Energy Efficient Data Gathering in IoT Networks with Heterogeneous Traffic for Remote Area Surveillance Applications A Cross layer Approach
ABSTARCT :
In this paper, the problem of energy-efficient data gathering in an Internet of Things (IoT) based remote area surveillance application is addressed by designing a suitable MAC layer uplink solution. We follow the 3GPP specified scheduled access scheme for narrowband IoT (NB-IoT) and consider presence of both delay sensitive and delay tolerant traffic in the network. First, we present a mixed integer non-linear programming (MINLP) based optimization framework to minimize the IoT devices’ cumulative transmission energy per uplink frame under the constraints of finite resource blocks, mean queue stability and residual energy levels of IoT nodes. A cross layer approach has been adopted. Precisely, using Lyapunov optimization, we propose a dynamic, distributed transmit power allocation for IoT nodes and a centralized node scheduling scheme. Further, assuming Poisson model for traffic generation at delay tolerant traffic generating nodes, we suggest a distributed probabilistic sleep scheduling scheme to improve the average delay experience of delay sensitive traffic while improving the overall energy conservation. Simulation results suggest impressive performance of our proposed solution over the existing uplink solution for NB-IoT, in terms of delay experience of delay sensitive traffic, buffer length requirement and nodes’ total energy consumption at high traffic load.
EXISTING SYSTEM :
? It also provides the opportunity to transform the traditional functioning of several existing industrial systems such as healthcare and manufacturing.
? The routing performance can be significantly improved for IoT based applications, and it is preferable than the existing OFs.
? Many existing studies have presented a different version of RPL energy optimization, but the duty cycling aspect has ignored mostly.
? In order to enlarge network lifetime, efficient resource utilization of nodes is an important concern particularly for resource-constrained networks.
? Each node in a graph connects in a way where there exist no cycles. The DAG is destined to one or more root and generally known as destination oriented DAG (DODAG).
DISADVANTAGE :
? The cluster heads that are one hop away from BS exhaust their energy resource much quickly and lead to the energy hole issue, which degrades network throughput and increases the latency ratio.
? However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production.
? The drastic changes in climate negatively impact the agriculture eco-system causing heaving rains, droughts, floods, and abrupt weather conditions.
? These low powered sensor nodes have significant impact on the performance of the network in terms of delivery ratio and energy consumption.
PROPOSED SYSTEM :
• The extensive simulation results show that the proposed RAROF can effectively extend the lifetime of the network, enhance the energy efficiency, and achieve higher reliability than that of other OFs.
• A new routing metric named node vulnerability index (NVI) is proposed, which includes the duty cycle information, link quality, energy profile of a node during the path selection process for network life improvement.
• The proposed work points out that default single-metric-based OFs are vulnerable in situations where congestion occurs due to a sudden increase in traffic volume, resulting in packet loss and delay.
• The proposed composite metric considers both energy and reliability parameters to select the optimum path.
ADVANTAGE :
? In various domains, the technology of wireless sensor network (WSN) has been used in an efficient way to improve network performances.
? Although, the field of WSN has been exploited by many researchers in the domain of agriculture to improve its performance and reduce the farmer’s burden.
? The main aim of the proposed solution is to improve the network performance in terms of cluster formation and cluster head selection based on distance, density, mobility, and energy factors.
? However, in most of the other existing works, the proposed solution also does not consider the performance of links in data transmission and leads to packets drop and latency ratio.
? The performance of the proposed framework with other solutions is measured based on network throughput, packets drop ratio, network latency, energy consumption, and routing overheads.
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