LSSTCS- A Social-Based DTN Routing in Cooperative Vehicular Sensor Networks

As a cooperative information system, vehicles in Vehicular Sensor Networks delivery messages based on collaboration. Due to the high speed of vehicles, the topology of the network is highly dynamic, and the network may be disconnected frequently. So how to transfer large files in such network is worth considering. In case that the encountering nodes which never meet before flood messages blindly to cause tremendous network overhead. We address this challenge by introducing the Encounter Utility Rank Router(EURR) based on social metrics. EURR includes three cases: Utility Replication Strategy, Lifetime Replication Strategy and SocialRank Replication Strategy. The Lifetime Replication is promising complement to Utility Replication. It enhances the delivery ratio by relaying the copy via the remaining lifetime. Considering network overhead, the SocialRank Replication replicates a copy according to the SocialRank when two communicating nodes do not meet before. The routing mechanism explores the utility of history encounter information and social opportunistic forwarding. The results under the scenario show an advantage of the proposed Encounter Utility Rank Router (EURR) over the compared algorithms in terms of delivery ratio, average delivery latency and overhead ratio

On the performance of social-based and location-aware forwarding strategies in urban vehicular networks

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work evaluates the performance of several location-based and social-aware forwarding schemes through emulations and in a real scenario. Gateway Location Awareness (GLA), a location-aware ranking classification, makes use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns. Aging Social-Aware Ranking (ASAR) exploits the social behavior of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles. To merge both location and social aforementioned algorithms, a HYBRID approach emerges, thus generating a more intelligent mechanism. For each strategy, we evaluate the influence of several parameters in the network performance, as well as we comparatively evaluate the strategies in different scenarios. Experiment results, obtained both in emulated (with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network) and real scenarios, show the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of mobile networks as a smart city network infrastructure.publishe

Congestion aware forwarding in delay tolerant and social opportunistic networks

We propose an approach for opportunistic forwarding that supports optimization of multipoint high volume data flow transfer while maintaining high buffer availability and low delays. This paper explores a number of social, buffer and delay heuristics to offload the traffic from congested parts of the network and spread it over less congested parts of the network in order to keep low delays, high success ratios and high availability of nodes. We conduct an extensive set of experiments for assessing the performance of four newly proposed heuristics and compare them with Epidemic, Prophet, Spay and Wait and Spay and Focus protocols over real connectivity driven traces (RollerNet) and with a realistic publish subscribe filecasting application. We look into success ratio of answered queries, download times (delays) and availability of buffer across eight protocols for varying congestion levels in the face of increasing number of publishers and topic popularity. We show that all of our combined metrics perform better than Epidemic protocol, Prophet, Spray and Wait, Spray and Focus and our previous prototype across all the assessed criteria

A Reliable and Efficient Encounter-Based Routing Framework for Delay/Disruption Tolerant Networks

This article addresses Delay/Disruption Tolerant Networking (DTN) routing under a highly dynamic scenario, envisioned for communication in Vehicular Sensor Networks (VSNs) suffering from intermittent connection. Here, we focus on the design of a high level routing framework, rather than the dedicated encounter prediction. Based on an analyzed utility metric to predict nodal encounter, our proposed routing framework considers the following three cases: 1) Messages are efficiently replicated to a better qualified candidate node, based on the analysed utility metric related to destination. 2) Messages are conditionally replicated if the node with a better utility metric has not been met. 3) Messages are probabilistically replicated if the information in relation to destination is unavailable in the worst case. With this framework in mind, we propose two routing schemes covering two major technique branches in literature, namely Encounter-Based Replication Routing (EBRR) and Encounter-Based Spraying Routing (EBSR). Results under the scenario applicable to VSNs show that, in addition to achieving high delivery ratio for reliability, our schemes are more efficient in terms of a lower overhead ratio. Our core investigation indicates that apart from what information to use for encounter prediction, how to deliver messages based on the given utility metric is also important

Human-mobility-based sensor context-aware routing protocol for delay-tolerant data gathering in multi-sink cell-phone-based sensor networks

Ubiquitous use of cell phones encourages development of novel applications with sensors embedded in cell phones. The collection of information generated by these devices is a challenging task considering volatile topologies and energy-based scarce resources. Further, the data delivery to the sink is delay tolerant. Mobility of cell phones is opportunistically exploited for forwarding sensor generated data towards the sink. Human mobility model shows truncated power law distribution of flight length, pause time, and intercontact time. The power law behavior of inter-contact time often discourages routing of data using naive forwarding schemes. This work exploits the flight length and the pause time distributions of human mobility to design a better and efficient routing strategy. We propose a Human-Mobility-based Sensor Context-Aware Routing protocol (HMSCAR), which exploits human mobility patterns to smartly forward data towards the sink basically comprised of wi-fi hot spots or cellular base stations. The simulation results show that HMSCAR significantly outperforms the SCAR, SFR, and GRAD-MOB on the aspects of delivery ratio and time delay. A multi-sink scenario and single-copy replication scheme is assumed

Congestion control framework for delay-tolerant communications

Detecting and dealing with congestion in delay tolerant networks is an important and challenging problem. Current DTN forwarding algorithms typically direct traffic towards particular nodes in order to maximise delivery ratios and minimise delays, but as traffic demands increase these nodes may become unusable. This thesis proposes Café, an adaptive congestion aware framework that reduces traffic entering congesting network regions by using alternative paths and dynamically adjusting sending rates, and CafRep, a replication scheme that considers the level of congestion and the forwarding utility of an encounter when dynamically deciding the number of message copies to forward. Our framework is a fully distributed, localised, adaptive algorithm that evaluates a contact’s next-hop potential by means of a utility comparison of a number of congestion signals, in addition to that contact’s forwarding utility, both from a local and regional perspective. We extensively evaluate our work using two different applications and three real connectivity traces showing that, independent of the network inter-connectivity and mobility patterns, our framework outperforms a number of major DTN routing protocols. Our results show that both Café and CafRep consistently outperform the state-of-the-art algorithms, in the face of increasing traffic demands. Additionally, with fewer replicated messages, our framework increases success ratio and the number of delivered packets, and reduces the message delay and the number of dropped packets, while keeping node buffer availability high and congesting at a substantially lower rate, demonstrating our framework’s more efficient use of network resources