Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 5 Articles
With the rapid growth of Internet of Things technologies, the management and control of Internet of Things networks\nface remarkable challenges. As such, software-defined networking, which decouples the control layer from data layer,\nresults in various advantages. An association of software-defined networking and Internet of Things, which is referred to\nas software-defined Internet of Things, provides a robust platform to improve the management and control abilities of\nInternet of Things networks. However, these benefits have resulted in an increase in the number of malicious attacks on\nlogically centralized controllers. For that reason, we have performed a specific vulnerability analysis in the link service,\nwhere the controller learns network topology through discovering every link between switches. In addition, we demonstrate\nlink spoofing attacks on the link service, and discuss a hybrid countermeasure to address this security problem...
Border surveillance is one of the high priority in the security of countries around the world. Typical and traditional border\nobservations involve troops and checkpoints at borders, but these do not provide complete security. One effective\nsolution is the addition of smart fencing to enhance surveillance in a Border Patrol system. More specifically, effective\nborder security can be achieved through the introduction of autonomous surveillance and the utilization of wireless sensor\nnetworks. Collectively, these wireless sensor networks will create a virtual fencing system comprising a large number\nof heterogeneous sensor devices. These devices are embedded with cameras and other sensors that provide a continuous\nmonitor. However, to achieve an efficient wireless sensor network, its own security must be assured. This article\nfocuses on the detection of attacks by unknown trespassers (perpetrators) on border surveillance sensor networks. We\nuse both the Dempsterââ?¬â??Shafer theory and the time difference of arrival method to identify and locate an attacked node.\nSimulation results show that the proposed scheme is both plausible and effective....
This article analyzes the clock model of sensor nodes and the basic principle of time synchronization. Then, it systematically\nintroduces the existing time synchronization mechanisms and compares their advantages and disadvantages. In\norder to solve the problems of consensus-based time synchronization, such as propagation delay and convergence\nspeed, Group Consensus Time Synchronization algorithm is proposed. In this protocol, minimum data exchange is\nachieved for both frequency and phase offset compensation, and at the same time, the propagation delay is considered\nand compensated for. The simulation results show that the protocol can adapt to dynamic topology and be suitable to\nlarge-scale wireless sensor networks....
We propose a dynamic energy balanced max flow routing algorithm to maximize load flow within the network lifetime\nand balance energy consumption to prolong the network lifetime in an energy-harvesting wireless sensor network. The\nproposed routing algorithm updates the transmission capacity between two nodes based on the residual energy of the\nnodes, which changes over time. Hence, the harvested energy is included in calculation of the maximum flow. Because\nthe flow distribution of the Fordââ?¬â??Fulkerson algorithm is not balanced, the energy consumption among the nodes is not\nbalanced, which limits the lifetime of the network. The proposed routing algorithm selects the node with the maximum\nresidual energy as the next hop and updates the edge capacity when the flow of any edge is not sufficient for the next\ndelivery, to balance energy consumption among nodes and prolong the lifetime of the network. Simulation results\nrevealed that the proposed routing algorithm has advantages over the Fordââ?¬â??Fulkerson algorithm and the dynamic max\nflow algorithm with respect to extending the load flow and the lifetime of the network in a regular network, a smallworld\nnetwork, and a scale-free network....
Global positioning system is an important space information network for location service. However, the satellite signal is\nattenuated or even blocked in indoor environment. To make up for the defect of global positioning system, IEEE 802.11\nwireless local area network (Wi-Fi) infrastructure is proposed to relay the task of positioning in indoor scenario with\ntheir superiority of being widely deployed. In Wi-Fi indoor localization application, received signal strength indicator is\nthe most prevalent parameter. From the former literature, it was supposed to be Gaussian distribution. In this article,\nwe first formulate the received signal in a multipath channel based on the IEEE 802.11 standard, then we derive the probability\ndensity function of received signal strength indicator and it is proved to be a noncentral Chi-square distribution.\nFinally, the correctness of the distribution is verified from the aspect of noncentral parameter in an experiment system....
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