A Chain-Based Wireless Sensor Network Model Using the Douglas-Peucker Algorithm in the Iot Environment

WSNs which are the major component in the IoT mainly use interconnected intelligent wireless sensors. These wireless sensors sense monitor and gather data from their surroundings and then deliver them to users or access connected IoT devices remotely. One of the main issues in WSNs is that sensor nodes are generally powered by batteries, but because of the rugged environments, it is difficult to add energy. The other one may cause an unbalanced energy consumption among sensor nodes due to the uneven distribution of sensors. For these reasons, the death of nodes by the energy exhausting and the performance of the network may rapidly decrease. Hence, an efficient algorithm study for prolonging the network lifetime of WSNs is one of important challenges. In this paper, a chain-based wireless sensor network model is proposed to improve network performance with balanced energy consumption via the solution of the long-distance communication problem. The proposed algorithm is consisted of three phases: Segmentation, Chain Formation, and Data Collection. In segmentation phase, an optimal distance tolerance is determined, and then the network field is divided into small sub-regions according to its value. The chain formation is started from the sub-region far away from the sink, and then extended, and sensed data are collected along a chain and transmitted to a sink. Simulations have been performed to compare with PEGASIS and Enhanced PEGASIS using an OMNET++ simulator. The simulation results from this study showed that the proposed algorithm prolonged the network lifetime via the achievement of the balanced energy consumption compared to PEGASIS and Enhanced PEGASIS. The proposed algorithm can be used in any applications to improve network performance of WSNs

An Improved Time Feedforward Connections Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the hidden state at time t-2 to be directly transferred to time t without the nonlinear transformation at time t-1. This is effective in improving the long-term dependence of RNNs. Then, a novel cell structure named Single Gate Recurrent Unit (SGRU) was presented. This cell structure can reduce the number of parameters for RNNs cell, consequently reducing the computational complexity. Next, applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties. Finally, the performance of our proposed TFC-SGRU was verified through several experiments in terms of long-term memory and anti-interference capabilities. Experimental results demonstrated that our proposed TFC-SGRU model can capture helpful information with time step 1500 and effectively filter out the noise. The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability

Reusable mesh signature scheme for protecting identity privacy of IoT devices

Peer reviewedPublisher PD

Long-term Results of Primary Total Knee Arthroplasty with and without Patellar Resurfacing

Among patients that underwent total knee arthroplasty from June, 1990 to January, 1999, 61 cases (44 patients) that could be followed for more than 10 years were included in this study. The patients were divided into a patellar retention group and a patellar resurfacing group, and were compared with regard to their clinical and radiological outcomes. In patients undergoing primary TKA, a selective patellar resurfacing protocol was used. The indications for patellar retention were a small patella, nearly normal articular cartilage, minimal preoperative patellofemoral pain, poor patellar bone quality, and young patient age. When patellar retention was performed, osteophytes of the patella were removed and marginal electrocauterization was carried out. There were 25 cases (20 patients) in the patellar retention group and 36 cases (29 patients) in the patellar resurfacing group. The mean follow-up period was 140.7 months in the patellar retention group and 149.0 months in the patellar resurfacing group. The selective patellar resurfacing with total knee arthroplasty had a favorable outcome;there were a significant difference noted between the 2 groups in the functional scores, which showed better outcomes in the patellar resurfacing group than in the patellar retention group

Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes

Virilizing Adrenocortical Oncocytoma in a Child: A Case Report

Functioning adrenocortical oncocytomas are extremely rare and most reported patients are 40-60 yr of age. To our knowledge, only 2 cases of functioning adrenocortical oncocytomas have been reported in childhood. We report a case of functioning adrenocortical oncocytoma in a 14-yr-old female child presenting with virilization. She presented with deepening of the voice and excessive hair growth, and elevation of plasma testosterone and dehydroepiandrosterone sulfate. She had an adrenalectomy. The completely resected tumor composed predominantly of oncocytes without atypical mitosis and necrosis. A discussion of this case and a review of the literature on this entity are presented

Modifications of T-Scores by Quantitative Ultrasonography for the Diagnosis of Osteoporosis in Koreans

To identify a proper T-score threshold for the diagnosis of osteoporosis in Koreans using quantitative ultrasonography (QUS), normative data from 240 females and 238 males (ages 20-29 yr) were newly generated. Then, the osteoporosis prevalence estimate for men and women over 50 yr of age was analyzed using previous World Health Organization (WHO) methods and heel QUS. T-scores were calculated from the normative data. There were definite negative correlations between age and all of the QUS parameters, such as speed of sound (SOS), broadband ultrasound attenuation (BUA), and estimated heel bone mineral density (BMD) (p<0.0001). After applying the recently determined prevalence of incident vertebral fracture in Koreans over 50 yr of age (11.6% and 9.1%, female vs male, respectively) to the diagnosis of osteoporosis by T-scores from heel BMD as measured by QUS, it was revealed that applicable T-scores for women and men were -2.25 and -1.85, respectively. These data suggest that simply using a T-score of -2.5, the classical WHO threshold for osteoporosis, underestimates the true prevalence when using peripheral QUS. Further prospective study of the power of QUS in predicting the absolute risk of fracture is needed