Lingguo Cui

A low energy intelligent clustering protocol for wireless sensor networks

LEACH (low-energy adaptive clustering hierarchy) is a well-known self-organizing, adaptive clustering protocol of wireless sensor networks. However it has some shortcomings when it faces such problems as the cluster construction and energy management. In this paper, LEICP (low energy intelligent clustering protocol), an improvement of the LEACH protocol is proposed to overcome the shortcomings of LEACH. LEICP aims at balancing the energy consumption in every cluster and prolonging the network lifetime. A fitness function is defined to balance the energy consumption in every cluster according to the residual energy and positions of nodes. In every round the node called auxiliary cluster-head calculates the position of the cluster-head using Bacterial Foraging Optimization Algorithm (BFOA). After aggregating the data received, the cluster-head node decides whether to choose another cluster-head as the next hop for delivering the messages or to send the data to the base station directly, using Dijkstra algorithm to compute an optimal path. The performance of LEICP is compared with that of LEACH. Simulation results demonstrate that LEICP can prolong the lifetime of the sensor network by about 62.28% compared with LEACH and acquire uniform number of cluster-heads and messages in the network.

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.

Tree-based Delay Guaranteed and Energy Efficient MAC Protocol for Wireless Sensor Networks

TDGEE is a cross-layered TDMA-based MAC protocol, which is energy efficient and delay guaranteed based on the tree topology. The protocol first enables the network to construct an energy-balanced tree topology. Then each node collects its local topology information in one hop. A distributed time slot assignment algorithm determines when each node should transmit and receive the data packets. The nodes can be sychronized by coordination messages broadcasted from the sink. Since the propocol, which uses distributed algorithm, has the adjustment mechanism, the TDGEE network is highly scalable and flexible. The performance of TDGEEis compared with existing protocols by simulations. For the network application we considering, the TDGEE network provides bounded delay guarantee and longer network lifetime than other protocols, making sensor network economically viable.

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.

Distributed model predictive control of linear systems with unmeasurable states and uncertain parameters

Distributed model predictive control (DMPC) is widely used in complex industrial process control. The theoretical researches of DMPC have got more and more attention because of its good performances, such as the ability of dealing with all kinds of constraints effectively, high flexibility and fault tolerance. In this paper, the linear systems with uncertain parameters and unmeasurable states are confirmed by generalized polynomial chaos expansion method. Then the DMPC algorithm is realized by using the state observers to estimate states.

Percolation thresholds on tree-based communities of wireless sensor networks

Many efficient deployments of large-scale wireless sensor networks based on the tree-based community rise into view recently. Sensor nodes are severely resource constrained, and lack sophisticated defense mechanisms to fight virus attacks. Cyber viruses spread through node populations over the networks, and a number of results about the prevalence have been derived in recent years by exploiting epidemic behaviors and the percolation processes on networks. A network model based on the Cayley tree is proposed to depict the underlying tree-based architectures of the network and the community. The percolation thresholds are calculated and analyzed in two cases. Due to random links in the communities, the sensor virus extends drastically on the network. The analysis and evaluation shows that the percolation threshold keeps decreasing with the increase of the shortcut probability. There is the smallest percolation threshold in a random network, where the virus easily attacks the network from one side to another. The conclusions can further our understanding of epidemic dynamics on tree-based communities of wireless sensor networks.

Percolation Thresholds on Tree-Based Communities of Wireless Sensor Networks

Many efficient deployments of large-scale wireless sensor networks based on the tree-based community rise into view recently. Sensor nodes are severely resource constrained, and lack sophisticated defense mechanisms to fight virus attacks. Cyber viruses spread through node populations over the networks, and a number of results about the prevalence have been derived in recent years by exploiting epidemic behaviors and the percolation processes on networks. A network model based on the Cayley tree is proposed to depict the underlying tree-based architectures of the network and the community. The percolation thresholds are calculated and analyzed in two cases. Due to random links in the communities, the sensor virus extends drastically on the network. The analysis and evaluation shows that the percolation threshold keeps decreasing with the increase of the shortcut probability. There is the smallest percolation threshold in a random network, where the virus easily attacks the network from one side to another. The conclusions can further our understanding of epidemic dynamics on tree-based communities of wireless sensor networks.

Epidemic Analyses on 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. Due to link additions, small world phenomena exist in tree topologies. Epidemics on small worlds of tree topologies are studied, and we calculate the percolation threshold at which the outbreak of the epidemic takes place. Compared with Cayley tree, the small world has a smaller percolation threshold suffering from the epidemic. In the further study, Epidemiological processes are observed by simulations. The infection extends exponentially with time for the existence of shortcuts.