N. N. Srinidhi

Wireless sensor networks (WSNs) are the sensor nodes that are generally interconnected and these nodes communicate wirelessly to collect the data from the environment around them. Sensor nodes are low-powered and distributed in a decentralized ADHOC manner. WSNs setup comes with some limitations such as energy constraints and the cooperative demands essential to perform multi-hop geographic routing for Internet of things (IoT) applications. Quality of Service (QoS) depends to a great extent on offering participating nodes an incentive for collaborating. The real-time applications of WSNs have potential design limitations such as energy and other QoS constraints in the context of IoT. This work formulates two different re-transmission strategies for optimized cross-layer design and also integrates the framework with a swarm intelligence technique assisted Bacteria Foraging Optimization (BFO) for efficient Cluster Head (CH) selection. The proposed work designs analytical modelling to realize the formulated concept and also simulates the computational design in a numerical computing tool. Finally, an extensive simulation is carried out using the MATLAB simulation tool. The obtained results show the effectiveness of the proposed system in terms of delay is reduced by 12%, throughput is increased to 10%, remaining energy of the network is increased to 11%, and packet delivery ratio of the proposed work in increased to 7-9% in comparison with the existing system.

Opportunistic networks in the Internet of Things (IoT) scenario, also known as OppIoT, espouse IoT devices interactions opportunistically in order to improve connectivity, the lifetime of the network, and network reliability. An increase in opportunistic utilization is fostered by IoT applications to find communication opportunities whenever possible to route and deliver data efficiently. In this opportunistic scenario, devising an efficient path for data delivery is a challenging work due to uncertainty in the connection between the nodes and the selection of intermediate forwarder nodes for data delivery towards the destination. Considering the scenario of uncertainty in device location and exploiting IoT devices opportunistically, this paper propounds a routing algorithm for OppIoT called Hybrid Multi-Copy Routing Algorithm (HMCRA). The proposed algorithm finds potential forwarder nodes by using fuzzy logic wherein residual energy, distance, and speed of the nodes are considered as input values while preparing fuzzy rules. Genetic Algorithm (GA) is considered along with fuzzy logic to select an efficient path for data delivery. In GA, the delay is taken as the fitness function to select a reliable path for data delivery. Simulation results of the proposed algorithm perform well in contrast with relative existing routing algorithms with respect to latency, overhead ratio, delivery probability, and hop count. The work uniqueness lies in the selection of potential nodes and finding path having less hop count in an opportunistic IoT network scenario.