Evaluating and Enhancing Information Dissemination in Urban Areas of Interest Using Opportunistic Networks

Chancay-García, Leonardo; Hernández-Orallo, Enrique; Manzoni, Pietro; Calafate, Carlos T.; Cano, Juan-Carlos

doi:10.1109/access.2018.2846201

Cited by 22 publications

(16 citation statements)

References 33 publications

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“…• STATION: it is a pedestrian mobility trace, which was generated from the movement of pedestrian in a subway station in Valencia, Spain [38]. This trace corresponds to a one hour interval (14:00 to 15:00) taken on Thursday, December 7th, 2017.…”

Section: Contact Time Characterisationmentioning

confidence: 99%

Optimising message broadcasting in opportunistic networks

Chancay-García

Hernández-Orallo

Manzoni

et al. 2020

Computer Communications

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Message Broadcasting in Opportunistic Networks is based on the opportunity of establishing contacts among mobiles nodes for message exchange. Nevertheless, as the amount of information transmitted in a contact is limited by the transmission speed and the contact duration, large messages are less likely to be exchanged, and thus their diffusion is severely limited. Furthermore, these failed transmissions can also lead to an important waste of network resources, since the message transmission is aborted when the contact ends and the message needs to be transmitted again in the next contact. Therefore, in this paper we study the impact that contact duration has on the broadcast of messages, showing that splitting a large message into smaller parts can improve its diffusion. Based on this idea, we propose an extension of the epidemic protocol called Xpread. The efficiency of this protocol mainly depends on how the original message is partitioned. Thus, in order to evaluate the impact and the efficiency of the partition scheme, we have developed an analytical model based on Population Processes, showing that a fixed size partition is the best option, while also providing a simple expression to obtain the optimal size. The Xpread has been evaluated exhaustively using four different mobiles traces, comprising both pedestrian and vehicular scenarios. The results show that the diffusion of large messages is improved up to four times with a slight reduction in the delivery time and overhead, minimising also message forwarding failures.

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Section: Contact Time Characterisationmentioning

confidence: 99%

Optimising message broadcasting in opportunistic networks

Chancay-García

Hernández-Orallo

Manzoni

et al. 2020

Computer Communications

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“…Therefore, Propagate and Filter proposes to converge the recommendation list of latent interest communities in the following way: Peer sampling does not have to be administered by any protocol since it is performed by the global movement of self-organized agents carrying smartphones. Propagate and Filter unfolds its dissemination potential at locations of high degrees of peer mobility such as urban areas [9]. When a peer receives data from a similar peer, he/she resamples his/her neighborhood preference list and else does nothing.…”

Section: Enhance Locally Available Profile Datamentioning

confidence: 99%

On gossip-based information dissemination in pervasive recommender systems

Eichinger

Beierle

Papke

et al. 2019

Proceedings of the 13th ACM Conference on Recommender Systems

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Pervasive computing systems employ distributed and embedded devices in order to raise, communicate, and process data in an anytime-anywhere fashion. Certainly, its most prominent device is the smartphone due to its wide proliferation, growing computation power, and wireless networking capabilities. In this context, we revisit the implementation of digitalized word-of-mouth that suggests exchanging item preferences between smartphones offline and directly in immediate proximity. Collaboratively and decentrally collecting data in this way has two benefits. First, it allows to attach for instance location-sensitive context information in order to enrich collected item preferences. Second, model building does not require network connectivity. Despite the benefits, the approach naturally raises data privacy and data scarcity issues. In order to address both, we propose Propagate and Filter, a method that translates the traditional approach of finding similar peers and exchanging item preferences among each other from the field of decentralized to that of pervasive recommender systems. Additionally, we present preliminary results on a prototype mobile application that implements the proposed device-to-device information exchange. Average ad-hoc connection delays of 25.9 seconds and reliable connection success rates within 6 meters underpin the approach's technical feasibility.

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“…n v h c i indicates the rating numbers of user v h under the interactive node's attribute c i . According to the above two formulas, the user's preference similarity to the interactive node is expressed as equation [12], where α is the balance coefficient (in this paper, α = 0.5).…”

Section: ) Active Level Of Nodesmentioning

confidence: 99%

“…Due to the 5G communication is closely related to social life, we define this kind of network structure as the opportunistic social networks [8]- [11]. On account of the development of high-speed communication mode brought by 5G network, the transmission of a large amount of data in a short time will also bring data redundancy, and the problems brought by it need to be solved by appropriate opportunistic social network routing scheme [12].…”

Section: Introductionmentioning

confidence: 99%

An Effective Transmission Strategy Exploiting Node Preference and Social Relations in Opportunistic Social Networks

Yan

Chen

et al. 2019

IEEE Access

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With the development of network technology and the advent of 5G communication era, highfrequency radio waves with high bandwidth will become the main choice of signal sources. While promoting the development of a high-speed communication network, a high-frequency radio wave has the problem of limited coverage, which makes opportunistic social network become the mainstream communication method. Since the transmission of a large amount of data in a short time will cause the problem of data redundancy, opportunistic social networks suggest that the most appropriate next hop should be selected to achieve efficient data transmission. At present, there are several routing algorithms based on social relations, which attempt to select the most suitable next-hop node among neighbor nodes by making use of relevant context information and historical interaction between nodes. However, existing data transmission methods in opportunistic social networks mainly focus on the influence of a few social attributes on the similarity between nodes but ignore the transmission preference caused by individual characteristics of nodes. To improve the transmission efficiency, this paper establishes an effective data transmission strategy (ENPSR) exploiting node preference and social relations in opportunistic social networks. In our scheme, individual transmission preferences are obtained by measuring the social attributes and historical information of nodes in the transmission process. The appropriate message delivery decision is determined by the prediction scheme, and the continuous and stable data transmission are realized through the recommendation mechanism. According to the simulation experiments, the average delivery ratio of ENPSR algorithm is 0.85, which is 20% higher than that of the epidemic algorithm.

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Evaluating and Enhancing Information Dissemination in Urban Areas of Interest Using Opportunistic Networks

Cited by 22 publications

References 33 publications

Optimising message broadcasting in opportunistic networks

Optimising message broadcasting in opportunistic networks

On gossip-based information dissemination in pervasive recommender systems

An Effective Transmission Strategy Exploiting Node Preference and Social Relations in Opportunistic Social Networks

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