Immunizations on small worlds of tree-based wireless sensor networks

The sensor virus is a serious threat,as an attacker can simply send a single packet to compromise the entire sensor network.Epidemics become drastic with link additions among sensors when the small world phenomena occur.Two immunization strategies,uniform immunization and temporary immunization,are conducted on small worlds of tree-based wireless sensor networks to combat the sensor viruses.With the former strategy,the infection extends exponentially,although the immunization effectively reduces the contagion speed.With the latter strategy,recurrent contagion oscillations occur in the small world when the spatial-temporal dynamics of the epidemic are considered.The oscillations come from the small-world structure and the temporary immunization.Mathematical analyses on the small world of the Cayley tree are presented to reveal the epidemic dynamics with the two immunization strategies.

Dynamics in small worlds of tree topologies of wireless sensor networks

Tree topologies, which construct spatial graphs with large characteristic path lengths and small clustering coefficients, are ubiquitous in deployments of wireless sensor networks. Small worlds are investigated in tree-based networks. Due to link ad- ditions, characteristic path lengths reduce rapidly and clustering coefficients increase greatly. A tree abstract, Cayley tree, is con- sidered for the study of the navigation algorithm, which runs auto- matically in the small worlds of tree-based networks. In the further study, epidemics in the small worlds of tree-based wireless sen- sor networks on the large scale are studied, and the percolation threshold is calculated, at which the outbreak of the epidemic takes place. Compared with Cayley tree, there is a smaller percolation threshold suffering from the epidemic.

Q-Learning Based Routing Protocol for Congestion Avoidance

The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.

EPIDEMICS ON SMALL WORLDS OF TREE-BASED WIRELESS SENSOR NETWORKS

Due to link additions,small world phenomena exist in tree-based wireless sensor networks.Epidemics on small worlds of tree-based networks are studied,and the epidemic threshold at which the outbreak of the epidemic occurs is calculated.Epidemiological processes are analyzed when the infection probability is larger than the percolation threshold.Although different epidemiological processes occur on the underlying tree topology,the number of infected nodes increases exponentially as the infection spreads.The uniform immunization procedure is conducted in the homogeneous small-world network.The infection still extends exponentially although the immunization effectively reduces the prevalence speed.