M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs

In this research work, we advise gateway based energy-efficient routing protocol (M-GEAR) for Wireless Sensor Networks (WSNs). We divide the sensor nodes into four logical regions on the basis of their location in the sensing field. We install Base Station (BS) out of the sensing area and a gateway node at the centre of the sensing area. If the distance of a sensor node from BS or gateway is less than predefined distance threshold, the node uses direct communication. We divide the rest of nodes into two equal regions whose distance is beyond the threshold distance. We select cluster heads (CHs)in each region which are independent of the other region. These CHs are selected on the basis of a probability. We compare performance of our protocol with LEACH (Low Energy Adaptive Clustering Hierarchy). Performance analysis and compared statistic results show that our proposed protocol perform well in terms of energy consumption and network lifetime.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Quantized Routing Models for Clustering Scheme in Wireless Sensor Networks

AbstractClustering routing protocols are effective topology approaches which can increase the scalability of wireless sensor networks and efficiently utilize the limited energy resources of the sensors. However, the loading or energy consumption of sensors in networks is heterogeneous so that some sensors may die earlier than the others. In this case, data from sensors will not be delivered properly to the base station. Many previous studies have focused on energyefficient routing protocols to prolong the network lifetime without considering the influences of transmitting range or availability of compression. In this paper, we propose quantized models to simulate the operations of clustering routing protocols and evaluate the energy consumption of networks as well as the load distribution of sensors. Besides, the cluster head selection algorithm is developed correspondingly. The comparison of data reception rate for LEACH with our model in cases of different compression rates by simulations is also presented

A Detailed Overview of Life Cycle Enhancing Approaches for WSN

The major target of a wireless sensor network (WSNs) is to amass related data in the form of packets from the physical world. Transmission of these packets towards lengthier route consumes extra battery, and amplification and causes more intervening. As a result, these variables limit the lifespan of the network and operational ability. Numerous techniques exist in the past to augment the lifespan of the WSN. In this paper we have analyzed state of art techniques which enhance the lifecycle of a WSN

Energy Threshold-based Cluster Head Rotation for Routing Protocol in Wireless Sensor Networks

 يمثل ترشيد استهلاك الطاقة في شبكات المتحسس اللاسلكي مسألة اساسية لكون عمر الشبكة يعتمد كليا على الطاقة المتوفرة عادة في اجهزة المتحسسات. في هذه المقالة تم اقتراح بروتوكولا للارسال مبني على اساس العنقدة غير المتساوية حيث يأخذ بنظر الاعتبار متغييرات الطاقة والمسافة والكثافة في تحديد راس كل مجموعة (عنقود). وكذلك يكون حجم العناقيد غير متساو وفقا لمتغرات المسافة والطاقة والكثافة. مضافا الى ان رؤوس العناقيد لا يتم تغييرها في كل دورة ما لم يصل مستوى الطاقة فيها الى حد معين من الطاقة. ولقد بينت نتائج المحاكاة الى ان كفاءة البروتوكول المقترح تحقق تحسسنا في ترشيد الطاقة.Energy efficiency represents a fundamental issue in WSNs, since the network lifetime period entirely depends on the energy of sensor nodes, which are usually battery-operated. In this article, an unequal clustering-based routing protocol has been suggested, where parameters of energy, distance, and density are involved in the cluster head election. Besides, the sizes of clusters are unequal according to distance, energy, and density. Furthermore, the cluster heads are not changed every round unless the residual energy reaches a specific threshold of energy. The outcomes of the conducted simulation confirmed that the performance of the suggested protocol achieves improvement in energy efficiency

Implementation of Leaky Bucket with deep learning Algorithm to Avoid Congestion in DEC Protocol in medical applications

A wireless sensor network is a critical component in many disciplines. There are a large number of sensor nodes in it. These sensor nodes perform various tasks, including identifying, dispensing, communicating, and providing power. Data is sent from source to destination and plays an important role. Congestion will occur during data transfer =0>0from one node to another and in the cluster head. Congestion will emerge as a result of either traffic division or resource allocation. Energy will be wasted due to traffic division congestion, which results in packet loss and retransmission of deleted packets. As a result, it must condense. Congestion management will be handled by a few wireless sensor networks using various protocols. Deterministic Energy Efficient Clustering (DEC) protocol is considered to reduce energy consumption based on residual energy in which the leaky bucket algorithm is cast-off. In the event of congestion, our plan outlines a strategy for dealing with and resolving it using this manner. According to simulation testing, the suggested approach may significantly improve longevity, energy, throughput, and packet loss

Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing in Wireless Sensor Networks

There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.Comment: 16 Pages, JGraph-Hoc Journa