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Energy efficient cross layer based adaptive threshold routing protocol for WSN

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Abstract

Wireless Sensor Networks (WSNs) perform an important part in modern day communication as it can sense the various physical and environmental parameters by employing low cost sensor devices. The growth of the networks due to scientific advancements have altogether made it possible to create an energy efficient cross layer network that can improve its lifespan. In this paper, a routing protocol is proposed for the networks which are heterogeneous and are based on the adaptive threshold sensitive distributed energy efficient cross layer routing protocol. The concept of weighted probability is used to assign the CH (Cluster Head) of the network cluster. The proposed algorithm is simulated, tested and compared with previously established routing protocols and has shown enhanced results and prolonged network lifespan. In the proposed protocol, a combination of the proactive and reactive network is considered for effective data transmission.

Introduction

Wireless Sensor Network (WSN) is evidently one of the most rapidly evolving networks which have led to the advancements in the field of communications. The reason behind this can be bestowed upon the development of sophisticated, reduced and cheaper sensor devices that exhibits the ability to sense different types of environmental and physical parameters, processing it, and transmitting the data wirelessly. A WSN comprises of spatially distributed independent sensors that can be used to observe environmental or physical conditions, such as, pressure, vibration, acoustic parameters and temperature parameters to synchronically authorize their data through the prime location, i.e. the base station (BS) across the network. Generally, WSNs consists of huge number of nodes which are having a power resource with limited energy. Since the energy needed to impart sensed data packets to the BS is large, so a routing protocol which is energy efficient is required. The routing protocols are required to build the paths for communication between the sensor nodes and the BS. Thus, to securely and efficiently transmit the data from nodes to BS becomes the utmost critical job of these sensor networks. Lately, various routing protocols have been proposed that use the resources efficiently, conclusively improving the network lifespan.

The cross layer technique [14], [15] is used to achieve optimal QoS (Quality of Services) parameters. It combines various layers of the communication protocol to exchange information in a non-hierarchical manner. This technique is thus suited for better communication of the information with reduced optimal energy consumptions [17]. The cross layer concept is depicted in Fig. 1.

The various WSNs routing protocols are classified [1] and depicted in Fig. 2. The routing protocols might be categorized into hierarchical and flat types, based upon the network structure. In flat routing protocols, each sensor node has to perform the same role thus minimizing the network overhead. It also achieves energy effectiveness, scalability, and stability when a network structure is enforced. In the hierarchical routing protocols, the nodes are segregated into clusters and the cluster head (CH) is a node in the cluster with higher residual energy.

Further, the communication model routing protocols can be categorized into query based protocols or coherence protocols. In the formal, a node requests a query from another sensor node(s). The coherence protocols can be further divided based on the coherent strategy and non-coherent strategy of routing whereas in the non-coherent strategy, the information which is sensed is treated before being transmitted to another nodes for additional processing, whereas in coherent routing protocols the data which is sensed is moved to the aggregators directly. The negotiation based routing protocol uses a meta-negotiation technique to reduce the transmission overhead.

The topology based protocols consist of two algorithms, namely mobile agent-based, and location-based algorithms. The mobile agent based routing make use of agent-based programming paradigm, and in location-based routing, the actual position information (maybe by using GPS) is used.

The reliable routing protocols can handle route failures. This resilience is achieved by maintaining an equilibrium between the loads or by supporting the QoS matrices [1] comprising of various parameters such as bandwidth utilization, delay, and throughput. The multipath based reliable routing makes use of multiple paths to overcome route failures, whereas QoS-based routing strive for a balance between the data quality and the energy expenditure.

The routing protocols generally employ a clustering technique [2], in which sensor networks are divided into small convenient units. The funding idea to employ clustering method is to advance the scalability of the network, but it also attains energy efficient routing protocol for transmission of the information. The additional features include the conversation of transmission bandwidth between the clusters, localization of an energy efficient route system between the clusters, and prevention of redundant data transfer among the nodes.

The clustering method LEACH has been proposed by Heinzelman et al. in [3] which shared the energy load amongst the sensor nodes equally. The LEACH approach is based on the random rotation of the cluster coordinate theory and utilizes the localized coordinates to enable ruggedness and scalability in the dynamic networks. It also includes data aggregation which is used to reduce the content of information passed to the base station. It has also added the advantage of increasing the network lifespans. Another clustering method, known as HEED is presented in [4], makes use of residual energy as the probability to select the CH.

The work has been further extended, as multihop-LEACH and energy-LEACH [5]. Energy-LEACH, known as E-LEACH, is devoted to the reduction of overall consumption of the network’s energy by enhancing the criteria of the choice for the CH. Multihop-LEACH, known as M-LEACH protocol acts on the mode of communication by initiating single to multihop communication across sink and CH. In 2013, Javaid et al. [6] introduced EDDEEC, a routing system based upon clustering for Wireless Sensor Networks which are hetrogeneous. The funding idea lies in the effect and dynamic change on the Cluster head election probability. The algorithm shows enhancement in stability period, network lifespan and enhanced bypassing of effective signals to BS in heterogeneous conditions as compared to Energy-Efficient Heterogeneous Clustering (EEHC) scheme, Distributive Energy-Efficient Clustering (DEEC), Developed Distributive Energy-Efficient Clustering (DDEEC), and Enhanced Developed Distributive Energy-Efficient Clustering (EDDEEC). All of the above mentioned approaches enhance the energy efficiency and the lifespan of the network by distributing network energy consumption uniformly. To code and control multiple video streams simultaneously in multihop wireless networks, a novel cross layer framework is proposed in [7]. In this, a scheme is proposed known as cross layer distributed flow control, an optimal transmission rate from source to destination for wireless communications is achieved. Another analytical coding approach known as – rate-distortion optimized joint source-channel is employed to chose the coding strategy of the channel optimally given that the optimal transmission rate is acquired from the prevailing channel condition and the proposed flow control scheme.

An optimum routing for cross layer is also proposed for wireless mesh networks based on the principle of adaptive spatial TDMA [8]. The authors in [9] surveys various cross-layer designs, especially for MANETs.

One of the main challenging issues in WSN designing is energy consumption. Thus, various routing protocols are there to attain energy efficiency. Of these, LEACH is representative and utilizes random rotation of the CHs to uniformly distribute the energy load across all the nodes. However, an energy efficient model can be developed as it solely depends on a probability model. A new ASLPR (application specific low power routing protocol) came into existence [10] which takes into consideration the few parameters from sensor nodes while selecting the optimal cluster heads. Also, advancement in disruptive technologies, such as the research in high-speed wireless and optical transmission and P2P content distribution and research in the virtualization of systems has provoked fundamental discussions to design the future Internet [11]. In [12], in multiuser diversity system a cross-layer framework is investigated using rate compatible STBC (Space-Time Block Codes) and LDPC (Low-Density Parity-Check)codes.

One of the primary problems faced for definitive multicast transmissions across wireless networks is the dynamic nature of the wireless link. Vien et al. [13] proposed a network coding to improve the overall network throughput. A new cross-layer optimization framework to design the network topology is introduced which optimizes the multicast rate of the wireless networks, the energy supply, the data flow of the wireless links, and node lifespan available bandwidth. ElAttar et al. [14] optimize these goals while considering the effects of various network design parameters like node spatial density, data rate, and traffic load by investigating the minimum transmitted power. The authors of [15] provides a distinctive categorization of cross-layer QoS approaches in Wireless Sensor Networks which permits to survey numerous studies with maximum clarity. Indumathi et al. [16] proposed a new Adaptive Cross-Layer Scheduler design, which outperforms the previous techniques by lessening a prescribed cost function which contains the delays states and the current channel of the packet in the queue according to both user throughput and packet delay. In [17], a novel cross-layer mobility aware MAC protocol for cluster-based cognitive radio sensor network is proposed, the primary focus being formation and maintenance of clusters.

In this paper, a novel method is introduced that employ a cross layer technique through the various layers (refer Fig.1). This method proves to be very effective for the construction of WSNs for optimized energy consumptions. For adaptive threshold sensitive distributed cross layer energy-efficient routing, the Cluster head is selected with respect to the weighted probability, which is based upon factor i.e. the mean energy of the whole network divided by residual energy of every single node. The results shows the improved stability and prolonged network lifespan when compared to DEEC, EEHC, and EDDEEC.

Section snippets

Proposed work

The proposed routing protocol employs two models, namely the Network model, and the Radio energy model. The Network Model concentrates on cluster formation and CH selection, whereas, the Radio Energy model focuses on calculating the energy expended during transmission, reception, and accumulation of data. The network is modeled as a square field of uniformly distributed random nodes. It is considered that every single one of the sensor nodes become static once the deployment gets over, i.e.

Results and discussions

The evaluation and examination of the performance for hierarchical protocols, the basic used matrices are total data packets received at Base Station, network residual energy, and the number of nodes alive.

The proposed model is tested in a fixed area for a particular number of sensor nodes. The proposed hybrid protocol (ATEER) is simulated with EDDEEC, DEEC, and EEHC protocols based on performance metrics.

It is evident from Fig. 3, that the network lifespan and stability period for proposed

Conclusions

The basic advancement of proposed hybrid technique ATEER is because of the consideration of all the three levels of node heterogeneity. The CHs have been selected in accordance to the ratio calculated from the average energy of entire network and residual energy of the sensor node. The technique shows better results for heterogeneous networks even when compared to homogeneous networks.

The employment of both reactive and proactive network concepts, has turned fruitful in improving the stability

Ramnik Singh is currently working as an Assistant Professor in the department of Computing Science and Engineering at DAV Institute of Engineering and Technology, Jalandhar, India. He is currently pursuing his Ph.D. degree from Thapar University Patiala. His work areas of interest are Wireless Sensor Networks and MANETs.

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Ramnik Singh is currently working as an Assistant Professor in the department of Computing Science and Engineering at DAV Institute of Engineering and Technology, Jalandhar, India. He is currently pursuing his Ph.D. degree from Thapar University Patiala. His work areas of interest are Wireless Sensor Networks and MANETs.

Anil Kumar Verma is currently working as Associate Professor in the department of Computer Science and Engineering at Thapar University, Patiala in Punjab (INDIA). He received his B.S. M.S. and Ph.D. in 1991, 2001 and 2008 respectively, majoring in Computer Science and Engineering. He has worked as Lecturer at M.M.M. Engineering College (now, MMM University of Technology), Gorakhpur from 1991 to 1996. From 1996 he is associated with the same University. He has been a visiting faculty to many institutions. He has published over 150 papers in refereed journals and conferences (India and Abroad). He is member of various program committees for different International/National Conferences and is on the review board of various International journals. His area of interest is Adhoc networks, Wireless sensor networks and security.

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