Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 5 Articles
In thewireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required\nto determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and\nan unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the\nlocation of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as\ncomplex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based\non least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model,\nwhich positively reduces the computation amount in the estimation process. The experimental results show that the proposed\nself-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement.\nConclusively, the proposed method is of definite practical value....
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New communication networks are composed of multiple heterogeneous types of networks including Internet,\nmobile networks, and sensor networks. Wireless sensor networks have been applied to various businesses and\nindustries since the last decade. Most sensors have the ability of communication and the requirement of low\npower consumption. 6LoWPAN (IPv6 over Low Power Wireless Personal Area Networks) plays an important role\nin this convergence of heterogeneous technologies, which allows sensors to transmit information using IPv6\nstack. Sensors perform critical tasks and become targets of attacks.\nWormhole attack is one of the most common attacks to sensor networks, threatening the network availability by\ndropping data or disturbing routing paths. RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) is a\nstandard routing protocol commonly used in sensor networks. This study proposes a RPL-based wormhole detection\nmechanism. The rank of a node-defined RPL is adopted to measure the distance. The proposed detection method\ndiscovers malicious wormhole nodes if unreasonable rank values are identified. The experimental results show that the\nproposed detection method can identify wormholes effectively under various wireless sensor networks....
Wireless sensor networks are proved to be effective in long-time localized torrential rain monitoring. However, the existing widely\nused architecture of wireless sensor networks for rain monitoring relies on network transportation and back-end calculation, which\ncauses delay in response to heavy rain in localized areas. Our work improves the architecture by applying logistic regression and\nsupport vector machine classification to an intelligent wireless sensor node which is created by Raspberry Pi.The sensor nodes in\nfront-end not only obtain data from sensors, but also can analyze the probabilities of upcoming heavy rain independently and give\nearly warnings to local clients in time. When the sensor nodes send the probability to back-end server, the burdens of network\ntransport are released. We demonstrate by simulation results that our sensor system architecture has potentiality to increase the\nlocal response to heavy rain. The monitoring capacity is also raised....
In this paper, we develop the statistical delay quality-of-service (QoS) provisioning framework for the energy-efficient spectrum sharing\nbased wireless ad hoc sensor network (WAHSN), which is characterized by the delay-bound violation probability. Based on\nthe established delay QoS provisioning framework, we formulate the nonconvex optimization problem which aims at maximizing\nthe average energy efficiency of the sensor node in the WAHSN while meeting PU�s statistical delay QoS requirement as well\nas satisfying sensor node�s average transmission rate, average transmitting power, and peak transmitting power constraints. By\nemploying the theories of fractional programming, convex hull, and probabilistic transmission, we convert the original fractional structured\nnonconvex problem to the additively structured parametric convex problem and obtain the optimal power allocation\nstrategy under the given parameter via Lagrangian method. Finally, we derive the optimal average energy efficiency and\ncorresponding optimal power allocation scheme by employing the Dinkelbach method. Simulation results show that our derived\noptimal power allocation strategy can be dynamically adjusted based on PU�s delay QoS requirement as well as the channel\nconditions. The impact of PU�s delay QoS requirement on sensor node�s energy efficiency is also illustrated....
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