RESUMEN
Computation offloading is a process that provides computing services to vehicles with computation sensitive jobs. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) is envisioned as a promising solution to perform task executions in vehicular networks using an emerging concept known as vehicle-as-a-resource (VaaR). In VCBV systems, offloading is the primary technique used for the execution of delay-sensitive applications which rely on surplus resource utilization. To leverage the surplus resources arising in periods of traffic congestion, we propose a hybrid VCBV task coordination model which performs the resource utilization for task execution in a multi-hop fashion. We propose an algorithm for the determination of boundary relay vehicles to minimize the requirement of placement for multiple road-side units (RSUs). We propose algorithms for primary and secondary task coordination using hybrid VCBV. Extensive simulations show that the hybrid technique for task coordination can increase the system utility, while the latency constraints are addressed.
RESUMEN
As smartphones and other small portable devices become more sophisticated and popular, opportunities for communication and information sharing among such device users have increased. In particular, since it is known that infrastructure-less device-to-device (D2D) communication platforms consisting only of such devices are excellent in terms of, for example, bandwidth efficiency, efforts are being made to merge their information sharing capabilities with conventional infrastructure. However, efficient multi-hop communication is difficult with the D2D communication protocol, and many conventional D2D communication platforms require modifications of the protocol and terminal operating systems (OSs). In response to these issues, this paper reports on a proposed tree-structured D2D communication platform for Android devices that combines Wi-Fi Direct and Wi-Fi functions. The proposed platform, which is expected to be used with general Android 4.0 (or higher) OS equipped terminals, makes it possible to construct an ad hoc network instantaneously without sharing prior knowledge among participating devices. We will show the feasibility of our proposed platform through its design and demonstrate the implementation of a prototype using real devices. In addition, we will report on our investigation into communication delays and stability based on the number of hops and on terminal performance through experimental confirmation experiments.
RESUMEN
In recent years, Wireless Sensor Networks with a Mobile Sink (WSN-MS) have been an active research topic due to the widespread use of mobile devices. However, how to get the balance between data delivery latency and energy consumption becomes a key issue of WSN-MS. In this paper, we study the clustering approach by jointly considering the Route planning for mobile sink and Clustering Problem (RCP) for static sensor nodes. We solve the RCP problem by using the minimum travel route clustering approach, which applies the minimum travel route of the mobile sink to guide the clustering process. We formulate the RCP problem as an Integer Non-Linear Programming (INLP) problem to shorten the travel route of the mobile sink under three constraints: the communication hops constraint, the travel route constraint and the loop avoidance constraint. We then propose an Imprecise Induction Algorithm (IIA) based on the property that the solution with a small hop count is more feasible than that with a large hop count. The IIA algorithm includes three processes: initializing travel route planning with a Traveling Salesman Problem (TSP) algorithm, transforming the cluster head to a cluster member and transforming the cluster member to a cluster head. Extensive experimental results show that the IIA algorithm could automatically adjust cluster heads according to the maximum hops parameter and plan a shorter travel route for the mobile sink. Compared with the Shortest Path Tree-based Data-Gathering Algorithm (SPT-DGA), the IIA algorithm has the characteristics of shorter route length, smaller cluster head count and faster convergence rate.