Data offloading plays an important role for the mobile data explosion problem that occurs in cellular networks. This paper proposed an idea and control scheme for offloading vehicular communication traffic in the cellular network to vehicle to vehicle (V2V) paths that can exist in vehicular ad hoc networks (VANETs). A software-defined network (SDN) inside the mobile edge computing (MEC) architecture, which is abbreviated as the SDNi-MEC server, is devised in this paper to tackle the complicated issues of VANET V2V offloading. Using the proposed SDNi-MEC architecture, each vehicle reports its contextual information to the context database of the SDNi-MEC server, and the SDN controller of the SDNi-MEC server calculates whether there is a V2V path between the two vehicles that are currently communicating with each other through the cellular network. This proposed method: 1) uses each vehicle's context; 2) adopts a centralized management strategy for calculation and notification; and 3) tries to establish a VANET routing path for paired vehicles that are currently communicating with each other using a cellular network. The performance analysis for the proposed offloading control scheme based on the SDNi-MEC server architecture shows that it has better throughput in both the cellular networking link and the V2V paths when the vehicle's density is in the middle.

This paper proposes a predicted k-hop-limited multi-RSU-considered (PKMR) vehicle to vehicle to roadside unit (RSU) (VVR) data offloading method based on the architecture of the Software Defined Network (SDN) controller inside the multi-access edge computing (MEC) server. In the proposed method, a source vehicle that wants to offload data traffic can use a VVR path that connects the source vehicle and the ahead/rear RSU to perform RSU data offloading when the source vehicle approaches the ahead RSU or leaves the rear RSU. Since some RSUs’ signal ranges may overlap, multi-RSU deployment and RSU handoff between signal-overlapping RSUs must be managed to utilize VVR-based RSU data offloading as much as possible. Based on a vehicle’s periodically reported contexts received by the MEC server, the SDN controller inside the MEC server can execute the proposed PKMR method, which adopts (i) the time-extended prediction mechanism to find the potential VVR paths that exist in a coming time period [tc, tc+T] and (ii) a quality function that takes vehicles’ and RSUs’ network conditions into consideration to select the most suitable VVR data offloading path. The performance evaluation results indicate that the proposed PKMR method produces better data offloading performance than the traditional self-offloading method.

This paper proposed a clustering scheme named as the credit-based clustering (CBC) scheme for point of interests’ (POIs’) geo data sharing in vehicular social network (VSN). In the proposed CBC scheme, a number of vehicles that belong to the same VSN can form a cluster to download POIs’ geo data when they are approaching to a new set of POIs. One vehicle is selected as the cluster head to download POIs’ geo data using its cellular network and shares the downloaded POIs’ geo data to its cluster members using IEEE 802.11p network. This paper 1) uses GPS to get vehicles’ locations to calculate the timing of triggering the clustering process, 2) proposes a clustering method to organize a group of vehicles that belong to the same VSN and are proximate with each other for a while during their touring to become a cluster, and 3) deploys a credit scheme to evaluate the credit that the cluster head can get and each cluster member needs to pay to encourage all vehicles to share POIs’ geo data. The simulation results show that the proposed scheme can achieve the goal of fairness, the higher successful ratio of the complete sharing of downloaded POIs’ geo data and the better receiving efficiency.

With the coming wireless mobile networks era and the popular use of P2P applications, how to improve the resource retrieval and discovery for P2P file sharing applications in wireless mobile networks becomes a critical issue. In this paper, we propose a novel network-aware P2P file architecture and related control schemes that can provide continuous resource retrieval and discovery for mobile users over the wireless network environment. The proposed architecture divides a P2P file sharing network into multiple network-aware clusters, in which peers are assigned to a network-aware cluster using a network prefix division. Accordingly, there are two designs for supporting mobile peers to retrieve files in wireless mobile networks. First, a novel file discovery control scheme named mobility-aware file discovery control (MAFDC) scheme is devised to obtain fresh status of shared peers and find the new resource providing peers in wireless mobile networks. Second, a resource provider selection algorithm is devised to enable a mobile peer to select new resource providing peers for continuous file retrieval.

How to measure the centrality of nodes is a significant problem in mobile social networks (MSNs). Current studies in MSNs mainly focus on measuring the centrality of nodes in a certain time interval based on the static graph that do not change over time. However, the network topology of MSNs is changing very rapidly, which is the main characteristic of MSNs. Therefore, it will not be accurate to measure the centrality of nodes in a certain time interval by using the static graph. To solve this problem, this paper first introduces a new centrality metric named cumulative neighboring relationship (CNR) for MSNs. Then, a time-ordered aggregation model is proposed to reduce a dynamic network to a series of time-ordered networks. Based on the time-ordered aggregation model, this paper proposes three particular time-ordered aggregation methods and combines with the proposed centrality metric CNR to measure the importance of nodes in a certain time interval. Finally, extensive trace-driven simulations are conducted to evaluate the performance of our proposed time-ordered aggregation model-based centrality metric time-ordered cumulative neighboring relationship (TCNR). The results show that the exponential time-ordered aggregation method can measure TCNR centrality in a certain time interval more accurately than other aggregation methods, and our proposed time-ordered aggregation model-based centrality metric TCNR outperforms other existing temporal centrality metrics.

This paper proposes a Multi-access Edge Computing (MEC)-based delay-constrained k-hop-limited VANET data offloading method. In the proposed method, when a vehicle X is not in the IEEE 802.11p/802.11bd Road Side Unit (RSU)'s signal coverage, if there is a multiple-hop vehicle-to-vehicle (V2V) path P connecting X with vehicle Y that is inside the RSU's signal coverage, X can launch the vehicle-to-vehicle-to-infrastructure (V2V2I), in which I denotes RSU, VANET data offload from the cellular network to the RSU through P. The MEC paradigm is used to check whether a k-hop-limited V2V2I offloading path exists for vehicle X or not based on the periodically reported contexts from vehicles. In addition to use the V2V2I path's lifetime and quality to derive the suitable V2V2I offloading paths using the snapshot VANET topology on the time point of receiving X's reported context, this work proposed a scheme to derive the potential V2V2I paths that may exist in the future, i.e., in a constrained time interval, to find the better V2V2I offloading path. The performance evaluation shown that the proposed method outperforms the one that (i) only considers the data offloading path's lifetime or (ii) does not consider the candidate V2V2I offloading paths existing in the future.