Combining Spatial and Social Awareness in D2D Opportunistic Routing

Nunes, Ivan O.; Celes, Clayson; Nunes, Igor; Melo, Pedro O. S. Vaz de; Loureiro, Antônio A. F.

doi:10.1109/mcom.2017.1600657

Cited by 29 publications

(27 citation statements)

References 14 publications

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“…In large-scale scenarios, they achieved approximately the same delivery ratio of Bubble Rap, but with 40% less overhead. In [9], the authors developed SAMPLER, an opportunistic forwarding strategy combining mobility, PoIs, and socialawareness. Their experiments showed improved delivery ratio, reduced overhead, and faster message delivery.…”

Section: A Related Workmentioning

confidence: 99%

Towards Human-Aware D2D Communication

Costa

Viana

Ziviani

et al. 2020

2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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Mobility, social interactions, and other human characteristics shall support Future Mobile Networks in routine prediction and resource management. This work investigates human-aware metrics supporting services or protocols leveraging opportunistic communication. These metrics represent different types of knowledge extracted from people routine present in their movements. Because of the strong routine component of human mobility, such metrics capture different but recurrent behaviors on wireless encounters between mobile users. We report the experience through a case study with a real-world dataset along with results from trace and metrics analysis. The results show heterogeneity in metric coefficients and contact occurrence and duration in different periods of the day, highlighting the need for characterising traces before their use.

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Section: A Related Workmentioning

confidence: 99%

Towards Human-Aware D2D Communication

Costa

Viana

Ziviani

et al. 2020

2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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“…The main goal of this algorithm is to adjust the priority of message routing-forwarding among nodes and synchronously implement an efficient data transmission process through node cooperation. Eventually, Nunes et [28] present a social-aware routing algorithm that do not pay attention to the importance of the spacial and temporal data information of human mobility. This scheme aims at reducing the average device-to-device delay and network overhead in opportunistic social networks via spacial and social attributes of nodes.…”

Section: The Existing Community-ignorant Routing Approaches In Opportmentioning

confidence: 99%

“…Therefore, it is essential for routing approaches in opportunistic networks to update and optimize node community periodically. (3) Most routing methods based on location prediction only consider the probability of nodes moving to the next position at the next state but do not evaluate the probability of the next encounter between destinations and neighbors and whether they belong to the same social community [28]. The location prediction for nodes during current short time periods are obviously not suitable for the real scenarios of opportunistic networks, so a periodic location prediction method is necessary for state assessment of nodes over a long period of time [29].…”

Section: Introductionmentioning

confidence: 99%

NPLP: An Improved Routing-Forwarding Strategy Utilizing Node Profile and Location Prediction for Opportunistic Networks

Bang-yuan

Chen

2019

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Opportunistic networks are considered as the promising network structures to implement traditional and typical infrastructure-based communication by enabling smart mobile devices in the networks to contact with each other within a fixed communication area. Because of the intermittent and unstable connections between sources and destinations, message routing and forwarding in opportunistic networks have become challenging and troublesome problems recently. In this paper, to improve the data dissemination environment, we propose an improved routing-forwarding strategy utilizing node profile and location prediction for opportunistic networks, which mainly includes three continuous phases: the collecting and updating of routing state information, community detection and optimization and node location prediction. Each mobile node in the networks is able to establish a network routing matrix after the entire process of information collecting and updating. Due to the concentrated population in urban areas and relatively few people in remote areas, the distribution of location prediction roughly presents a type of symmetry in opportunistic networks. Afterwards, the community optimization and location prediction mechanisms could be regarded as an significant foundation for data dissemination in the networks. Ultimately, experimental results demonstrate that the proposed algorithm could slightly enhance the delivery ratio and substantially degrade the network overhead and end-to-end delay as compared with the other four routing strategies.

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“…For example, SimbetTS [14] adds the tie strength for utility calculation, Oi [15] and SCORP [16] combine users' social ties with their interest for social-aware opportunistic routing, and GROUPS-Net [17] combines social awareness with a probabilistic approach using group meetings as a measure of social context to improve data delivery ratio. SAMPLER [18] uses social communities and social popularity metrics as they were introduced in the original Bubble Rap [4] scheme and adds the individual mobility and points of interest within a region to them, helping to achieve high delivery ratio and reducing network overhead. LASS [19] is proposed taking into account the difference of members' internal activity within each community.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Forwarding Capability Evaluation Method for Opportunistic Offloading in Mobile Edge Computing

Wang

Gao

Niu

et al. 2018

Wireless Communications and Mobile Computing

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Opportunistic offloading can be utilized to offload computing tasks and traffic data in Mobile Edge Computing (MEC). To improve the ratio of successful data offloading and reduce unnecessary data redundancy in opportunistic forwarding process, some methods of evaluating a device’s forwarding capability are proposed. However, most of these methods do not consider the temporal impact from device mobility and the efficiency influence from the capability computation process. To settle these problems, we proposed a Transient-cluster-based Capability Evaluation Method (TCEM) to evaluate a device’s data forwarding capability. The TCEM can be divided into two steps. The first step aims to reduce computational complexity by evaluating a device’s possibility of contacting the destination within a time constraint based on the transient cluster generated by our proposed Transient Cluster Detection Method (TCDM). The second step is to calculate a device’s probability of directly and indirectly forwarding data to the destination. The probability as a metric of evaluating a device’s forwarding capability can be used in different data forwarding strategies. Simulation results demonstrate that the TCEM-based data forwarding strategy outperforms other data forwarding strategies from the aspect of the proportion of the data delivery ratio to the data redundancy.

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Combining Spatial and Social Awareness in D2D Opportunistic Routing

Cited by 29 publications

References 14 publications

Towards Human-Aware D2D Communication

Towards Human-Aware D2D Communication

NPLP: An Improved Routing-Forwarding Strategy Utilizing Node Profile and Location Prediction for Opportunistic Networks

An Efficient Forwarding Capability Evaluation Method for Opportunistic Offloading in Mobile Edge Computing

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