Network Lifetime Analysis in IOT Environment in Healthcare Sectors Using Deep Learning Routing Approach

The Internet of Things (IoT) provides an improved flexibility in data collection, network deployment and data transmission to the sink nodes. However, depending on the application, the IoT network tends to consume lot of power from the individual devices. Various conventional solutions are provided to reduce the consumption of energy but most methods focus on increasing the data acquisition speed, data transmission and routing capabilities. However, these methods tend to fall under the trade-off between these three factors. Hence, in order to maintain the trade-off between these constraints, a viable solution is developed in this paper. A deep learning-based routing is built considering the faster acquisition of data, faster data transmission and routing path estimation with increasing path estimation. The paper models a Deep belief Network (DBN) to route the data considering all these constraints. The experimental validation is conducted to check the network lifetime, energy consumption of IoT nodes. The results show that the DBN offers greater source of flexibility with increased data routing capabilities than other methods.

Development of functionalities extension approach and implementation of address routing for IFogSim based simulators

The object of research is an approach of functionality extension for simulation toolkits based on iFogSim. It is assumed by the native approach that enhancement of functionalities should be achieved by inheriting the fog device class and defining new features in its body. However, this approach makes it impossible to use inherited simulators together and significantly decreases flexibility even when utilizing a single simulator. Another problem related exclusively to iFogSim is a specific communication scheme between application modules, which results in data routing limitations in fog architectures and odd data streams taken into account. This paper introduces an alternative extension approach incorporating a peculiar inheritance scheme which tries to reconsider the standard approach from a behavioral design patterns point of view. The key feature of the suggested approach is an extraction of fog device features from the native class into separate behavioral classes. Meanwhile, the designed inheritance scheme allows to flexibly override and combine behaviors. According to the approach principles the developed simulator extends iFogSim with application modules addressing capabilities solving limitations, along with implementing users’ mobility and dynamic wireless connectivity as it is done in MobFogSim. With the aim to check its correctness, the designed toolkit was validated with the standard for iFogSim case study of «EEG Tractor Beam game» application. The validation included four scenarios. In the first two scenarios the features of users’ mobility and dynamic base station connectivity were validated. And in the next scenarios that utilized address routing the obtained delay and network usage values were compared with theoretically calculated ones. The validation results indicated the correct simulator behavior, and introduced functionalities extension approach, being more complex in comparison with the inative one, can significantly improve flexibility of the simulator

Anthoc Based Trust Behavioural Network (Anthoc-tbn) Assist to Detect Blackhole Attack by Discover Secure Route

MANET is a wireless topology mainly used to grouping several mobile nodes as network. This wireless network is highly vulnerable by several security threads. Blackhole attack, a severe thread that voluntarily disrupts the healthy nodes and turn it as malicious node. So, introducing security mechanism can forcefully work together to defeat the black hole attack. In this paper, a new approach of AntHoc based trust behavioural network (AntHoc-TBN) model is introduced. This model effectively discovers the route with trust based packet transmission take place. Here, the forward ant agent performs shortest distance computation to discover the route. So that, Dijikstra algorithm is employed to detect the shortest route of all other nodes from source. Then, trust procedure is applied to find that corresponding node is trustable or not. Based on trust threshold, the respective intermediate node is computed and stored the updated value in extended data routing information (EDRI) table. Once the condition is not satisfied, then updated value in EDRI table tends to be distrust node and the concerned node is eliminated from the routing table. Now, the trusted node becomes an active path after that the packet has to be sent. Likewise, the same process is repeated for backward ant agent to transfer the Ack message from destination to source. To simulate the process, the result reveals that the AntHoc-TBN model improves its efficiency by lowest delay, packet loss and routing overhead. And also, the technique gets highest value of packet delivery ratio. The performance measures of our proposed model achieves better outcome when compared to existing techniques such as AODV, AntNet and AntHoc Net.