Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 5 Articles
View of wireless sensor network (WSN) devices is small but have exceptional\nfunctionality. Each node of a WSN must have the ability to compute and\nprocess data and to transmit and receive data. However, WSN nodes have limited\nresources in terms of battery capacity, CPU, memory, bandwidth, and\ndata security. Memory limitations mean that WSN devices cannot store a lot\nof information, while CPU limitations make them operate slowly and limited\nbattery capacity makes them operate for shorter periods of time. Moreover,\nthe data gathered and processed by the network face real security threats. This\narticle presents an Adaptable Resource and Security Framework (ARSy) that\nis able to adapt to the workload, security requirements, and available resources\nin a wireless sensor network. The workload adaptation is intended to\npreserve the resource availability of the WSN, while the security adaptation\nbalances the level of security with the resource utilization. This solution makes\nresources available on the basis of the workload of the system and adjusts the\nlevel of security for resource savings and makes the WSN devices work more\nefficiently....
A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and\nthe total number of detections that report ââ?¬Å?1ââ?¬Â to the fusion center (FC) is studied for a wireless sensor network\n(WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated. We propose the logistic\nregression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a\nglobal decision about the presence/absence of the target. Both the fixed and variable numbers of decisions\nreceived by the FC are examined. The fusion rule of K out of N and the counting rule are compared with the LRFR.\nThe LRFA does not depend on the signal model and the priori knowledge of the local sensorsââ?¬â?¢ detection\nprobabilities and false alarm rate. The numerical simulations are conducted, and the results show that the LRFR\nimproves the performance of the system with low computational complexity....
LLNs are gradually attracting people�s attention for the feature of low energy consumption. RPL is specifically designed for LLNs to\nconstruct a high energy efficiency network topology. In the noisy environment, the packet loss rate of RPL-based WSN increases\nduring data transmission. The DODAG constructed by RPL-based WSN increases in depth when communication is affected by\nnoise. In this situation the data transmission in the network will consume more energy. In event detectionWSNs, the appropriate\nnetwork topology enables root to use less sensor data to fuse enough information to determine whether or not an event has occurred.\nIn this paper, we will improve RPL in two ways to optimize topology and reduce energy consumption. (1) The neighbor list and\nthe information of DIO sender�s parent of a node are used to construct a better DODAG in the noisy environment. (2) SPRT and\nthe quality of information of the data are used in the event detecting process for saving energy consumption. Simulation results\nshow that, compared with the original RPL, QoI-aware RPL can reduce the energy consumption by collecting the same quality of\ninformation with less data transmission....
As much as accurate or precise position estimation is always desirable, coarse\naccuracy due to sensor node localization is often sufficient. For such level of\naccuracy, Range-free localization techniques are being explored as low cost\nalternatives to range based localization techniques. To manage cost, few location\naware nodes, called anchors are deployed in the wireless sensor environment.\nIt is from these anchors that all other free nodes are expected to estimate\ntheir own positions. This paper therefore, takes a look at some of the\nforemost Range-free localization algorithms, detailing their limitations, with a\nview to proposing a modified form of Centroid Localization Algorithm called\nReach Centroid Localization Algorithm. The algorithm employs a form of\nanchor nodes position validation mechanism by looking at the consistency in\nthe quality of Received Signal Strength. Each anchor within the vicinity of a\nfree node seeks to validate the actual position or proximity of other anchors\nwithin its vicinity using received signal strength. This process mitigates multipath\neffects of radio waves, particularly in an enclosed environment, and\nconsequently limits localization estimation errors and uncertainties. Centroid\nLocalization Algorithm is then used to estimate the location of a node using\nthe anchors selected through the validation mechanism. Our approach to localization\nbecomes more significant, particularly in indoor environments,\nwhere radio signal signatures are inconsistent or outrightly unreliable. Simulated\nresults show a significant improvement in localization accuracy when\ncompared with the original Centroid Localization Algorithm, Approximate\nPoint in Triangulation and DV-Hop....
In underwater acoustic channel, signal transmission may experience significant latency and attenuation that would degrade the\nperformance of underwater communication. The cooperative communication technique can solve it but the spectrum efficiency\nis lower than traditional underwater communication. So we proposed a time reversal aided bidirectional OFDM underwater\ncooperative communication algorithm. The algorithm allows all underwater sensor nodes to share the same uplink and downlink\nfrequency simultaneously to improve the spectrum efficiency. Since the same frequency transmission would produce larger\nintersymbol interference, we adopted the time reversal method to degrade the multipath interference at first; then we utilized\nthe self-information cancelation module to remove the self-signal of OFDM block because it is known for sensor nodes. In the\nsimulation part, we compare our proposed algorithm with the existing underwater cooperative transmission algorithms in respect\nof bit error ratio, transmission rate, and computation.The results show that our proposed algorithmhas double spectrumefficiency\nunder the same bit error ratio and has the higher transmission rate than the other underwater communication methods....
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