Radu-Corneliu Marin

Exploring Predictability in Mobile Interaction

Recent endeavors in mobile computing concentration analyzing the predictability of human behavior by means of mobility models synthesized from real mobile user traces. Currently, the main focus of such studies is physic allocation: discovering travel patterns, estimating real user movements and anticipating the whereabouts and dynamics of individuals. In this paper, we propose to widen the analyzed context as to take into account a more natural activity inhuman behavior, namely interaction. As such, we explore the predictability of user synergy based on tracing data collected from mobile phone users in academic and office environments. We take into account interactions over Bluetooth and over wireless networks and, by measuring the entropy of interacting both with peers and wireless access points, we discover a remarkable invariability in synergic patterns.

A methodology for assessing the predictable behaviour of mobile users in wireless networks

The analysis of the predictability of human behaviour is an emerging topic in the ubiquitous computing community. Recent endeavours in studying the human behaviour are either based on synthetic models or on real mobile user traces, but what they are mainly focusing on is location: discovering travel patterns or anticipating the whereabouts of mobile users. We extend the analysed context by studying the wireless behaviour of mobile users: interactions with both peers and network devices in academic and office environments. We propose a methodology and a set of guidelines to assess and aid in analysing wireless mobile traces. We prove that the human behaviour is predictable when the studied traced sets are convergent, complete and correct, and we obtain a surprising invariability in interacting with wireless access points, as we are able to pinpoint a wireless user to one of two locations at any specific hour. Copyright

Interest-awareness in data dissemination for opportunistic networks

Generally, data dissemination in opportunistic network uses flooding to ensure that content is spread to interested subscribers. However, this might lead to congestion and high overhead, so alternative solutions are required. In this article, we propose leveraging context information such as node interests, social connections and predictions based on contact history, in order to decrease congestion without affecting the networks hit rate and delivery latency. Thus, we first propose a basic interest-based dissemination algorithm, to show that nodes tend to group together based on interests. Then, we present ONSIDE, an algorithm that also uses other types of context information to select the nodes that act as forwarders. Finally, we propose five heuristics for sorting the messages in a nodes memory, and show how each of them affects hit rate, delivery latency and congestion.

Reaching for the clouds: contextually enhancing smartphones for energy efficiency

Energy efficiency has gradually become a compulsory need in mobile computing as the processing requirements for smartphones have increased exponentially. Moreover, the current demand is stretching beyond the extents of modern battery technology. In this sense, we introduce a novel collaboration solution for mobile devices based on a contextual search entitled Hybrid Contextual Cloud for Ubiquitous Platforms comprising of Smartphones (HYCCUPS). HYCCUPS takes advantage of the pervasive nature of smartphones and of current wireless communication technologies as to offer offloading execution of mobile applications in an opportunistic on-the-fly hybrid computing cloud. We design an adaptive contextual search algorithm for schedulling mobile code execution in smartphone communities based on predicting the availability and mobility of devices in the near vicinity. We emulate the HYCCUPS framework based on real user traces and we prove that it improves battery health, maximizes power save, minimizes overall execution time of mobile applications and it preserves or even enhances user experience.

Opportunistic dissemination using context-based data aggregation over Interest Spaces

The traditional pub/sub paradigm is inadequate for dissemination in mobile networks, since any node is able to publish content at any time, thus easily leading to congestion. Therefore, a dissemination paradigm where mobile nodes contribute with a fraction of their resources is needed through the use of opportunistic networks. Moreover, as shown in recent work, a suitable organization for data dissemination in mobile networks should be centered around interests. Thus, we propose an interest-based dissemination framework for opportunistic networks entitled Interest Spaces. We focus on the first step required for implementing it in real life: data aggregation. Furthermore, we propose a method for aggregating data from encountered peers, in order for opportunistic nodes to have an informed view of the network and to avoid storing excessive amounts of information or performing many data exchanges.

Trust and reputation management for opportunistic dissemination

Abstract Nodes in opportunistic networks need to cooperate to disseminate data. However, employing intermediate nodes for dissemination leads to several security issues. Here, we propose an opportunistic trust and reputation mechanism entitled SAROS, which detects and avoids malicious nodes, i.e. nodes which, upon receiving messages for other interested peers, modify their content in order to spread false information. This can negatively affect the network, by polluting it with spam messages, or dropping messages of interest to the nodes in the network. By detecting and avoiding malicious nodes, SAROS is able to increase the percentage of correct messages that reach their destinations.

Interest spaces

We propose the Interest Spaces framework.The framework supports opportunistic dissemination by allowing applications to mark data with tags, and managing caching, routing, forwarding, and disseminating decisions.We perform analysis of each functionality. Embedded ubiquitous computing systems (EUCS) promise to grow remarkably in the near future. For such systems, new communication technologies are constructed to cope with the increasing functional and temporal demans. Because of the ubiquitousness of mobile devices nowadays, for example, traditional publish/subscribe is no longer an adequate model for data dissemination in mobile networks. Since any node can publish content at any time, the network can get congested easily, so a dissemination paradigm where mobile nodes contribute with a fraction of their resources is needed, through the use of opportunistic networks. Furthermore, a suitable organization for data dissemination in mobile networks should be centered around interests. Thus, we propose a unified interest-based dissemination framework for opportunistic networks entitled Interest Spaces, which simplifies dissemination by just allowing applications to mark data items with certain tags, letting the framework handle the caching, routing, forwarding, and disseminating. Similarly, applications that need to subscribe to channels simply have to specify the tags they are interested in, and the framework does the rest. However, the Interest Spaces framework also allows applications to have more control over the dissemination process if they wish, by specifying various criteria and dissemination rules. In this article, we present the architecture and components of Interest Spaces. We focus on data aggregation at the context layer, showing through simulations the benefits it brings in opportunistic dissemination. We also present a real-life use case for Interest Spaces through Chatty, an opportunistic chat application.

MobEmu: A Framework to Support Decentralized Ad-Hoc Networking

Opportunistic networks (ONs) are an extension of mobile ad hoc networks where nodes are generally human-carried mobile devices like smartphones and tablets, which do not have a global view of the network. They only possess knowledge from the nodes they encounter, so well-defined paths between a source and a destination do not necessarily exist. There are plenty of real-life uses for ONs, including, but not limited to, disaster management, smart cities, floating content, advertising, crowd management, context-aware platforms, distributed social networks, or data offloading and mobile cloud computing. In order to implement and test a routing or dissemination solution for opportunistic networks, simulators are employed. They have the benefit of allowing developers to analyze and tweak their solutions with reduced costs, before deploying them in a working environment. For this reason, in this chapter we present MobEmu, an opportunistic network simulator which can be used to evaluate a user-created routing or dissemination algorithm on a desired mobility trace or synthetic model.

Improving Opportunistic Networks by Leveraging Device-to-Device Communication

Several popular low-level device-to-device techniques, such as Bluetooth and Wi-Fi Direct, are seen today as enablers for 5G mobile networks, as the communication infrastructure for future Internet of Things systems. However, most of these technologies do not support direct overthe- air communication between end users/devices. Opportunistic networks propose the use of delay-tolerant and wireless communication over such technologies, toward routing messages between end users. In our previous studies, we have shown how taking advantage of the existing Wi-Fi infrastructure leads to high hit rates and low latency based on the increased wireless range coupled with high bandwidth. In this article, we extend our proposed architecture for covering infrastructure-less environments through a novel mechanism that allows connecting peers through Wi-Fi Direct in a seamless and secure fashion. Furthermore, we emulate real-life user traces to empirically prove that our solution improves hit rate by 13 percent without impacting battery life.

ONSIDE-SELF: A Selfish Node Detection and Incentive Mechanism for Opportunistic Dissemination

The advent of IoT (the Internet of Things) has led to the necessity of fast and secure communication between devices, ranging from small sensors to top-of-the-line smartphones or laptops. One proposal for IoT communication is through 5G, which is estimated to be rolled out by 2020. However, the infrastructure for 5G communication might not always be present, or it should be avoided because of congestion. Moreover, employing it in smaller IoT networks can prove too expensive in some cases, while some small devices such as sensors might not even have 5G capabilities (or having them would greatly increase their price). For these reasons, opportunistic communication is an alternative for IoTs where mobile broadband connections cannot be used. Opportunistic networks are formed of mobile devices (such as smartphones and tablets belonging to social users) that communicate using close-range protocols such as Bluetooth or WiFi Direct. These networks are based on the store-carry-and-forward paradigm, where contacts between nodes are used opportunistically to transport data from a source to a destination, even though the two nodes might never be in direct communication range. Data dissemination assumes that nodes do not send directed messages (i.e., from a source to a pre-set destination), instead using channels to perform communication. Nodes are able to subscribe to channels, which are represented by interests (e.g., a node interested in “IT” will need to receive all messages marked with that tag). The main requirement of opportunistic networks is that the participating nodes should be altruistic, since communication is performed with the help of other nodes. However, this might not always be the case, since selfish nodes might decide that they do not want to help others. Such nodes should be detected and not allowed to participate in the dissemination process. This way, their messages will not be delivered, so they will be forced to become altruistic if they want a good networking experience. In this chapter, we propose a method for detecting and punishing selfish nodes in opportunistic networks dissemination, using gossiping mechanisms over the dynamic social network. Nodes learn about the behavior of other nodes and, when a contact occurs, share this information with an encountered device. We apply this method to an existing social and interest-based dissemination algorithm (ONSIDE) and show that it correctly detects and punishes selfish nodes, thus increasing the network’s behavior in terms of message delivery and congestion.