Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs)

This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios

Distance-based sensor node localization by using ultrasound, RSSI and ultra-wideband - A comparision between the techniques

Wireless sensor networks (WSNs) have become one of the most important topics in wireless communication during the last decade. In a wireless sensor system, sensors are spread over a region to build a sensor network and the sensors in a region co-operate to each other to sense, process, filter and routing. Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. WSNs have so many applications in different areas such as health-care, monitoring and control, rescuing and military; they all depend on nodes being able to accurately determine their locations. This master’s thesis is focused on distance-based sensor node localization techniques; Received signal strength indicator, ultrasound and ultra-wideband. Characteristics and factors which affect these distance estimation techniques are analyzed theoretically and through simulation the quality of these techniques are compared in different scenarios. MDS, a centralized algorithm is used for solving the coordinates. It is a set of data analysis techniques that display the structure of distance-like data as a geometrical picture. Centralized and distributed implementations of MDS are also discussed. All simulations and computations in this thesis are done in Matlab. Virtual WSN is simulated on Sensorviz. Sensorviz is a simulation and visualization tool written by Andreas Savvides.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Autonomous Gossiping: A self-organizing epidemic algorithm for selective information dissemination in mobile ad-hoc networks

We introduce autonomous gossiping (A/G), a new genre epidemic algorithm for selective dissemination of information in contrast to previous usage of epidemic algorithms which flood the whole network. A/G is a paradigm which suits well in a mobile ad-hoc networking (MANET) environment because it does not require any infrastructure or middleware like multicast tree and (un)subscription maintenance for publish/subscribe, but uses ecological and economic principles in a self-organizing manner in order to achieve its selectivity. The trade-off of using an infrastructure-less self-organizing mechanism like A/G is that it does not guarantee completeness deterministically as is one of the original objectives of alternate selective dissemination schemes like publish/subscribe. We argue that such incompleteness is not a problem in many non-critical real-life civilian application scenarios and realistic node mobility patterns, where the overhead of infrastructure maintenance may outweigh the benefits of completeness, more over, at present there exists no mechanism to realize publish/subscribe or other paradigms for selective dissemination in MANET environments. A/G's reliance and hence vulnerability on cooperation of mobile nodes is also much less as compared to other possible schemes using routing information, since it does not expect node philanthropy for forwarding/carrying information, but only cooperation to the extent that nodes already carrying the information pass it on to other suitable ones. Thus autonomous gossiping is expected to be a light-weight infrastructure-less information dissemination service for MANETs, and hence support any-to-many communication (flexible casting) without the need to establish and maintain separate routing information (e.g., multicast trees)

Social relationship based routing for delay tolerant Bluetooth-enabled PSN communications

PhDOpportunistic networking is a concept derived from the mobile ad hoc networking in which devices have no prior knowledge of routes to the intended destinations. Content dissemination in opportunistic networks thus is carried out in a store and forward fashion. Opportunistic routing poses distinct challenges compared to the traditional networks such as Internet and mobile ad hoc networks where nodes have prior knowledge of the routes to the intended destinations. Information dissemination in opportunistic networks requires dealing with intermittent connectivity, variable delays, short connection durations and dynamic topology. Addressing these challenges becomes a significant motivation for developing novel applications and protocols for information dissemination in opportunistic networks. This research looks at opportunistic networking, specifically at networks composed of mobile devices or, pocket switched networks. Mobile devices are now accepted as an integral part of society and are often equipped with Bluetooth capabilities that allow for opportunistic information sharing between devices. The ad hoc nature of opportunistic networks means nodes have no advance routing knowledge and this is key challenge. Human social relationships are based on certain patterns that can be exploited to make opportunistic routing decisions. Targeting nodes that evidence high popularity or high influence can enable more efficient content dissemination. Based on this observation, a novel impact based neighbourhood algorithm called Lobby Influence is presented. The algorithm is tested against two previously proposed algorithms and proves better in terms of message delivery and delay. Moreover, unlike other social based algorithms, which have a tendency to concentrate traffic through their identified routing nodes, the new algorithm provides a fairer load distribution, thus alleviating the tendency to saturate individual nodes