Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

DAWN: A Density Adaptive Routing for Deadline-Based Data Collection in Vehicular Delay Tolerant Networks

Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.

Trust routing algorithm based on multiple decision factor theory in OSPF protocol

This paper presents a novel trust model based on multiple decision factor theory （MDFT） and a trust routing algorithm based on MDFT to exactly evaluate routing node trust and establish a trustworthy routing path. MDFT integrates four dimensional trust decision factors including behavior, state, recommend and node liveness to realize an exactly finer-grained trust evaluation. On the basis of MDFT, a trust routing algorithm is presented and validated in open shortest path first （OSPF） protocol. Simulation resuRs show that the algorithm can reflect the routing node trust accurately and has better dynamic response ability. Under the circumstance of existing deceptive nodes, the algorithm has better anti-deception performance and higher attack node detection rate than conventional algorithm.

Connectivity of wireless sensor networks for plant growth in greenhouse

Wireless sensor networks have been applied in farmland and greenhouse.However,poor connectivity always results in a lot of nodes isolation in the network in a scenario.For this reason,the network connectivity is worth considering to improve its quality,especially when the collected data cannot be sent to the data center because of the obstacles such as the growth of crop plants and weeds.Therefore,how to reduce the effect of crop growth on network connectivity,and enable the reliable transmission of field information,are the key problems to be resolved.To solve these problems,the method which adds long distance routing nodes to the WSN to reduce the deterioration of WSN connectivity during the growth of plants was proposed.To verify this method,the network connectivity of the deployed WSN was represented by the rank of connection matrix based on the graph theory.Consequently,the rank with value of 1 indicates a fully connected network.Moreover,the smaller value of rank means the better connectedness.In addition,the network simulator NS2 simulation results showed that the addition of long-distance backup routing nodes can improve the network connectivity.Furthermore,in experiments,using ZigBee-based wireless sensor network,a remote monitoring system in greenhouse was established,which can obtain environmental information for crops,e.g.temperature,humidity,light intensity and other environmental parameters as well as the wireless link quality especially.Experimental results showed adding of long-distance backup routing nodes can guarantee network connectivity in the region where received signal strength indication(RSSI)was poor,i.e.RSSI value was less than−100 dBm,and the energy was low.In conclusion,this method was essential to improve the connectivity of WSN,and the optimized method still needs further research.

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.