow the sender to adjust the rate accordingly. A scalable feedback mechanism that allows timely congestion feedback in the face of potentially very large receiver sets is one of the contributions of this dissertation. But also other components of a congestion control protocol, such as the rate increase/decrease policy or the slow-start mechanism, need to be adjusted to be able to use them in a multicast environment. Our resulting multicast congestion control protocol was implemented in a simulation environment for extensive protocol testing and turned into a library for the use in real-world applications. In addition, a simple video transmission tool was built for test purposes that uses this congestion control library

An evidence theoretic approach for traffic signal intrusion detection

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms