44 research outputs found
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A microbial inspired routing protocol for VANETs
We present a bio-inspired unicast routing protocol for vehicular Ad Hoc Networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multi-attribute decision-making strategy, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the state-of-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-to-end delay, and congestion, over the conventional method
From cellular attractor selection to adaptive signal control for traffic networks
The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains
A distributed position-based protocol for emergency messages broadcasting in vehicular ad hoc networks
Vehicular ad hoc networks (VANETs) can help reduce traffic accidents through broadcasting emergency messages among vehicles in advance. However, it is a great challenge to timely deliver the emergency messages to the right vehicles which are interested in them. Some protocols require to collect nearby real-time information before broadcasting a message, which may result in an increased delivery latency. In this paper, we proposed an improved position-based protocol to disseminate emergency messages among a large scale vehicle networks. Specifically, defined by the proposed protocol, messages are only broadcasted along their regions of interest, and a rebroadcast of a message depends on the information including in the message it has received. The simulation results demonstrate that the proposed protocol can reduce unnecessary rebroadcasts considerably, and the collisions of broadcast can be effectively mitigated
Intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles
Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput
Infrastructure-less D2D Communications through Opportunistic Networks
MenciĂłn Internacional en el tĂtulo de doctorIn recent years, we have experienced several social media blackouts, which have
shown how much our daily experiences depend on high-quality communication services.
Blackouts have occurred because of technical problems, natural disasters, hacker attacks
or even due to deliberate censorship actions undertaken by governments. In all cases,
the spontaneous reaction of people consisted in finding alternative channels and media so
as to reach out to their contacts and partake their experiences. Thus, it has clearly
emerged that infrastructured networks—and cellular networks in particular—are well
engineered and have been extremely successful so far, although other paradigms should
be explored to connect people. The most promising of today’s alternative paradigms
is Device-to-Device (D2D) because it allows for building networks almost freely, and
because 5G standards are (for the first time) seriously addressing the possibility of using
D2D communications.
In this dissertation I look at opportunistic D2D networking, possibly operating in an
infrastructure-less environment, and I investigate several schemes through modeling and
simulation, deriving metrics that characterize their performance. In particular, I consider
variations of the Floating Content (FC) paradigm, that was previously proposed in the
technical literature.
Using FC, it is possible to probabilistically store information over a given restricted
local area of interest, by opportunistically spreading it to mobile users while in the area.
In more detail, a piece of information which is injected in the area by delivering it to one
or more of the mobile users, is opportunistically exchanged among mobile users whenever
they come in proximity of one another, progressively reaching most (ideally all) users in
the area and thus making the information dwell in the area of interest, like in a sort of
distributed storage.
While previous works on FC almost exclusively concentrated on the communication
component, in this dissertation I look at the storage and computing components of FC,
as well as its capability of transferring information from one area of interest to another.
I first present background work, including a brief review of my Master Thesis activity,
devoted to the design, implementation and validation of a smartphone opportunistic
information sharing application. The goal of the app was to collect experimental data that permitted a detailed analysis of the occurring events, and a careful assessment of
the performance of opportunistic information sharing services. Through experiments, I
showed that many key assumptions commonly adopted in analytical and simulation works
do not hold with current technologies. I also showed that the high density of devices and
the enforcement of long transmission ranges for links at the edge might counter-intuitively
impair performance.
The insight obtained during my Master Thesis work was extremely useful to devise
smart operating procedures for the opportunistic D2D communications considered in this
dissertation. In the core of this dissertation, initially I propose and study a set of schemes
to explore and combine different information dissemination paradigms along with real
users mobility and predictions focused on the smart diffusion of content over disjoint
areas of interest. To analyze the viability of such schemes, I have implemented a Python
simulator to evaluate the average availability and lifetime of a piece of information, as
well as storage usage and network utilization metrics. Comparing the performance of
these predictive schemes with state-of-the-art approaches, results demonstrate the need
for smart usage of communication opportunities and storage. The proposed algorithms
allow for an important reduction in network activity by decreasing the number of data
exchanges by up to 92%, requiring the use of up to 50% less of on-device storage,
while guaranteeing the dissemination of information with performance similar to legacy
epidemic dissemination protocols.
In a second step, I have worked on the analysis of the storage capacity of probabilistic
distributed storage systems, developing a simple yet powerful information theoretical
analysis based on a mean field model of opportunistic information exchange. I have
also extended the previous simulator to compare the numerical results generated by the
analytical model to the predictions of realistic simulations under different setups, showing
in this way the accuracy of the analytical approach, and characterizing the properties of
the system storage capacity.
I conclude from analysis and simulated results that when the density of contents seeded
in a floating system is larger than the maximum amount which can be sustained by the
system in steady state, the mean content availability decreases, and the stored information
saturates due to the effects of resource contention. With the presence of static nodes, in
a system with infinite host memory and at the mean field limit, there is no upper bound
to the amount of injected contents which a floating system can sustain. However, as with
no static nodes, by increasing the injected information, the amount of stored information
eventually reaches a saturation value which corresponds to the injected information at
which the mean amount of time spent exchanging content during a contact is equal to
the mean duration of a contact.
As a final step of my dissertation, I have also explored by simulation the computing
and learning capabilities of an infrastructure-less opportunistic communication, storage and computing system, considering an environment that hosts a distributed Machine
Learning (ML) paradigm that uses observations collected in the area over which the FC
system operates to infer properties of the area. Results show that the ML system can
operate in two regimes, depending on the load of the FC scheme. At low FC load, the ML
system in each node operates on observations collected by all users and opportunistically
shared among nodes. At high FC load, especially when the data to be opportunistically
exchanged becomes too large to be transmitted during the average contact time between
nodes, the ML system can only exploit the observations endogenous to each user, which
are much less numerous. As a result, I conclude that such setups are adequate to support
general instances of distributed ML algorithms with continuous learning, only under the
condition of low to medium loads of the FC system. While the load of the FC system
induces a sort of phase transition on the ML system performance, the effect of computing
load is more progressive. When the computing capacity is not sufficient to train all
observations, some will be skipped, and performance progressively declines.
In summary, with respect to traditional studies of the FC opportunistic information
diffusion paradigm, which only look at the communication component over one area of
interest, I have considered three types of extensions by looking at the performance of FC:
over several disjoint areas of interest;
in terms of information storage capacity;
in terms of computing capacity that supports distributed learning.
The three topics are treated respectively in Chapters 3 to 5.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en IngenierĂa Telemática por la Universidad Carlos III de MadridPresidente: Claudio Ettori Casetti.- Secretario: Antonio de la Oliva Delgado.- Vocal: Christoph Somme
A survey of flooding, gossip routing, and related schemes for wireless multi- hop networks
Flooding is an essential and critical service in computer networks that is
used by many routing protocols to send packets from a source to all nodes in
the network. As the packets are forwarded once by each receiving node, many
copies of the same packet traverse the network which leads to high redundancy
and unnecessary usage of the sparse capacity of the transmission medium.
Gossip routing is a well-known approach to improve the flooding in wireless
multi-hop networks. Each node has a forwarding probability p that is either
statically per-configured or determined by information that is available at
runtime, e.g, the node degree. When a packet is received, the node selects a
random number r. If the number r is below p, the packet is forwarded and
otherwise, in the most simple gossip routing protocol, dropped. With this
approach the redundancy can be reduced while at the same time the reachability
is preserved if the value of the parameter p (and others) is chosen with
consideration of the network topology. This technical report gives an overview
of the relevant publications in the research domain of gossip routing and
gives an insight in the improvements that can be achieved. We discuss the
simulation setups and results of gossip routing protocols as well as further
improved flooding schemes. The three most important metrics in this
application domain are elaborated: reachability, redundancy, and management
overhead. The published studies used simulation environments for their
research and thus the assumptions, models, and parameters of the simulations
are discussed and the feasibility of an application for real world wireless
networks are highlighted. Wireless mesh networks based on IEEE 802.11 are the
focus of this survey but publications about other network types and
technologies are also included. As percolation theory, epidemiological models,
and delay tolerant networks are often referred as foundation, inspiration, or
application of gossip routing in wireless networks, a brief introduction to
each research domain is included and the applicability of the particular
models for the gossip routing is discussed
Cooperative Content Transmission for Vehicular Ad Hoc Networks using Robust Optimization
Vehicular ad hoc networks (VANETs) have a potential to promote vehicular telematics and infotainment applications, where a key and challenging issue is the design of robust and efficient vehicular content transmissions to combat the lossy inter-vehicle links. In this paper, we focus on the robust optimization of content transmissions over cooperative VANETs. We first derive a stochastic model for estimation of time-varying inter-vehicle distance, which is dependent of the vehicle real-time kinematics and the distribution of the initial space headway. With this model, we analytically formulate the transient inter-vehicle connectivity assuming Nakagami fading channels for the physical (PHY) layer. We also model the contention nature of the medium access control (MAC) layer, on which we are based to evaluate the throughput achieved by each vehicle equipped with dedicated short-range communication (DSRC). Combining these models, we derive a closed-formed expression for the upper bound of the probability of failure in intact-content transmissions. Based upon this theoretical bound, we develop a robust optimization model for assigning content data traffic among different cooperative transmission paths, where the objective is to minimize the maximum likelihood of unsuccessful content transmissions over the cooperative VANET. We mathematically transform the optimization model to another equivalent form, such that it can be practically deployed. Finally, we validate our theoretical development with extensive simulations. Numerical results are also provided to confirm the power of cooperation in boosting the VANET performance as well as demonstrate the advantage of the proposed robust optimization in terms of content data reception reliability
Mobile Ad-Hoc Networks
Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks
Special Topics in Information Technology
This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists
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An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data
Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions.
In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms.
Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data.
A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used.
PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods