Current Issue : April - June Volume : 2015 Issue Number : 2 Articles : 6 Articles
In recent years, wireless sensor networks have been widely used in data acquisition, surveillance, event monitoring, and so forth.\nTopology control is an important issue in designing sensor networks. Considering the uncertainty of distance between nodes, a\ndistributed topology control algorithm named as LRMST, which is based on the local minimum spanning tree (LMST) algorithm,\nis proposed by applying the 0-1 robust discrete optimization theory. Firstly,when only the cost coefficients are subject to uncertainty,\nit is proved that the robust counterpart of the 0-1 discrete optimization problem on n variables can be solved by solving at most\nn + 1 ? ? deterministic problems, where ? denotes the number of cost coefficients which change in an uncertainty set. Then we\npresent a robust model for the MST problem under distance uncertainty. According to the proved conclusion, an algorithm is\nproposed to obtain the robust solution of uncertain MST problem by solving only one deterministic MST problem, after which\nLRMST algorithm is designed when the distance between nodes is affected by uncertainty. Simulation results show that LRMST\nalgorithm tends to select some edges whose estimated distance is slightly longer and obtains the robustness when the distance is\nuncertain at the expense of less optimal value compared with LMST algorithm....
The partially stabilized zirconia (PSZ) ceramic has wide applications due to its excellent mechanical toughness and chemically\ninert and electrical properties for fabricating various devices. In this paper, a novel high temperature pressure sensor with the\nPSZ was designed and fabricated. The sensor was designed based on the small deflection theory, which enables its theoretic\npressure-capacitance capability up to 60 bar. HTCC process technology was used to fabricate the sensor, which would realize a\ncompletely passive LC resonant circuit integrated on the ceramic substrate. According to the coupling principle, non contact testing\nis achieved using the designed readout system, with average sensitivity up to 38 kHz Bar?1 presented. Compared to the fabrication\nand measurement of traditional sensors, excellent packaging process is demonstrated, and the sensor can be completely tested from\n0 to 60bar....
This paper proposes a distributed localization algorithm which can be applied to an irregular three-dimensional wireless sensor\nnetwork, considering the algorithm accuracy and complexity. The algorithm uses clusters to eliminate the multi hop distance\nerrors. An anchor node position optimization scheme is proposed to maximize the uniformity of each sub network. The proposed\nalgorithm also employs a new three-dimensional coordinate transformation algorithm, which helps to reduce the errors introduced\nby coordinate integration between clusters and improves the localization precision. The simulation and performance analysis\nresults show that the localization accuracy of the D3D-MDS algorithm increases by 49.1% compared with 3D-DV-HOP and 38.6%\ncompared with 3D-MDS-MAP. This distributed localization scheme also demonstrates a low computational complexity compared\nwith other centralized localization algorithms....
Indoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness\nand high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K-nearest neighbors (WKNN),\nwhich calculates K-nearest neighboring points to a mobile user. However, existing WKNN cannot effectively address the problems\nthat there is a difference in observed AP sets during offline and online stages and also not all the K neighbors are physically close\nto the user. In this paper, similarity coefficient is used to measure the similarity of AP sets, which is then combined with radio\nsignal strength values to calculate the fingerprint distance. In addition, isolated points are identified and removed before clustering\nbased on semi-supervised affinity propagation. Real-world experiments are conducted on a university campus and results show the\nproposed approach does outperform existing approaches....
Tag collision is one of the main issues impacting the performance of radio-frequency identification (RFID) systems. Several\nresearch efforts have been done in order to solve such problem. Current RFID standards, such as EPCGen2 and ISO-18000-7,\nadopt ALOHA-based protocols as the basis to solve collisions. In recent years, there has been a trend on designing schemes that\nsplit the interrogation zone into smaller regions with the aim of improving the system�s performance. In this paper, we evaluate\nand optimize the performance of ALOHA-based protocols for this new type of partitioning schemes. We establish the guidelines\nfor adapting ALOHA protocols to this new approach in order to exploit the advantages it offers.Thus, we propose a new version\nof the EPCGen2 standard adapted to the new partitioning schemes, which overcomes its counterpart for the traditional approach,\nsignificantly reducing the identification delay, which is the main parameter to optimize in RFID....
In past decades, to achieve energy-efficient communication,manyMAC protocols have been proposed for wireless sensor networks\n(WSNs). Particularly, asynchronous MAC protocol based on low power listening (LPL) scheme is very attractive in duty-cycled\nWSNs: it reduces the energy wasted by idle listening. In LPL scheme, a sensor node wakes up at every polling interval to sample\nthe channel. If the channel is busy, the sensor node will stay in wake-up mode for receiving the data packet. Otherwise, it goes\nto sleep and saves power. However, wrong choice of polling interval in LPL scheme causes unexpected energy dissipation. This\npaper focuses on the polling interval adaptation strategy in LPL scheme with the aim of maximizing energy efficiency, defined as\nthe number of packets delivered per energy unit. We propose a novel polling interval adaptation algorithm based on stochastic\nlearning automata, where a sensor node dynamically adjusts its polling interval. Furthermore, our simulation results demonstrate\nthat the polling interval asymptotically converges to the optimal value....
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