The main objective of the VANET networks is to improve road safety as well as transport efficiency through the use of communications technology and the emergence of wireless devices at low cost. Thus, the design of an efficient dissemination protocol, that informs vehicles about interesting safety and nonsafety events, is of paramount importance. The thriving challenge would be to maximize the delivery ratio by avoiding as far as possible the broadcast storm problem. A scrutiny of the literature wealthy number of approaches highlights that all of them fail to fulfill with the "sine qua non" requirements that we introduce. In this paper and to palliate this shortage, we introduce a new infrastructure-less geocast approach, called DPMS (data Dissemination Protocol based on Map Splitting). The main thrust of DPMS stands in reaching a high delivery ratio as well as a high geocast precision by sending messages only to vehicles in the Zone of Relevance (ZOR) with a minimum overhead cost. Carried out experiments showed that DPMS outperforms its competitors in terms of effectiveness and efficiency.

P2P networking has grasped an increasing interest worldwide among both researchers and computer networking professionals. As a witness, several P2P applications mainly used for file sharing over the Internet have been proposed. Considering the great success of mobile devices in recent years, P2P applications have also been deployed over mobile networks such as mobile ad-hoc networks (MANETs). However, the mismatch between the P2P overlay and the MANET underlay topologies makes the resources lookup mechanism in mobile P2P applications very difficult. Therefore, this downside is the main hindrance to the deployment of such applications over MANETs. To overcome the mismatch issue, we propose in this paper RLSM-P2P a cross-layer resource lookup scheme for Mobile P2P applications. The main thrust of RLSM-P2P consists of building an efficient unstructured P2P overlay that closely matches the underlay physical network and swiftly adapts to its volatility and dynamicity by considering different MANET constraints. Furthermore, RLSM-P2P relies on a query learning resource lookup mechanism for locating pertinent resources to user queries. The performed experiments show that RLSM-P2P outperforms its competitors in terms of effectiveness and efficiency.

Peer-to-peer (P2P) systems have offered users an efficient way to share various resources and access diverse services over the Internet. In unstructured P2P systems, resource storage and indexation are fully distributed among participating peers. Therefore, locating peers sharing pertinent resources for a specific user query is a challenging issue. In fact, effective query routing requires smart decisions to select a certain number of peers with respect to their relevance for the query instead of choosing them at random. In this respect, we introduce here a new query-oriented approach, called the reinforcement learning-based query routing approach (RLQR). The main goal of RLQR is to reach high retrieval effectiveness as well as a lower search cost by reducing the number of exchanged messages and contacted peers. To achieve this, the RLQR relies on information gathered from previously sent queries to identify relevant peers for forthcoming queries. Indeed, we formulate the query routing issue as the reinforcement learning problem and introduce a fully distributed approach for addressing it. In addition, RLQR addresses the well-known cold-start issue during the training stage, which allows it to improve its retrieval effectiveness and search cost continuously, and, therefore, goes quickly through the cold-start phase. Performed simulations demonstrate that RLQR outperforms pioneering query routing approaches in terms of retrieval effectiveness and communications cost.