In Wireless Sensor Networks (WSN's), congestion plays an important role in degrading the performance of the network. Under idle condition, the network load is very low whereas when an event is detected the network load becomes high which leads to congestion. Due to congestion the overall performance of the network degrades. Hence it is necessary to detect and control congestion. In this paper an efficient technique to detect and control congestion has been proposed. The congestion is detected by calculating a metric called Congestion Degree (Cd). It is the ratio between packet inter arrival time and packet inter service time. Once the congestion is detected, it is notified using Implicit Congestion Notification (ICN) signaling. On receiving the congestion notification signal, the transmission rate is controlled in order to reduce congestion. Further congestion control is implemented using Fuzzy Logic Controller. The performance of the network is measured for delivery ratio with different transmission rate and the PDR is compared with CODA.
Wireless sensor networks (WSNs) employ the mobile sinks to gather the information from the sensors deployed in the environment periodically, in such a way to avoid the energy-crisis and hotspot issues. Delay in visiting all the nodes is addressed through the rendezvous points that collect the data from other nodes such that the nodes collect the data from this point rather than visiting all the nodes, which saves energy. However, the optimal placement of the mobile sinks to visit those rendezvous points is a need, which is addressed optimally using the proposed fractional rider optimization algorithm (FROA).The FROA is the modification of the rider optimization algorithm (ROA) with the fractional theory, and the ultimate goal of the proposed algorithm is to optimally place the mobile sink for which initially, the wireless sensor environment is split as uniform-sized cells with the Voronoi partitions and clusters are formed using sparse fuzzy c-means (sparse-FCM) algorithm. The constraints for enabling the optimal sink placement are distance, delay, and energy of the nodes. The simulation analysis reveals that the proposed method outperformed the existing algorithms, with minimal distance of 132.2069 m, maximal network energy, alive nodes, and throughput of 21.4605, 56, and 62.3702, respectively.
Diabetes is one of the chronic metabolic disorder. Under diabetic condition, blood glucose level should be properly maintained in order to avoid various major diseases. The condition will be worse when it is not controlled at an earlier stage. Even massive heart attack cannot be identified when the patient has been affected by diabetes. Early diagnosis is required for preventing fatal diseases like cardiac problem, asthma, heart attack etc. In the proposed system measurement of glucose level and Prediction/ diagnosis of diabetes is based on the real time low complexity neural network implemented on a wearable device. A larger network is required for the diagnosis which needs to be present far-off in cloud and initiated for diagnosis and classification process of diabetes whenever it is essential. People can be able to manage and monitor the required basic parameters like heart rate, glucose level, lung condition, pressure of blood using the corresponding light weight biosensors in the wearable device designed through telemedicine technology. The quality of the disease diagnosis and Prediction is improved in this way. Using neural network feed forward prediction model in conjugation with back propagation algorithm and given training data, the system predicts whether the patient is prone to diabetes or not. The proposed work was evaluated using physic sensor data from physio net data base and also tested for real time functioning. The Proposed system found to be efficient in accuracy, sensitivity and fast operative.
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