Current Issue : April - June Volume : 2020 Issue Number : 2 Articles : 5 Articles
Wireless sensor networks (WSNs) have great potential for numerous domains of application because of their ability to sense and\nunderstand unattended environments. However, a WSN is subject to various attacks due to the openness of the public wireless\nchannel. Therefore, a secure authentication mechanism is vital to enable secure communication within WSNs, and many studies\non authentication techniques have been presented to build robust WSNs. Recently, Lu et al. analyzed the security defects of the\nprevious ones and proposed an anonymous three-factor authenticated key agreement protocol for WSNs. However, we found that\ntheir protocol is vulnerable to some security weaknesses, such as the offline password guessing attack, known session-specific\ntemporary information attack, and no session key backward secrecy. We propose a lightweight security-improved three-factor\nauthentication scheme for WSNs to overcome the previously stated weaknesses. In addition, the improved scheme is proven to be\nsecure under the random oracle model, and a formal verification is conducted by ProVerif to reveal that the proposal achieves the\nrequired security features. Moreover, the theoretical analysis indicates that the proposal can resist known attacks. A comparison\nwith related works demonstrates that the proposed scheme is superior due to its reasonable performance and additional\nsecurity features....
Recent technological advances in both air sensing technology and Internet of Things (IoT)\nconnectivity have enabled the development and deployment of remote monitoring networks of air\nquality sensors. The compact size and low power requirements of both sensors and IoT data loggers\nallow for the development of remote sensing nodes with power and connectivity versatility. With\nthese technological advancements, sensor networks can be developed and deployed for various\nambient air monitoring applications. This paper describes the development and deployment of a\nmonitoring network of accurate ozone (O3) sensor nodes to provide parallel monitoring in an air\nmonitoring site relocation study. The reference O3 analyzer at the station along with a network of\nthree O3 sensing nodes was used to evaluate the spatial and temporal variability of O3 across four\nSouthern California communities in the San Bernardino Mountains which are currently represented\nby a single reference station in Crestline, CA. The motivation for developing and deploying the\nsensor network in the region was that the single reference station potentially needed to be relocated\ndue to uncertainty that the lease agreement would be renewed. With the implication of siting a new\nreference station that is also a high O3 site, the project required the development of an accurate and\nprecise sensing node for establishing a parallel monitoring network at potential relocation sites. The\ndeployment methodology included a pre-deployment co-location calibration to the reference\nanalyzer at the air monitoring station with post-deployment co-location results indicating a mean\nabsolute error (MAE) < 2 ppb for 1-h mean O3 concentrations. Ordinary least squares regression\nstatistics between reference and sensor nodes during post-deployment co-location testing indicate\nthat the nodes are accurate and highly correlated to reference instrumentation with R2 values > 0.98,\nslope offsets < 0.02, and intercept offsets < 0.6 for hourly O3 concentrations with a mean\nconcentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for\ndiurnal O3 trends was found between locations within 5 km of each other with spatial variability\nbetween sites more pronounced during nighttime hours. The parallel monitoring was successful in\nproviding the data to develop a relocation strategy with only one relocation site providing a 95%\nconfidence that concentrations would be higher there than at the current site....
Fault tolerance is an important aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability\nand high reliability in different environments. The beginning of software-defined networking (SDN) has both presented new\nchallenges and opened a new era to develop new strategies, standards, and architectures to support fault tolerance. In this paper, a\nstudy of fault tolerance is performed for two architectures: (1) a single master with multiple slave controllers and (2) multiple slave\ncontrollers. The proposed model is called a Generic Controller Adaptive Load Balancing (GCALB) model for SDNs. GCALB\nadapts the load among slave controllers based on a GCALB algorithm. Mininet simulation tool is utilized for the experimentation\nphase. Controllers are implemented using floodlights. Experiment results were conducted using GCALB when master controller is\ntaking the responsibility of distributing switches among four and five slave controllers as a case study. Throughput and response\ntime metrics are used to measure performance. GCALB is compared with two reference algorithms: (1) HyperFlow (Kreutz et al.,\n2012), and (2) Enhanced Controller Fault Tolerant (ECFT) (Aly and Al-anazi, 2018). Results are promising as the performance of\nGCALB increased by 15% and 12% when compared to HyperFlow and by 13% and 10% when compared to ECFT in terms of\nthroughput and response time....
Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical\nnetwork. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although\nSDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be\nfixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural\nnetwork (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within\nSDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally.\nExperiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%,\nrespectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition,\nthe experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with\nother options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising\napproach for intrusion detection in SDN environments....
Software-Defined Networking (SDN) has opened a promising and potential approach\nfor future networks, which mostly requires the low-level configuration to implement different\ncontrols. With the high advantages of SDN by decomposing the network control plane from the data\nplane, SDN has become a crucial platform to implement Internet of Things (IoT) services. However,\na static SDN controller placement cannot obtain an efficient solution in distributed and dynamic\nIoT networks. In this paper, we investigate an optimization framework under a well-known theory,\nnamely submodularity optimization, to formulate and address different aspects of the controller\nplacement problem in a distributed network, specifically in an IoT scenario. Concretely, we develop a\nframework that deals with a series of controller placement problems from basic to complicated use\ncases. Corresponding to each use case, we provide discussion and a heuristic algorithm based on\nthe submodularity concept. Finally, we present extensive simulations conducted on our framework.\nThe simulation results show that our proposed algorithms can outperform considered baseline\nmethods in terms of execution time, the number of controllers, and network latency....
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