Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

Abstract

Internet of Things (IoT) is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet links. Particularly, some of the nodes in IoT are mobile and dynamic in nature. Hence maintaining the link among the nodes, efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data processing. In such networks, many routing-problems arise due to difficulties in energy and bandwidth based quality of service. Due to the mobility and finite nature of the nodes, transmission links between intermediary nodes may fail frequently, thus affecting the routing-performance of the network and the accessibility of the nodes. The existing protocols do not focus on the transmission links and energy, bandwidth and link stability of the nodes, but node links are significant factors for enhancing the quality of the routing. Link stability helps us to define whether the node is within or out of a coverage range. This paper proposed an Optimal Energy and bandwidth based Link Stability Routing (OEBLS) algorithm, to improve the link stable route with minimized error rate and throughput. In this paper, the optimal route from the source to the sink is determined based on the energy and bandwidth, link stability value. Among the existing routes, the sink node will choose the optimal route which is having less link stability value. Highly stable link is determined by evaluating link stability value using distance and velocity. Residual-energy of the node is estimated using the current energy and the consumed energy. Consumed energy is estimated using transmitted power and the received power. Available bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

“…The researchers studied the use of Naive Bayes (NB) and Support Vector Machine for machine learningbased sentiment classification of movie reviews (SVM) [8][9][10][11][12]. Sentiment Analysis is a two-class classification problem comprising Positive and Negative classes; this kind of study may be used to classify textual information and feature selection affects classifier performance.…”

Decentralized Link Failure Prevention Routing (DLFPR) Algorithm for Efficient Internet of Things

“…Within the next time Δt of the IoT nodes, future meeting of other nodes happen. The P(xi, yi) is dependent on the Coverage Area (CA) at Δt time [20][21][22][23][24][25][26][27][28]. The coverage area and the number of nodes are highly inter related and determine the network performance.…”

Deep learning based model for classification of COVID −19 images for healthcare research progress

“…The contrast of the infected person's image is 2 to 3 times that of the normal lung image. Researchers are proposing several security protocols [24] , [25] , [26] , [27] , [28] , [29] , [30] in healthcare system to protect healthcare information among various users and servers in wireless network.…”