Opportunistic networks (OppNets) leverage opportunistic contacts to flow data across an infrastructure-free network. As of yet, OppNets’ performance depends on applying the most suitable forwarding strategy based on the OppNet typology. On the other hand, software-defined networking (SDN) is a paradigm for wired networks that decouples the control and data planes. The control plane oversees the network to configure the data plane optimally. Our proposal uses SDN-like controllers to build a partial overview of the opportunistic network. The forwarding strategy uses this context information to achieve better network performance. As a use case of our proposal, in the context of an OppNet quota-based forwarding algorithm, we present a controller-driven architecture to tackle the congestion problem. Particularly, the controller-driven architecture uses the context information on the congestion of the network to dynamically determine the message replication limit used by the forwarding algorithm. A simulation based on real and synthetic mobility traces shows that using context information provided by the controller to configure the forwarding protocol increments the delivery ratio and keeps a good latency average and a low overhead compared with the baseline forwarding protocols based on message replication. These results strengthen the benefits of using supervised context information in the forwarding strategy in OppNets.

We have designed a collection of scenarios, a corpus, for its use in the study and development of routing algorithms for opportunistic networks. To obtain these scenarios, we have followed a methodology based on characterizing the space and choosing the best exemplary items in such a way that the corpus as a whole was representative of all possible scenarios. Until now, research in this area was using some sets of non-standard network traces that made it difficult to evaluate algorithms and perform fair comparisons between them. These developments were hard to assess in an objective way, and were prone to introduce unintentional biases that directly affected the quality of the research. Our contribution is more than a collection of scenarios; our corpus provides a fine collection of network behaviors that suit the development of routing algorithms, specifically in evaluating and comparing them. If the scientific community embraces this corpus, the community will have a global-agreed methodology where the validity of results would not be limited to specific scenarios or network conditions, thus avoiding self-produced evaluation setups, availability problems and selection bias, and saving time. New research in the area will be able to validate the routing algorithms already published. It will also be possible to identify the scenarios better suit specific purposes, and results will be easily verified. The corpus is available free to download and use.

This paper proposes a protocol to ensure market equity in car-sharing applications where fares result fair and just, and actors cannot influence prices to their benefit. To achieve our proposal, we use homomorphic encryption to hide prices and opportunistic networking to avoid any central element that could alter the market. Both technologies are combined in a twoway communication protocol achieving fair price setting for both drivers and passengers. Results show that the proposal is feasible and mitigate price alteration attempts.

In this paper, we present a broadcast dissemination protocol for messages in opportunistic networks (OppNet) that is efficient in terms of energy consumption and network capacity usage, while not increasing the number of excluded nodes (nodes not receiving messages). The majority of the OppNet broadcast delivery schemes proposed in the literature, do not take into consideration that reducing energy and buffer usage is of paramount importance in these wireless networks normally consisting of small devices. In our protocol, broadcast messages are limited by carefully selecting their prospective forwarders (storers). The keystone of our protocol is the use of Optimal Stopping Theory, which selects the best message storers at every stage of the algorithm, while holding back broad message dissemination until convenient conditions are met. The broadcast efficiency of the proposed protocol out competes other OppNet broadcast proposals in four well-known scenarios. Furthermore, the protocol reduces the number of both dropped messages and nodes not receiving messages, thus maximising network capacity usage, and the span of the message delivery.

This paper presents a new strategy to efficiently deliver messages to influencers in social opportunistic networks. An influencer node is an important node in the network with a high social centrality and, as a consequence, it can have some characteristics such as high reputation, trustfulness and credibility, that makes it an interesting recipient. Social network analysis has already been used to improve routing in opportunistic networking, but there are no mechanisms to efficiently route and deliver messages to these network influencers. The delivery strategy proposed in this article uses optimal stopping statistical techniques to choose among the different delivery candidate nodes in order to maximise the social centrality of the node chosen for delivery. For this decision process, we propose a routing–delivery strategy that takes into account node characteristics such as how central a node is in terms of its physical encounters. We show, by means of simulations based on real traces and message exchange datasets, that our proposal is efficient in terms of influencer selection, overhead, delivery ratio and latency time. With the proposed strategy, a new venue of applications for opportunistic networks can be devised and developed using the leading figure of social influencers.

In the domain of opportunistic networking, just like in any other domain of computer science, the engineering process should span all stages between an original idea and the validation of its implementation in real conditions. Yet most researchers often stop halfway along this process: they rely on simulation to validate the protocols and distributed applications they design, and neglect to go further. Their algorithms are thus only rarely implemented for real, and when they are, the validation of the resulting code is usually performed at a very small scale. Therefore, the results obtained are hardly repeatable or comparable to others. LEPTON is an emulation platform that can help bridge the gap between pure simulation and fully operational implementation, thus allowing developers to observe how the software they develop (instead of pseudo-code that simulates its behavior) performs in controlled, repeatable conditions. In this paper we present LEPTON, an emulation platform we developed, and we show how existing opportunistic networking systems can be adapted to run with this platform. Taking two existing middle ware systems as use cases, we also demonstrate that running demanding scenarios with LEPTON constitute an excellent stress test and a powerful tool to improve the opportunistic systems under test. (C) 2018 Elsevier Inc. All rights reserved.

Real-life room-escape games are ludic activities in which participants enter a room in order to get out of it only after solving some riddles. In this paper, we explain a Room Escape teaching experience developed in the Engineering School at Universitat Autònoma de Barcelona. The goal of this activity is to increase student’s motivation and to improve their learning on two courses of the second year in the Computer Engineering degree: Computer Networksand Information and Security.

In the context of Opportunistic Ad Hoc Networking paradigms, group communication schemes (Manycast) are difficult to conduct. In this article, we propose a general delivery scheme for Manycast group communications based on mobile code. Our proposal extends network addressing by moving from the static header field paradigm to a software code-based addressing scheme. We allow messages to be delivered using built-in software codes that consider application-defined, context-aware or history-based information. Additionally, we allow messages to carry a delivery state that permits them to perform refined delivery-decision-making methods. As a consequence of this scheme, we have found that new group communication schemes, besides the traditional ones, may be beneficial to improve the network performance and to provide new functionalities to emerging scenarios like intermittently connected networks of heterogeneous physical objects. We present an application of this scheme to solve, following an analytical delivery method, the problem of sending a message to k and only k nodes of a heterogeneous and opportunistic network scenario that fit best a given criterion. We show, using simulations, that our proposal performs better than traditional approaches. Finally, to show that our proposal is feasible, we present an implementation of our proposal in a real Opportunistic Ad Hoc network, a DTN network, compatible with the de facto standard Bundle Protocol.

In the context of Opportunistic Networking (OppNet), designing routing and delivery protocols is currently an open and active line of research. In some OppNet scenarios, destination addresses are not always known by sending applications. Profile-cast models solve this problem by allowing message destinations to be users or groups of users defined by their profiles. These profiles provide very effective ways of characterizing nodes in terms of node’s attributes such as their profession, interests or typical whereabouts, for example. However, there are strong limitations in OppNet Profile-casting. There is no current way of representing special profiles defined by relative delivery functions such as best, maximum or over-the-average: nodes belong to these relative profiles taking into account not only attributes from the very same node but also relative to others from the same profile. In this article, we introduce Relcast, a Profile-cast model that allows messages to be sent to profiles defined in terms of relative delivery functions. Additionally, we present Explore and Wait, a composite routing-delivery scheme that uses optimal stopping theory-based delivery strategies to route Relcast messages. We show, using simulations, that this routing-delivery scheme performs better than traditional approaches that use state-of-the-art routing-delivery primitives.

The students' lack of motivation is a usual problem. The students value more the obtention of the degree than the developing of competences and skills. In order to fight this, we developed a gamification's experience based on merits and leaderboards. The merits are linked to the attainment of skills and competences that students usually do not appreciate. During the year 2016, we are carrying out a pilot involving 950 students at the Graduate of Engineering in Computer Science at the Autonomous University of Barcelona (UAB).

We present HabCast, a profile-cast communication paradigm that learns about the mobility habits of the location-aware nodes of the network and uses this information both to route the messages, and to deliver them only to the nodes that match the target behavioral profile. HabCast substitutes destination's identifier by a mobility profile model called habitat, meaning that allows users to send messages "to any nodes who usually roams around this area" instead of sending messages intended to a node. HabCast is designed to operate without network infrastructure, using Opportunistic Networking strategies and operates in three phases: approximation, floating and delivery phase. HabCast enables new services and applications on Opportunistic Networking by automatically inferring the nodes' behavioral profiles and using them to define the messages' destinations. The overhead introduced by HabCast is evaluated using a proof-of-concept implementation, and its performance and feasibility is studied, through simulation, under the scope of a real carsharing application.

We present PrivHab+, a secure geographic routing protocol that learns about the mobility habits of the nodes of the network and uses this information in a secure manner. PrivHab+ is designed to operate in areas that lack of network, using the store-carry-and-forward approach. PrivHab+ compares nodes and chooses the best choice to carry messages towards a known geographical location. To achieve a high performance and low overhead, PrivHab+ uses information about the usual whereabouts of the nodes to make optimal routing decisions. PrivHab+ makes use of cryptographic techniques from secure multi-party computation to preserve nodes' privacy while taking routing decisions. The overhead introduced by PrivHab+ is evaluated using a proof-of-concept implementation, and its performance is studied under the scope of a realistic application of podcast distribution. PrivHab+ is compared, through simulation, with a set of well-known delay-tolerant routing algorithms in two different scenarios of remote rural areas. (C) 2015 Elsevier B.V. All rights reserved.

La falta de motivacio de l'alumnat es un problema recorrent. L'alumnat valora obtenir el titol per sobre d'assolir coneixements, habilitats i competencies. Per combatre aixo, hem desenvolupat una experiencia de ludificacio que s'articula a traves d'un sistema de merits i classificacions. Els merits van lligats a l'assoliment d'habilitats i competencies que l'alumnat no sol valorar. Durant el curs 15/16, s'esta realitzant un pla pilot amb 905 alumnes del Grau d'Enginyeria en Informatica a la UAB.

AbstractPrivHab+ is a secure geographic routing protocol that learns the habits of the nodes.PrivHab+ compares nodes and selects the best to carry a message towards a location.PrivHab+ makes use of cryptographic techniques to preserve nodes' privacy.PrivHab+ performance is studied under the scope of a podcast distribution application. Display Omitted We present PrivHab+, a secure geographic routing protocol that learns about the mobility habits of the nodes of the network and uses this information in a secure manner. PrivHab+ is designed to operate in areas that lack of network, using the store-carry-and-forward approach. PrivHab+ compares nodes and chooses the best choice to carry messages towards a known geographical location. To achieve a high performance and low overhead, PrivHab+ uses information about the usual whereabouts of the nodes to make optimal routing decisions. PrivHab+ makes use of cryptographic techniques from secure multi-party computation to preserve nodes' privacy while taking routing decisions. The overhead introduced by PrivHab+ is evaluated using a proof-of-concept implementation, and its performance is studied under the scope of a realistic application of podcast distribution. PrivHab+ is compared, through simulation, with a set of well-known delay-tolerant routing algorithms in two different scenarios of remote rural areas.

PrivHab is a georouting protocol that improves multiagent systems itinerary decision-making. PrivHab learns the mobility habits of the nodes of the network by building a model of the habitat of every node. Then, it uses this information to select an itinerary for each agent carrying a piece of data to reach its destination. PrivHab makes use of cryptographic techniques from secure multi-party computation to make the decisions while preserving nodes' privacy.

Pro-active message's delay tolerant networks DTNs are based on the usage of mobile code to obtain messages that contain their own routing code. This architecture allows applications to use the same network in different ways. The keystone of this type of heterogeneous network is a collection of contextual and application-related information that it is stored in every node and accessed by the messages' routing code. Access to that information must be protected in order to make the whole architecture feasible; the operation of the network has to be secure, and attacks of information poisoning have to be avoided. We propose an identity-based access control system for pro-active message's DTN based on tools that are available in DTN networks, like symmetric key encryption and hashes. Our system grants confidentiality and integrity to the contextual information and solves the question of messages needing to use distributed information stored in nodes to route properly. The proof of concept of identity-based access control in a certain kind of application demonstrates the feasibility of the proposal. The comparison between our proposal and other access control systems shows that identity-based access control is the only system that fits well with the special characteristics of pro-active message's DTN. Copyright © 2016 John Wiley & Sons, Ltd.

PrivHab is a georouting protocol that improves multiagent systems itinerary decision-making. PrivHab learns the mobility habits of the nodes of the network by building a model of the habitat of every node. Then, it uses this information to select an itinerary for each agent carrying a piece of data to reach its destination. PrivHab makes use of cryptographic techniques from secure multi-party computation to make the decisions while preserving nodes' privacy.

We consider a realistic podcast distribution application in remote rural areas, where programs have to be recorded into a CD and distributed to the local radio stations. We use a store-carry-and-forward approach, based on mobile agents, that is designed to operate in areas that lack network infrastructure. PrivHab is a georouting protocol that learns the mobility habits of the nodes of the network, then, it uses this information to select itineraries for the agents carrying the data. PrivHab makes use of secure multi-party computation techniques to preserve nodes’ privacy. The PrivHab protocol is compared with a set of delay-tolerant routing algorithms and shown to outperform them.

We consider a realistic podcast distribution application in remote rural areas, where programs have to be recorded into a CD and distributed to the local radio stations. We use a store-carry-and-forward approach, based on mobile agents, that is designed to operate in areas that lack network infrastructure. PrivHab is a georouting protocol that learns the mobility habits of the nodes of the network, then, it uses this information to select itineraries for the agents carrying the data. PrivHab makes use of secure multi-party computation techniques to preserve nodes' privacy. The PrivHab protocol is compared with a set of delay-tolerant routing algorithms and shown to outperform them.