A macro-level model for investigating the effect of directional bias on network coverage

Random walks have been proposed as a simple method of efficiently searching, or disseminating information throughout, communication and sensor networks. In nature, animals (such as ants) tend to follow correlated random walks, i.e., random walks that are biased towards their current heading. In this paper, we investigate whether or not complementing random walks with directional bias can decrease the expected discovery and coverage times in networks. To do so, we develop a macro-level model of a directionally biased random walk based on Markov chains. By focussing on regular, connected networks, the model allows us to efficiently calculate expected coverage times for different network sizes and biases. Our analysis shows that directional bias can significantly reduce coverage time, but only when the bias is below a certain value which is dependent on the network size.Comment: 15 page

A Search Strategy of Level-Based Flooding for the Internet of Things

This paper deals with the query problem in the Internet of Things (IoT). Flooding is an important query strategy. However, original flooding is prone to cause heavy network loads. To address this problem, we propose a variant of flooding, called Level-Based Flooding (LBF). With LBF, the whole network is divided into several levels according to the distances (i.e., hops) between the sensor nodes and the sink node. The sink node knows the level information of each node. Query packets are broadcast in the network according to the levels of nodes. Upon receiving a query packet, sensor nodes decide how to process it according to the percentage of neighbors that have processed it. When the target node receives the query packet, it sends its data back to the sink node via random walk. We show by extensive simulations that the performance of LBF in terms of cost and latency is much better than that of original flooding, and LBF can be used in IoT of different scales

On the coverage process of random walk in wireless ad hoc and sensor networks

Random walk (RW) is simple to implement and has a better termination control. The Markov chain analysis informs that RW eventually visits all vertices of a connected graph. Due to such nice properties, RW is often proposed for information dissemination or collection from all or part of a large scale unstructured network. The random walker, which can be used to disseminate or collect information, visits the nodes while selecting randomly one of the neighbors. The selection of neighbors is effected by the neighbor density or the connectivity degree of the nodes. The connectivity degree in turn depends on the radius of transmission of wireless nodes. In this paper we studied the coverage process of the RW on random geometric graph. The random geometric graphs are often considered as a model for wireless ad hoc and sensor networks. We defined and studied a metric called "attenuation" that indicates how fast a RW can move in the network while disseminating or collecting information. We showed that attenuation depends on the topology, the number of nodes in a network and the transmission radius of the nodes. We then studied the effect of attenuation on the RW coverage process analytically and through simulations and showed that attenuation is the normalized estimated search time of the network. In the end we applied the results obtained to show that the estimated search time in random geometric graphs is proportional to the reciprocal of the number of replicated targets. ©2010 IEEE