Opportunistic mobile social networks: architecture, privacy, security issues and future directions

Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security

Opportunistic Shortest Path Forwarding in Delay Tolerant Networks

ABSTRACT Delay Tolerant Networks (DTNs) are characterized by probabilistic links formed among mobile nodes indicating their probabilistic encounters. Prior work on DTN routing uses expected delays as a routing metric to decide the next hop relay node for packet delivery to the destination. However, they measure the expected delays by taking the minimum of the expected delays over all possible paths from a candidate relay. This metric, denoted by MinEx, does not account for the opportunity gain enabled by having multiple paths to the destination through encountering multiple future neighbors. Since DTN routing uses as the relay the first encountered node satisfying given routing criteria, the random delays to multiple relay nodes should be aggregated. Thus, the true expected delays can be measured by taking the expectation of the minimum delays, denoted as ExMin, over all possible probabilistic paths from the candidate

Threshold Based Best Custodian Routing Protocol for Delay Tolerant Network

Delay Tolerant Network (DTN) is a kind of network in which the source may not be able to establish the stable and uninterrupted path to destination due to network partitioning, dynamic topology change and frequent disconnections. In order to dealt disruption and disconnections a store, carry and forward paradigm is used in which node stores the incoming messages in its buffer, carries it while moving and forward when comes within the transmission range of other nodes. Message forwarding contributes and important role in increasing its delivery. For instance, probabilistic routing protocol forwards message to a node having high probability value to meet message destination. These protocols cannot handle a situation in which the node continually transmits messages even the probability difference is very small. In this paper, we have proposed a routing protocol known as Threshold Based best custodian Routing Protocol (TBbcRP) for delay tolerant network. We have proposed a threshold-based method to compute the quality value which is the ability of node to carry message. A self-learning mechanism has been used to remove the delivered messages from the network. Moreover, a buffer aware mechanism has been used that make sure availability of buffer space at receiver before message transmission. We have compared the performance of TBbcRP with Epidemic, PRoPHET and Delegated Forwarding. The proposed TBbcRP outperforms in terms of maximizing the delivery probability, reducing number of transmissions and message drop

Timely Data Delivery in a Realistic Bus Network

Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I

FSF: Applying machine learning techniques to data forwarding in socially selfish Opportunistic Networks

[EN] Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our ¿FriendShip and Acquaintanceship Forwarding¿ (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.This work was partially supported by the "Camilo Batista de Souza/Programa Doutorado-sanduiche no Exterior (PDSE)/Processo 88881.133931/2016-01" and by the Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018, Spain, under Grant RTI2018-096384-B-I00".Souza, C.; Mota, E.; Soares, D.; Manzoni, P.; Cano, J.; Tavares De Araujo Cesariny Calafate, CM.; Hernández-Orallo, E. (2019). FSF: Applying machine learning techniques to data forwarding in socially selfish Opportunistic Networks. Sensors. 19(10):1-26. https://doi.org/10.3390/s19102374S1261910Trifunovic, S., Kouyoumdjieva, S. T., Distl, B., Pajevic, L., Karlsson, G., & Plattner, B. (2017). 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FROM WIDE- TO SHORT-RANGE COMMUNICATIONS: USING HUMAN INTERACTIONS TO DESIGN NEW MOBILE SYSTEMS AND SERVICES

The widespread diffusion of mobile devices has radically changed the way people interact with each other and with object of their daily life. In particular, modern mobile devices are equipped with multiple radio interfaces allowing users to interact at different spatial granularities according to the various radio technology they use. The research community is progressively moving to heterogeneous network solutions which include many different wireless technologies seamlessly integrated to address a wide variety of use cases and requirements. In 5th- Generation (5G) of mobile network we can find multiple network typology such as device-to-device (D2D), vehicular networks, machine-to-machine(M2M), and more, which are integrated in the existing mobile-broadband technology such as LTE and its future evolutions. In this complex and rich scenario, many issues and challenges are still open from a technological, architectural, and mobile services and applications points of view. In this work we provide network solutions, mobile services, and applications consistent with the 5G mobile network vision by using users interactions as a common starting point. We focus on three different spatial granularities, long, medium/short, and micro mediated by cellular network, Wi-Fi, and NFC radio technologies, respectively. We deal with various kinds of issues and challenges according to the distinct spatial granularity we consider. We start with an user centric approach based on the analysis of the characteristics and the peculiarities of each kind of interaction. Following this path, we provide contributions to support the design of new network architectures, and the development of novel mobile services and applications