Search

Simulation Model Portability standard 2 (SMP2) is a successful simulation model reuse standard in European Space Agency. Discrete Event System Specification (DEVS) is one of most common and powerful simulation model formalisms. Transforming a simulation model from DEVS representation to SMP2 representation is of great significance for model reuse. A Model Driven Architecture (MDA) based transformation methodology is presented first. According to this methodology, a MOF based DEVS metamodel is created and the mappings from DEVS metamodel to SMP2 metamodel are established. A transformation model from DEVS to SMP2 is described based on Query/View/Transformation (QVT) language. An illustrative example is detailed to demonstrate the application of the transformation model. The transformation model’s further application strategy and our next work are discussed in the final part.

To improve the time performance of SMP2 Simulation under Service Oriented Architecture, a hybrid time synchronization method is promoted. The method would reduce the redundancy message exchange via specifying different time advance policy to different SMP2 simulation node. An interaction graph model is introduced based on SMP2 model composition and schedule and the parallelity evaluating algorithm is propounded, based on which a time synchronization policy determination method is detailed. Finally, a simulation experiment is given to make comparison between the hybrid method and the traditional methods.

The Particle Swarm Optimization (PSO) algorithm is already proved efficient in the rule extraction in intrusion detection. But in practice the most intrusion detection systems often have a high false alarm rate. To solve it, this paper gives a new PSO-based algorithm which has a special fitness function to extract better rules set with lower false alarm rate to detect the attacks. Experiments based on the 1999 KDD cup data show that the algorithm can efficiently lower the false alarm rate.

Based on the changeability of wireless network circumstance, information packets are prone to lose in the process of the wireless transmission. In wireless broadcasting ,any node of multi-nodes request retransmission information packets .This paper presents a novel retransmission approach in wireless broadcasting based on network coding (NCWBR), whose key idea is to combine different lost packets with network coding to achieve retransmission. Theoretical analysis reveals that our approach can ensure the solvability in the received nodes, and have better retransmission performance. Simulation results indicate that compared with existing approach, the approach in this paper can effectively reduce the average number of transmissions and advance the transmission efficiency.

In environments where sensor networks are used to monitor sensitive objects or valuable assets, attackers may use the method of hop-by-hop backtracking to find out the protected objects. This paper proposes a new source protected protocol in WSN, the phantom routing with locational angle (PRLA). In PRLA, inclination angles are introduced and used to direct random walks, which avoids choosing paths harmful to the privacy of source location. Simulation results show that, compared to the phantom single-path routing protocol proposed in the literature, PRLA improves the safety period by up to 50% with minor increase in energy overhead.

A new anonymous communication mechanism DDACM (delay demand-based anonymous communication mechanism) has been proposed in this paper. In DDACM, rerouting path can be setup according to the initiator's delay-demand, which can guarantee the initiator's anonymity. Hops between two peers are used as the metric of choosing rerouting node. Peers can find hops to neighbors through broadcasting TTL packets and record the neighbors with n hops as its n-hops group. Initiator's delay demand is described as DelayD being carried in the rerouting request. Every node receiving the request will randomly choose a neighbor from its 0 to DelayD-hops groups and forward the request with probability Pf. So, the delay between every two forwarders will be limited in DelayD hops and the total delay of rerouting path will be decreased. The anonymity and delay performance of DDACM have analyzed in this paper. Mathematic analysis and simulation results indicate that DDACM can keep almost the same anonymity with the typical anonymous communication system Crowds and decrease the transmit delay obviously.

The quality of network services is directly affected by QoS routing algorithms, and QoS routing algorithms rely heavily on network state information specifying the resource availability at network nodes and links. In practice, the network state information is not always accurate because it does not update in time. This paper proposes a randomized QoS routing algorithm on networks with inaccurate link-state information, and develops a simulation environment. Our algorithm reduces computational cost and protocol overhead. Experimental tests demonstrate that our algorithm performs very well in practice.

A frequent item of a data stream is a data point whose occurrence frequency is above a given threshold. Finding frequent item of data stream has wide applications in various fields, such as network traffic monitor, data stream OLAP and data stream mining, etc. In data stream model, the algorithm can only scan the data in one pass and the available memory space is very limited relative to the volume of a data stream, therefore it is usually unable to find all the accurate frequent items of a data stream. This paper proposes a novel algorithm to find e-approximate frequent items of a data stream, its space complexity is O(e −1 ) and the processing time for each item is O(1) in average. Moreover, the frequency error bound of the results returned by the proposed algorithm is e(1−s+e)N.

It is very important in a lot of applications to forecast future trend of data streams. For example, using predictive queries to a sensor network for monitoring environment, observers can forecast future average temperature and humidity in the area covered by the network to determine abnormal events. Recent works on query processing over data streams mainly focused on approximate queries over newly arriving data. To the best of the knowledge, there is nothing to date in the literature on predictive query processing over data streams. Adopting multivariable linear regression, a predictive mathematical model for forecasting the aggregate value over data streams is first proposed. Then, based on the model, a predictive aggregate query processing method over data streams is proposed in the paper. When the frequency of forecast failing is greater than a predefined threshold, an adaptive strategy for the predictive mathematical model is proposed. A mathematical model that characterizes the affects of the updating cycle of sliding window and data stream rate on predictive accuracy is also presented. Analytical and experimental results show that the proposed method is very effective, and the proposed algorithms have higher performance and provide better prediction of aggregate values over data streams to users. In experiments the TPC-H data and ocean air temperature data measured by TAO (tropical atmosphere ocean) are used to construct data streams.