Pruned Adaptive Routing in the Heterogeneous Internet of Things

Abstract-Recent research endeavours are capitalizing on state of the art technologies to build a scalable Internet of Things (IoT). Envisioned as a technology to integrate the best of Wireless Sensor Networks and RFID systems, there is much promise for a global network of objects that are identifiable, track-able, and harmoniously informing. However, the realization of an IoT framework is hindered by many factors, the most pressing of which is attributed to the integration of these heterogeneous nodes and devices. A considerable subset of these nodes undergoes movement and dynamically enters and leaves the network backbone/topology. Routing packets and inter-nodal communication has received little attention; mainly due to the sheer reliance on the Internet as a backbone. However, spatially correlated entities in the IoT, and those which most often interact, would pose a significant overhead of communication if all intermediate packets need to be routed over distant backhauls. In remedy, we present a Pruned Adaptive IoT Routing (PAIR) protocol that selectively establishes routes of communication between IoT nodes. Since nodes in the IoT belong to different owners, we also introduce a pricing model to cater for the exchange of monetary costs by intermediate nodes to utilize their relaying resources. We also establish a cap on inter-nodal routing to dynamically utilize the Internet backbone if the source to destination distance surpasses a preset (case optimized) threshold. The PAIR routing protocol is elaborated upon, building upon the detailed system model presented in this paper. We finally present a use case to demonstrate the utility and practicality of PAIR in the heterogeneous IoT as it scales

Fuzzy Logic Approach for Routing in Internet of Things Network

A performance of network is evaluated by considering different parameters. The network lifetime depends on many factors Residual energy, Link lifetime and Delay. The Major Challenge in IoT is to the increased lifetime of low power and lossy network (RPL).The process considering input and output to evaluate Network performance by considering the above factors. The proposed system makes use of FIS (Fuzzy Inference System) for selecting the best path to maximize network lifetime. The outcome obtained by using MATLAB and Network performance is increased. The excellent route is selected if Residual Energy is 194, Link quality is 51.2 and Delay is 1.05 then excellent route quality is 73.4%

Design and Analysis of an Optimized Scheduling Approach using Decision Making over IoT (TOPSI) for Relay based Routing Protocols

This research work focuses on support towards QoS approaches over IoT using computational models based on scheduling schemes to enable service oriented systems. IoT system supports on application of day-to-day physical tasks with virtual objects which inter-connect to create opportunities for integration of world into computer-based systems. The QoS scheduling model TOPSI implements a top-down decision making process over top to bottom interconnected layers using service supportive optimization algorithms based on demandable QoS requirements and applications. TOPSI adopts Markov Decision Process (MDP) at the three layers from transport layer to application layer which identifies the QoS supportive metrics for IoT and maximizes the service quality at network layer. The connection cost over multiple sessions is stochastic in nature as service is supportive based on decision making algorithms. TOPSI uses QoS attributes adopted in traditional QoS mechanisms based on transmission of sensor data and decision making based on sensing ability. TOPSI model defines and measures the QoS metrics of IoT network using adaptive monitoring module at transport layer for the defined service in use. TOPSI shows optimized throughput for variable load in use, sessions and observed delay. TOPSI works on route identification, route binding, update and deletion process based on the validation of adaptive QoS metrics, before the optimal route selection process between source and destination. This research work discusses on the survey and analyzes the performance of TOPSI and RBL schemes. The simulation test beds and scenario mapping are carried out using Cooja network simulator

Internet of Things Communication Reference Model and Traffic Engineer System (TES)

Abstract. One of the biggest challenges facing Internet of is the existing infrastructure of Internet and its mechanism of action. This paper proposes a new system, which sends the full Internet best path (between source and destination objects) to source object on IoT. This will help data of source object to reach its final destination object faster. This system saves most of recalculation of the Internet best paths again and again in the Internet Routers during a data trip. The authors call this system Traffic Engineer System (TES). The most important effect of this system is that it changes the form of "Internet of Things Communication Reference Model". This paper merges two addressing layers (IP/ID and Link) from this model in one new layer; where routers transition data through one address and the data have its full best path

ESBL: Design and Implement A Cloud Integrated Framework for IoT Load Balancing

The continuous growth in wireless communication, the demand for sophisticated, simple and low-cost solutions are also increasing. The demand motivated the researchers to indulge into inventing suitable network solutions ranging from wireless sensor networks to wireless ad-hoc networks to Internet of Things (IoT). With the inventions coming from the researchers, the demand for further improvements into the existing researchers have also growth upbound. Initially the network protocols were the demand for research and further improvements. Nevertheless, the IoT devices are started getting used in various fields and started gathering a huge volume of data using complex application. This invites the demands for research on load balancing for IoT networks. Several research attempts were made to overcome the communication overheads caused by the heavy loads on the IoT networks. Theses research attempts proposed to manage the loads in the network by equally distributing the loads among the IoT nodes. Nonetheless, in the due course of time, the practitioners have decided to move the data collected by the IoT nodes and the applications processing those data in to the cloud. Hence, the challenge is to build an algorithm for cloud-based load balancer matching with the demands from the IoT network protocols. Hence, this work proposes a novel algorithm for managing the loads on cloud integrated IoT network frameworks. The proposed algorithm utilizes the analytics of loads on cloud computing environments driven by the physical host machines and the virtual environments. The major challenge addressed by this work is to design a load balancer considering the low availability of the energy and computational capabilities of IoT nodes but with the objective to improve the response time of the IoT network. The proposed algorithm for load balancer is designed considering the low effort integrations with existing IoT framework for making the wireless communication world a better place