Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propose a distributed clustering algorithm based on the group mobility and a revised group mobility metric which is derived from the instantaneous speed and direction of nodes. Our dynamic, distributed clustering approach use Gauss Markov group mobility model for mobility prediction that enables each node to anticipate its mobility relative to its neighbors. In particular, it is suitable for reflecting group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. We also take the residual energy of nodes and the number of neighbor nodes into consideration. The proposed clustering scheme aims to form stable clusters by reducing the clustering iterations even in a highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two wellknown clustering approaches, the MOBIC and DGMA, in terms of average number of clusterhead changes.

No abstract

No abstract

The development of modern vehicles equipped with various sensors and wireless communication has been the impetus for vehicular crowdsensing applications, which can be used to complete large-scale and complex social sensing tasks such as monitoring road surfaces condition. However, most of the sensing tasks are closely related with specific location and required to be performed in certain area, and in this article, we have proved these kind of location-based optimal task assignment to be an NP-hard (non-deterministic polynomial-time hard) problem. To solve this challenge, we first establish mathematical model of multi-vehicle collaborative task assignment problem, considering vehicle's time budget constraint, location, and multiple requirements of sensing tasks. And we propose an approximation location-based task assignment mechanism for it, which is composed of two parts: the first part is to determine the allocating order among engaged vehicles and the second part is to schedule optimal sensing path for single vehicle, which in this article we propose an optimal sensing path scheduling algorithm to finish this task. Using Lingo software, we prove the efficiency of the proposed optimal sensing path scheduling algorithm. Extensive simulation results also demonstrate correctness and effectiveness of our approach.

Resending Interest packets to support consumer mobility in Named Data Networking usually leads to latency, which can severely affect the performance of applications with strict latency requirements. We propose a proactive multi-level cache selection scheme to enhance the consumer mobility support in Named Data Networking. This scheme focuses on a handover situation, wherein the consumer disconnects from a network or moves to another access router before receiving the requested content. The proactive multi-level cache selection is designed to prefetch contents into a subset of the neighboring routers, to achieve a trade-off between the latency and cache cost. In particular, this approach is feasible for a two-level router structure and scalable to a multi-level structure. The results obtained from simulation show that compared with equivalent schemes such as the original no-prefetch scheme in the Named Data Networking and the all-prefetch scheme, proactive multi-level cache selection for a two-level router structure can provide a better handover performance to some extent; the user experience is improved and the negative impacts of consumer mobility are alleviated.