Current Issue : July - September Volume : 2020 Issue Number : 3 Articles : 5 Articles
Software defined networking (SDN) has been adopted in many application domains as it provides functionalities to dynamically\ncontrol the network flow more robust and more economical compared to the traditional networks. In order to\nstrengthen the security of the SDN against cyber attacks, many security solutions have been proposed. However, those\nsolutions need to be compared in order to optimize the security of the SDN. To assess and evaluate the security of the SDN\nsystematically, one can use graphical security models (e.g., attack graphs and attack trees). However, it is difficult to provide\ndefense against an attack in real time due to their high computational complexity. In this paper, we propose a real-time\nintrusion response in SDN using precomputation to estimate the likelihood of future attack paths from an ongoing attack. We\nalso take into account various SDN components to conduct a security assessment, which were not available when addressing\nonly the components of an existing network. Our experimental analysis shows that we are able to estimate possible attack paths\nof an ongoing attack to mitigate it in real time, as well as showing the security metrics that depend on the flow table, including\nthe SDN component. Hence, the proposed approach can be used to provide effective real-time mitigation solutions for\nsecuring SDN....
At present, precision agriculture and smart agriculture are the hot topics, which are based on the efficient data collection by using\nwireless sensor networks (WSNs). However, agricultural WSNs are still facing many challenges such as multitasks, data quality, and\nlatency. In this paper, we propose an efficient solution for multiple data collection tasks exploiting edge computing-enabled wireless\nsensor networks in smart agriculture. First, a novel data collection framework is presented by merging WSN and edge computing.\nSecond, the data collection process is modeled, including a plurality of sensors and tasks. Next, according to each specific task and\ncorrelation between task and sensors, on the edge computing server, a double selecting strategy is established to determine the best\nnode and sensor network that fulfills quality of data and data collection time constraints of tasks. Furthermore, a data collection\nalgorithm is designed, based on set values for quality of data. Finally, a simulation environment is constructed where the\nproposed strategy is applied, and results are analyzed and compared to the traditional methods. According to the comparison\nresults, the proposal outperforms the traditional methods in metrics....
The appearance of coverage holes in the network leads to transmission links being disconnected, thereby resulting in decreasing the\naccuracy of data. Timely detection of the coverage holes can effectively improve the quality of network service. Compared with\nother coverage hole detection algorithms, the algorithms based on the Rips complex have advantages of high detection accuracy\nwithout node location information, but with high complexity. This paper proposes an efficient coverage hole detection\nalgorithm based on the simplified Rips complex to solve the problem of high complexity. First, Turanâ??s theorem is combined\nwith the concept of the degree and clustering coefficient in a complex network to classify the nodes; furthermore, redundant\nnode determination rules are designed to sleep redundant nodes. Second, according to the concept of the complete graph,\nredundant edge deletion rules are designed to delete redundant edges. On the basis of the above two steps, the Rips complex is\nsimplified efficiently. Finally, from the perspective of the loop, boundary loop filtering and reduction rules are designed to\nachieve coverage hole detection in wireless sensor networks. Compared with the HBA and tree-based coverage hole detection\nalgorithm, simulation results show that the proposed hole detection algorithm has lower complexity and higher accuracy and\nthe detection accuracy of the hole area is up to 99.03%....
Wireless sensor networks are widely used in many fields, such as medical and health care,military monitoring, target tracking, and\npeopleâ??s life, because of their advantages of convenient deployment, low cost, and good concealment. However, due to the low\nbattery capacity of sensor nodes and environmental changes, the energy consumption of nodes is serious and the accuracy of data\ncollection is low. In the data collection method of multiple random paths, due to the uneven geographical distribution between\nnodes and the influence of the environment, it is easy to cause the communication between nodes to be blocked and the\nconstruction of random paths to fail.This paper proposes an efficient data collection algorithm for this problem. The algorithm is\nimproved on the basis of the random node selection algorithm.This method can effectively avoid the failure of random path node\nselection and improve the node selection of random path in wireless sensor networks. Then, the sensor network in the dynamic\nenvironment is analyzed based on the static environment. An efficient data collection algorithm based on the position prediction\nof extreme learning machines is proposed.This method uses extreme learning machine methods to perform trajectory prediction\nfor nodes in a dynamic environment....
Wireless Sensor Networks (WSNs) are increasingly involved in many applications. However, communication overhead and\nenergy efficiency of sensor nodes are the major concerns in WSNs. In addition, the broadcast communication mode of WSNs\nmakes the network vulnerable to privacy disclosure when the sensor nodes are subject to malicious behaviours. Based on the\nabovementioned issues, we present a Queries Privacy Preserving mechanism for Data Aggregation (QPPDA) which may reduce\nenergy consumption by allowing multiple queries to be aggregated into a single packet and preserve data privacy effectively by\nemploying a privacy homomorphic encryption scheme. The performance evaluations obtained from the theoretical analysis and\nthe experimental simulation show that our mechanism can reduce the communication overhead of the network and protect the\nprivate data from being compromised....
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