Energy-Efficient Geographical Multi-path Routing ...

Jour of Adv Research in Dynamical & Control Systems, Vol. 9, No. 9, 2017

Energy-Efficient Geographical Multi-path Routing Protocol with Adaptive Load Balancing for Wireless Multimedia Sensor Networks Hasib Daowd Esmail Al-ariki, JSS Research Foundation, Department of Electronics and Communication, Sri Jayachamarajendra College of Engineering, Mysore, Karnataka, India. E-mail:[email protected] Dr. Abdo Saif Mohammed, Department of Computer Science and Information Technology, Thamar University, Thamar, Republic of Yemen. E-mail:[email protected] Dr.M.N. Shanmukha Swamy, Department of Electronics and Communication, Sri Jayachamarajendra College of Engineering, Mysore, Karnataka, India. E-mail:[email protected]

Abstract--- Energy bottleneck and load balancing are two major issues in multipath routing for Wireless Multimedia Sensor Networks (WMSN). Pairwise Directional Geographical Routing (PWDGR) resolves energy bottleneck problem, however the high energy consumption due to GPS based node localization is a concern. This paper proposes Energy Enhanced PWDGR (EE-PWDGR) with triangulation based localization system and reduced energy consumption by selecting the forwarding node based on energy drain rate and distance parameter for path discovery. Though EE-PWDGR improves energy efficient routing, WMSN require quality of service (QoS) satisfaction in multimedia communications. Hence additionally Energy Enhanced Load Balancing PWDGR (EELBPWDGR) is proposed for avoiding overload conditions by estimating QoS parameters namely path reliability, link quality, and average delay. Finally high priority multimedia data are transmitted through paths with highest weight values. Experimental results show that the proposed EE-PWDGR and EELB-PWDGR protocols efficiently resolves high energy consumption and load balancing problems. Keywords--- Wireless Multimedia Sensor Networks, Pair-wise Directional Geographical Routing, Triangulation, Load Balancing, Energy Bottleneck, Quality of Service, Path Reliability, Link Quality, Average Delay, Node Localization.

I.

Introduction

WMSN (Anasane and Satao, 2016; Almalkawi et al., 2010) is considered to be one of the novel sensor networks which include multimedia information perspective function for collecting and transmitting multimedia content. The improvements in the hardware technologies enable the WMSN to utilize a single sensor to collect the video and audio information from the environment. The main objective of the WMSN is to transmit multimedia content with a particular level of QoS. In order to support high-end applications, it is essential that the QoS parameters (Jin et al., 2015) are satisfied to minimizing energy consumption and controlling congestion in sensor networks. Geographical routing is an important type of routing strategy for the WMSN as it allows the efficient forwarding of the larger size multimedia content. In (Al-Ariki and Swamy, 2017), strategies such as Greedy Perimeter State Routing (GPSR), Two-Phase geographic Greedy Forwarding (TPGF), Directional Geographical Routing (DGR) and PWDGR were briefly discussed to support the multimedia transmission. Though these routing strategies were found to improve routing performance, most of them continued to suffer from energy bottleneck problems with a considerable loss in transmitted packets. PWDGR is a novel strategy proposed by Wang et al (2015) for the routing of multimedia data in the sensor networks. PWDGR ensures the selection of pairwise nodes in 360 degrees scope around sink node is based on energy so that the energy consumption problem of the sink is resolved. However the problem in PWDGR is that it utilizes DGR scheme to send video packets from source node to pairwise hop node while GPSR scheme from pairwise hop nodes to the sink. Both schemes require GPS like information for knowing the geographical locations of the nodes, which suffer from limitations of satellite coverage, longer distance communication, halted by harsh climates and increased the network cost by consuming more energy. Further, the GPSR scheme takes into considers only the distance parameter to select the nodes in a greedy manner. Therefore, overload conditions that occur in a multipath routing scheme are not effectively handled in an efficient manner.

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To rectify this defect, the EE-PWDGR has been introduced which employs a standard, distance-based clustering approach to collect the local coordinates using which a transformation matrix is constructed. Then the two Cartesian coordinate systems of clusters are efficiently combined to obtain a global coordinate system. The triangulation technique is used to localize the node position. This strategy operates asynchronously without any centralized controller and does not require that the location of the sensors are known a priori. The GPSR which is used to forward multimedia data from the pairwise node to the sink node can also be enhanced. It presently considers only the distance parameter to determine the neighboring nodes. In the proposed approach, the GPSR in greedy mode selects the neighboring nodes based on i) weight value calculated by considering energy drain rate of the node and ii) the distance covered between forwarding node and the destination node. In the perimeter mode, next node in the planar graph is selected with an increasing order of angle and energy drain rate. An adaptive load balancing scheme is then introduced to develop EELB-PWDGR for avoiding overload conditions in multi-paths. Important parts of an image data are with high reliability for the overall output. To realize this, data packets of an image file are divided into different priority classes such as low, medium and high. The fuzzy logic is used to decide the priority to the multimedia data packets. In this way, different priority values are set for each class of packets. EELB-PWDGR then balances the load among the available paths by estimating the QoS parameters such as path reliability, link quality, and average delay. The path with the highest weight values is assigned to the highly prioritized multimedia content, thus resolving the problems of energy consumption and load balancing.

II.

Related Works

The localization of nodes position in wireless sensor network (WSN) is important to establish a path from source to sink. Karp and Kung (2000) introduced GPSR approach which uses the locations of the nodes to provide routing in a greedy manner. GPSR uses GPS or Galileo to discover geographical locations of the nodes. Though GPSR approach provides better routing, this approach suffers from lengthy path formation by perimeter mode, computation complexity of planarization and the inability of eliminating up the edge without obstacles. Wang and Xu(2010) proposed GPS free localization algorithm by merging homogeneous local coordinate system. This approach effectively overcomes the flip ambiguity problem but has technical issues relating energy consumption. Sanchez et al (2012) proposed a beaconless multicast geographic routing algorithm by integration of opportunistic data delivery and dynamic neighborhood discovery mechanism which reduce the bandwidth consumption and control overhead of getting location information. Chen et al (2014) handle the time-varying and direction-varying connectivity natures of nodes in wireless sensor network which tend to influence the accuracy of location prediction. The dynamic sleep scheduling parameter used in this paper affect the localization accuracy. Villas et al (2015) proposed an energy efficient joint localization and synchronization solution. Qu et al (2015) presented the energy efficient anchor-free localization algorithm. Kumar and Kumar (2016) proposed position based beaconless routing for sensor networks. Efficient transmission of multimedia streams in WMSNs continues to be a main challenging problem due to mainly constraints of bandwidth and power resource of sensor nodes. Chen et al (2007) proposed DGR that constructs a number of multiple disjointed paths for a video sensor node to transmit parallel FEC-protected real-time video streams in multiple paths. However, DGR suffers from the energy bottleneck problem due to multi-path forwarding. Shu et al (2010) presented TPGF routing algorithm with two phases: the possible shortest paths are found in first phase while the selected shortest paths are optimized with least number of hop in second phase. Li and Kim(2012) proposed a Geographic Routing Protocol for WMSN by accounting energy and delay of neighbor nodes while selecting path from source to sink. However, this method not considered trade-off between end-to-end delay and network lifetime under various network settings. Xu et al (2012) proposed the bandwidth-power aware cooperative multi-path routing (BP-CMPR) for WMSNs. The energy-efficient node-disjoint multi-path routing for a given source-destination pair by joint route construction, relay assignment and power allocation methods are utilized for the effective construction of the routing scheme. Li and Chuang (2013) proposed Geographic Energy-Aware non-interfering Multipath (GEAM) for effective multipath routing of multimedia transmission in WSN. However, the use of many source-sink pairs in GEAM might reduce the overall energy efficiency. WMSN does not merely require improvement of parameters such as energy constrains, limited computing power, and memory availability of the sensor nodes, but also requires techniques to deliver multimedia content with a certain level of Quality of Service (QoS). Lin et al (2010) proposed adaptive reliable routing based on cluster hierarchy for WMSN. The cluster structure is formed based on the cellular structure to enhance performance. The prediction of the remaining energy of the other nodes, adaptation of flat and clustering based routing are making this protocol as high efficiency on energy equilibrium and reliability in WMSN. Cobo et al (2010) proposed the antbased routing strategy for the WMSN by incorporating multiple QoS metrics. However, this approach does not

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support the multi-sink and also does not consider the node mobility. Li and Wang (2010) proposed a load balancing based hierarchical routing (LBHR) algorithm for WMSNs by the improved ant colony optimization algorithm to find a primary path and some backup paths while the intra-cluster routing is built by the minimum spanning tree algorithm. Kandris et al (2011) proposed energy efficient and perceived QoS-aware video routing by combining energy aware hierarchical routing protocol with an intelligent video packet scheduling algorithm to manage the network load according to the energy residues of the nodes and prevent useless data transmissions. Lin et al (2011) presented the energy efficient QoS assurance routing based on cluster hierarchy (EEQAR) to achieve energy efficiency and to meet the requirements of QoS. Huang et al (2014) introduced QoS-aware routing algorithm based on ant-clusters. Chen and Lai (2014) proposed a fuzzy logic controller approach with the traffic load parameter as priority to control congestion. This scheme meets the QoS requirements for network transmission by mitigating congestion, improving upon packet loss probability and reducing average queuing delay. Macit et al (2014) introduced a novel approach to set different reliability values for image packets for image transmission in WMSNs. Through using this prioritization, the important parts of an image which are assigned a higher priority are transmitted. The vast analysis of the research models in literature infuses a clear path for the development of EEPWDGR and EELB-PWDGR.

III.

Proposed System Model

3.1. Energy Efficient Pair-wise Directional Geographical Routing The GPS utilizes more energy for node localization in PWDGR, thus resulting in the cost of GPS devices and the non-availability of GPS signals in confined environments becoming serious issues. Hence the novel Energy Efficient Pairwise Directional Geographical Routing (EE-PWDGR) has been proposed. 3.1.1. Initialization In EE-PWDGR, the Self Positioning Algorithm (SPA) is utilized for the localization of the nodes based on the coordinate information without GPS receivers. However, in SPA algorithm since each node participates individually in the process of building and merging the local coordinate system the communication cost and convergence time grow exponentially with the number of nodes. The cluster based SPA algorithm is proposed based on the work of Iyengar and Sikdar(2003). The main concept is the collection of local coordinates and determination of global coordinates to locate the sensor nodes. The following assumptions are made in order to collect the local coordinate details in the proposed model. • The observed network is a network of wireless devices • All the nodes are stationary • There are no landmarks for absolute location information of a node. • All the nodes have the same technical characteristics • All the wireless links between the nodes are bidirectional • The nodes use Omni-directional antennae • Priority is assigned to multimedia data packets In this technique, the formation of local coordinates is only for a small subset of total nodes. The sensor nodes are deployed randomly with a given average density, after which, a random waiting timer is started to decrement in each sensor. When the timer value becomes zero of node i, the sensor i broadcasts a message MSG1 with a multiplication factor α ≥ 1 proclaiming that it is beginning a master node, a central point of a new cluster. All nodes in the transmission range of node i which receive this message become a slave node. Some nodes are assigned as border nodes which can be utilized for merging the coordinate systems and for detecting of coordinates depend on the hop distance. Thus, the nodes with different hops, 1-hop, 2-hop, and 3-hop are clustered, and the clustering process can be summarized as follows: 1) Each sensor initializes a random waiting timer WTi = (0,Tmax) and initial status of i is Si= none .i=1,2,3 ..n. 2) Decrease all random waiting timer WTi 3) Master node validation If random waiting timer expires WTi=0 i) Si = Master ii) Broadcast MSG1 with multiple factor α iii) Reset waiting timer WT End if

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4) Update neighbor identification If a node j receives MSG1 at time t i) If (Sj = =slave ) Sj= border Else Sj = slave node End if ii) Transmit MSG2 messages to neighbors with dij // dij is distance between a and j iii) Update WTj (t+1) = α WTj (t) iv) If WTj (t+1) >Tmax Reset waiting timer End if End if 5) Checking of stopping criteria If the waiting timers of all sensors are reset Stop Else t = t+ 1 and go to step (2) End if If the multiplication factor α = ∞, when receiving an MSG1 sent from master node Si at time period t, the waiting timer value of all neighbor nodes are greater than the Tmax value. Thus neighbor nodes do not become the master nodes any more. If the multiplication factor α is the distance between two nodes, the chance for master node is higher to change as master node again. In order to maintain the number of slave nodes and master nodes stable, provide a small α(t) during the early stages and a large α(t) during the later stages. In the clustering phase, the multiplication factor α performs an essential function in adjusting the number of master nodes and slave nodes and the degree of connectivity amongst master nodes and lowering communication overhead. 3.1.2. Node Localization 3.1.2.1. Local Coordinate System Each node in the selected WMSN builds its own Local Coordinate system based on the triangulation method. The node becomes the center of its own coordinate system with the position (0,0) and the positions of its neighbors are computed accordingly. Let N be the set of all nodes in the network. If a node j can communicate directly in one-hop with node i, then j is called as the one-hop neighbor of i. ∀i ϵ N, K i is defined as the set of one-hop neighbors and di is the set of distances between i and each node j ϵ K i . The neighbors will also be detected via utilizing beacons. After the absence of an exact number of successive beacons, it can be concluded that the node is no longer a neighbor. At each node, the one-hop neighbors K i are detected, the distances di to one-hop neighbors are measured andK i and di are sent to all one-hop neighbors.

Two nodes p, q ϵ K i are selected such that the distances between the p and q (dpq ) are known and is larger than zero. Further, it is also ensured the nodes i, p, q do not lie on the same line providing node i, defines its Local Coordinate System. The Local Coordinate System is defined such that the node p lies on the positive x-axis of the coordinate system and node q has a positive qy component. Thus, the Local Coordinate System of i is uniquely defined as a function of i, p and q.

Figure 1: Illustration of Obtaining Position of Node j in the Coordinate System of Node i

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In general, triangulation is the process of determining the position of a point in a plane or graph by measuring the angles to that point from the other known points without the need for direct measurement. The nodes are mapped as points of a triangle and the local coordinates of the points and the distance between the points are then detected to locate the position of the points. From Figure 1, the coordinates of the nodes i, p and q are given as: 𝑖𝑖𝑥𝑥 = 0; 𝑖𝑖𝑦𝑦 = 0; 𝑝𝑝𝑥𝑥 = 𝑑𝑑𝑖𝑖𝑖𝑖 ; 𝑝𝑝𝑦𝑦 = 0; 𝑞𝑞𝑥𝑥 = 𝑑𝑑𝑖𝑖𝑖𝑖 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐; 𝑞𝑞𝑦𝑦 = 𝑑𝑑𝑖𝑖𝑖𝑖 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

(1)

whereγ is the angle considering nodes (p, i, q) it is given by cosine rule for triangles. 𝛾𝛾 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

2 2 2 𝑑𝑑 𝑖𝑖𝑖𝑖 +𝑑𝑑 𝑖𝑖𝑖𝑖 −𝑑𝑑 𝑝𝑝𝑝𝑝

(2)

2𝑑𝑑 𝑖𝑖𝑖𝑖 𝑑𝑑 𝑖𝑖𝑖𝑖

The positions of the node j ϵ K i , j ≠ p, q, are computed by the triangulation. 𝑗𝑗𝑥𝑥 = 𝑑𝑑𝑖𝑖𝑖𝑖 cos 𝛼𝛼𝑗𝑗

(3)

𝑖𝑖𝑖𝑖 𝛽𝛽𝑗𝑗 = �𝛼𝛼𝑗𝑗 − 𝛾𝛾� 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

𝑑𝑑 sin 𝛼𝛼𝑗𝑗 𝑗𝑗𝑦𝑦 = � 𝑖𝑖𝑖𝑖 −𝑑𝑑𝑖𝑖𝑖𝑖 sin 𝛼𝛼𝑗𝑗

(4)

Where 𝛼𝛼𝑗𝑗 is the angle ∠(𝑝𝑝, 𝑖𝑖, 𝑗𝑗) and 𝛽𝛽𝑗𝑗 is the angle∠(𝑗𝑗, 𝑖𝑖, 𝑞𝑞) the values of 𝛼𝛼𝑗𝑗 and 𝛽𝛽𝑗𝑗 can be computed using cosine rule 𝛼𝛼𝑗𝑗 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝛽𝛽𝑗𝑗 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

2 2 2 𝑑𝑑 𝑖𝑖𝑖𝑖 +𝑑𝑑 𝑖𝑖𝑖𝑖 −𝑑𝑑 𝑗𝑗𝑗𝑗

(5)

2 2 2 𝑑𝑑 𝑖𝑖𝑖𝑖 +𝑑𝑑 𝑖𝑖𝑖𝑖 −𝑑𝑑 𝑗𝑗𝑗𝑗

(6)

2𝑑𝑑 𝑖𝑖𝑖𝑖 𝑑𝑑 𝑖𝑖𝑖𝑖

2𝑑𝑑 𝑖𝑖𝑖𝑖 𝑑𝑑 𝑖𝑖𝑖𝑖

The Limited power ranges of the nodes reduce the total number of one-hop neighbors for which node i is able to compute the position. A set of all possible combinations of p and q for node iis then shown as set Ci below: 𝐶𝐶𝑖𝑖 = {(𝑝𝑝, 𝑞𝑞) ∈ 𝐾𝐾𝑖𝑖

𝑠𝑠𝑠𝑠𝑠𝑠ℎ 𝑡𝑡ℎ𝑎𝑎𝑎𝑎 𝑝𝑝 ∈ 𝐾𝐾𝑞𝑞 }

(7)

0 ≤ |𝐶𝐶𝑖𝑖 | ≤ |𝐾𝐾𝑖𝑖 |

where|Ci | is the cardinality of the set Ci and K i is the list of neighbors. The selection of p and q should maximize the number of the nodes for which, the position can be computed as (𝑝𝑝, 𝑞𝑞) = 𝑎𝑎𝑎𝑎𝑎𝑎 max(𝑝𝑝 𝑘𝑘 ,𝑞𝑞 𝑘𝑘 )𝜖𝜖𝐶𝐶𝑖𝑖 |(𝑝𝑝𝑘𝑘 , 𝑞𝑞𝑘𝑘 )|

(8)

Where pk, qk are the different combinations of nodes in the local view set of the neighbors. 3.1.2.2. Global Coordinate System Using the collected local coordinates of clusters, a transformation matrix is constructed among the clusters. The two local coordinate systems are efficiently merged to form the global coordinate system. After building the local coordinate system, their positions are first set as (0,0) and their coordinate systems have different directions. The two coordinate systems have the same directions only if the directions of their x-axis and y-axis are the same. As mentioned earlier, the organization of the coordinate system is converted into a mathematical optimization problem and solved using a linear matrix equation. For a coordinate p(px,py), the transformation equation can be given as �𝑝𝑝𝑥𝑥′ 𝑝𝑝𝑦𝑦′ 1� = �𝑝𝑝𝑥𝑥 𝑝𝑝𝑦𝑦 1�. 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 = �𝑝𝑝𝑥𝑥 𝑝𝑝𝑦𝑦 1�. �

𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 1 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 3

𝑎𝑎 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 2 � = �𝑝𝑝𝑥𝑥 𝑝𝑝𝑦𝑦 1� � 𝑐𝑐 𝑇𝑇𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 4 𝑙𝑙

𝑏𝑏 0 𝑑𝑑 0�(9) 𝑚𝑚 1

Where Tmtr is a 3x3 matrix called transformation matrix. Tmtr can be divided into divided into four sub-matrices a b � representing rotation, reflection, shearing and non-uniform scaling transformation. 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 2 = with 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 1 = � c d [l m]representing the translation transformation. 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 3 = �0�representing projection transformation, and𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 4 = 0

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a b 0 [1] representing uniform scaling transformation.Tmtr can be simplified as �−b a 0� because of rotation of θ l m 1 radians counter-clockwise about the origin.

Figure 2: Merging of Local Coordinate Systems The two Cartesian coordinate systems (Figure 2) can be merged using the three groups coordinates of the same sensor nodes in two different coordinate systems. These nodes are known as border nodes. Let i and k be the two master nodes. The direction of the coordinate system of k is changed without any loss of generality to the local coordinate system of i. Assuming three nodes j1, j2, and j3 as the border nodes whose coordinates in the two 1′ 2 2 2′ 2′ 3 3 3′ 3′ systems are �j1x , j1y �, �j1′ x , jy �, �jx , jy �, �jx , jy �, �jx , jy �, and �jx , jy � , the transformation equation obtained can be represented as following: 𝑗𝑗𝑥𝑥1′ �𝑗𝑗𝑥𝑥2′ 𝑗𝑗𝑥𝑥3′

𝑗𝑗𝑦𝑦1′ 𝑗𝑗𝑦𝑦2′ 𝑗𝑗𝑦𝑦3′

1 𝑗𝑗𝑥𝑥1 1� = �𝑗𝑗𝑥𝑥2 𝑗𝑗𝑥𝑥3 1

𝑗𝑗𝑦𝑦1 𝑗𝑗𝑦𝑦2 𝑗𝑗𝑦𝑦3

1 𝑎𝑎 1� . �−𝑏𝑏 𝑙𝑙 1

𝑏𝑏 𝑎𝑎 𝑚𝑚

0 0� 1

(10)

In order to compute the positions of the nodes in the global coordinate system, the directions are adjusted to obtain the same direction for all the nodes in the network. The coordinate system directions for two nodes i and j are computed by adjusting the direction of the node k to be as in the same direction of node i, as shown in Figure 3. The direction of the coordinate system is corrected after the detection of the position of node j in the coordinate system of i and k. The correction angle is determined by the following conditions. If αj − αk < 𝜋𝜋 and βj − βi > 𝜋𝜋 or αj − αk > 𝜋𝜋and βj − βi < 𝜋𝜋, then the mirroring technique is not required and the correction angle is βi − αk + π. If αj − αk < 𝜋𝜋 and βj − βi < 𝜋𝜋 or αj − αk > 𝜋𝜋and βj − βi > 𝜋𝜋, then the mirroring technique is required and the correction angle is βi + αk .

Figure 3: Position of Node j in Local Coordinate System of i and k Once node k has rotated its local coordinate system by the correction angle and has mirrored it if the nodes i and k have the same direction of their Local Coordinate Systems, the same procedure can be repeated for all the nodes in the network in these respective order.

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3.1.2.3. Position Computing The position of the nodes in the global coordinate system is computed since The global coordinate system is selected as the local coordinate system of a node i, it is important that the nodes in the network adjust the directions of their coordinate systems to the direction of the coordinate system of i and computes its position. Let the nodes k be one-hop neighbor, while node l be the two-hop neighbor of node i, as represented in Figure 4. Node i knows its position in the coordinate system of node k and node i. As the coordinate systems of nodes since k and i have the same directions, the position of the node l in the coordinate system of the node i is obtained as the sum of two vectors.

Figure 4: Position Computation when the Local Coordinate Systems have Same Distance �⃗ = 𝑖𝑖𝑖𝑖 ���⃗ + 𝑘𝑘𝑘𝑘 ���⃗ 𝑖𝑖𝑖𝑖 (11)

The approach is applied to the 3-hop neighbors of node i, if the coordinate system l has the same direction as the coordinate systems of i and k. These nodes will receive the position of node l in the coordinate system of node i and add this vector to their vector in the coordinate system of node l. Thus, the nodes can detect their positions in the coordinate system of node i and by repeating this procedure, positions of all the nodes in the coordinate systems are computed. 3.1.3. Loop-free Route Construction Loops in the routing paths decrease routing efficiency by increasing packet delivery delay and reducing the number of successful packet deliveries. Hence the loop-free routes are constructed in this approach(Garcia-LunaAceves et al., 2003). When the position of all the nodes in the network are detected, the route from the source nodes to the sink nodes is constructed based on the route request messages. The sequence numbers are given for the packets based on the current round of broadcasting. The approach is followed by the initialization for the construction of a tree like structure that leads to the destination. Based on this tree, the paths are selected and the path for a particular transmission is decided by the energy consumption in that path. If path has node A, B and destination D, then the loop free path is constructed by checking few conditions. For a given destination D for which a node has a route, it maintains the sequence number originated by D and is represented as snAD , its distance to D is represented as dAD , its next hop to D and its feasible distance fdAD to D. The feasible distance fdAD is the minimum distance known for the current sequence numbersnAD .

The loop-free routes are determined based on sufficient conditions. The sufficient invariant conditions like source node conditions (SDC), feasible distance conditions (FDC) and numbered distance conditions (NDC) are normally utilized for determining loop-free conditions. In this approach, the conditions are utilized to the creation of routing table loops. Node A accepts advertisements from neighbor B for destination D and updates its routing tables independent of the other nodes if A has no information about D. The conditions (12) and (13) are needed to be satisfied. 𝑠𝑠𝑠𝑠𝐷𝐷𝐵𝐵 > 𝑠𝑠𝑠𝑠𝐷𝐷𝐴𝐴

𝑠𝑠𝑠𝑠𝐷𝐷𝐵𝐵 = 𝑠𝑠𝑠𝑠𝐷𝐷𝐴𝐴 ∧ 𝑑𝑑𝐷𝐷𝐵𝐵 < 𝑓𝑓𝑓𝑓𝐷𝐷𝐴𝐴

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(12) (13)

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NDC is used to allow the nodes to change successive nodes without coordination among the nodes. FDC allows enforcing the ordering of the feasible distance of all the nodes along a path to the destination. SDC is employed to allow a node that does not have a neighbor satisfying NDC to find a distant node that can provide a loop-free path(LP) to the destination D. The loop-free route construction is carried out as follows: 1) 2) 3) 4)

Initialize nodes for path construction For node A Estimate 𝑑𝑑𝐷𝐷𝐴𝐴 , 𝑓𝑓𝐷𝐷𝐴𝐴 , 𝑠𝑠𝑠𝑠𝐷𝐷𝐴𝐴 , next-hop Condition for loop-free If invariant conditions (12) & (13) satisfied Node A→ LPA //A is suitable for loop-free path End if Next node A+1 5) Construct loop-free paths 3.1.4. Disjoint Route Construction The problem of redundancy leads to the formation of reliable and fault tolerant paths. This problem in WMSN can be overcome by the node-disjoint concept of route building (Challal et al., 2011). Though many approaches focus on discovering node disjoint paths, the number of alternate paths is always limited. This Study utilizes the concept of “one message per sensor” and avoids packet duplication, to enable more alternate paths without altering the tolerance level. The node-disjoint paths are constructed between the sensor node i and the sink node from available loop free paths. Firstly, the probability of disconnection P is computed for i. If node i use full duplication for sending packets, the probability of node disconnection is given as 𝑃𝑃𝑖𝑖 = ∏𝐿𝐿𝐿𝐿=𝐿𝐿𝐿𝐿 𝑖𝑖 (1 − (1 − 𝜏𝜏)|𝐿𝐿𝐿𝐿| )

(14)

The selection of random parent node is then carried out inorder to balance the load transmission energy over the nodes. After the selection of random parent node p from LP paths to transmit packets, the probability node disconnection becomes 1

𝑃𝑃𝑖𝑖𝑖𝑖 = ∑𝐿𝐿𝐿𝐿=𝐿𝐿𝐿𝐿 𝑖𝑖 (1 − (1 − 𝜏𝜏)|𝐿𝐿𝐿𝐿| ) × |𝐿𝐿𝐿𝐿 | 𝑖𝑖

(15)

Finally, the path loss tolerance probability(PLTP) for the disconnection of nodes is considered for the transmission. Thus the node-disjoint paths are constructed. The disjoint multipath routes are constructed as follows: 1) 2) 3) 4) 5) 6) 7) 8)

Select N nodes Select all LPs Compute 𝑃𝑃𝑖𝑖 for nodes in LP using (14) Compute Pip for nodes with parent nodes to construct disjoint path using (15) Consider the threshold level for path loss tolerance probability ( minPLTP) If Pip (𝑖𝑖) ≥ minPLTP Node i→ 𝑃𝑃𝑃𝑃𝑃𝑃ℎ 𝐷𝐷𝐷𝐷𝑖𝑖 (disjoint route) Repeat step 3to add all nodes in DRs

3.1.5. Route Discovery The nodes in the selected WMSN which are requested to discover the energy efficient routes are discovered by sending route request packets in other networks while the WMSN are larger and placed at geographically distant locations. Initially, the source nodes which are required to transmit the packets broadcasts the route request (RREQ) packets to the other nodes. The nodes which can act as routers reply with the route reply (RREP) packets that contain the sequence number of the nodes. The packets are received at the source which selects the nodes with the highest sequence number for data forwarding. However, the broadcast of RREQ packets is possible only when the

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location of the nodes is known. Hence, the detection of nodes’ positions using is important for constructing of the routing paths. The proposed Energy efficient node localization algorithm efficiently locates the position of the nodes 3.1.6. Route Selection The constructed multi-paths for the transmission of multimedia contents are selected based on the level of satisfaction of the QoS factors. In PWDGR, as GPSR is used and it considers the distance d between the forwarding node and the destination node for the efficient selection of the transmission paths. As the approach consumes more energy, the energy of the forwarding nodes drains at a faster rate, which is not considered in GPSR. In EE-PWDGR, the forwarding node is selected depending on the weight values of the nodes based on the energy drain rate and the distance between the forwarding node and the destination node. The distance between the forwarding node and the destination node can be computed as explained in section 3.1.2. The energy drain rate of a node can be calculated by estimating the energy consumed during transmission 𝐸𝐸𝑡𝑡, the energy consumed during reception 𝐸𝐸𝑟𝑟, the energy spent when the nodes are in idle state 𝐸𝐸𝑖𝑖, and the energy spent for data sensing 𝐸𝐸𝑠𝑠. The energy drain rate 𝐸𝐸d is given by 𝐸𝐸𝑑𝑑𝑑𝑑𝑑𝑑 = 𝐸𝐸𝐸𝐸 + 𝐸𝐸𝐸𝐸 + 𝐸𝐸𝐸𝐸 + 𝐸𝐸𝐸𝐸

(16)

In the energy consumption model, 𝐸𝐸𝑟𝑟, 𝐸𝐸𝑖𝑖, and 𝐸𝐸𝑠𝑠 are constant and 𝐸𝐸𝑡𝑡 varies based on the distance covered during transmission. The 𝐸𝐸𝐸𝐸 value and the distance D should be a minimum for efficient transmission. The route selection is performed as follows: 1) 2) 3) 4)

Consider for all nodes in each DR Compute distance d between each nodei in DR& destination node Calculate energy drain rate for each node i in DR using (16) If ( 𝐸𝐸𝑑𝑑𝑑𝑑𝑑𝑑 ≥ 𝐸𝐸𝑑𝑑𝑑𝑑 𝑚𝑚𝑚𝑚𝑚𝑚 && 𝑑𝑑 ≤ 𝑑𝑑𝑚𝑚𝑚𝑚𝑚𝑚 ) Add Node i in to𝐹𝐹𝐹𝐹𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 // FWN –Forwarding Node Else i=i+1 End if 5) Repeat step2 for all DRs The 𝐸𝐸𝑑𝑑 value and the distance d should be a minimum for selected route for efficient transmission.

3.2. Energy Efficient Load Balancing Pairwise Directional Geographical Routing (EELB-PWDGR) EELB-PWDGR performs the similar processes as in the case of EE-PWDGR. The appropriate routes are selected based on energy drain rate, distance measures, path reliability, average delay and link quality. The QoS parameters are computed for all the data paths and then the fuzzy logic is generated for each parameter and used to determine the priority for the path selection. 3.2.1. Load Balancing based Route Selection A mechanism for prioritizing image content is also proposed to improve the load balancing. Using this mechanism, different reliability values are set for image packets for image transmission. The important parts of an image are assigned high reliability which is provided to high-priority data traffic. To realize this, the data packets of an image file are divided into different classes such as low, medium and high. Fuzzy logic is used to decide the reliability values to the source packets based on the type of media, thus, helping in obtaining different reliability values set for each class. After selecting multiple the paths from EEPWDGR, the following metrics are calculated, and the priority of the path is then calculated using the formula Pp=w1 * Rp

+

w2 *1/𝐴𝐴𝐴𝐴 + w3 * Pq

(17)

where ∑𝑖𝑖=1 𝑡𝑡𝑡𝑡 3 𝑤𝑤𝑖𝑖 = 1.

Path Reliability Path reliability (Rp) is the sum of packet loss rate and the bit error rate (BER) in the path p.

where

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𝑅𝑅𝑝𝑝 = 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 + 𝐵𝐵𝐵𝐵𝐵𝐵

(18)

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𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

𝐵𝐵𝐵𝐵𝐵𝐵 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇

Average Delay The average delay is given by

𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

1

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝐴𝐴𝐴𝐴 = (𝜇𝜇 −𝜆𝜆)

(19) (20)

(21)

where μ is the number of packets handled per second and λ is the average rate at which the packets are arriving to the path. Link Quality Link quality (QL) can be estimated in terms of the packets received undamaged in a link during t seconds. 𝑄𝑄𝐿𝐿 = max ⁡( 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑖𝑖𝑖𝑖 𝑡𝑡

𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑖𝑖𝑖𝑖 𝑡𝑡,𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑖𝑖𝑖𝑖 𝑡𝑡)

(22)

Link Quality for the whole network is computed as Link Quality𝑃𝑃𝑞𝑞 = ∑𝑛𝑛𝑖𝑖=1 𝑄𝑄𝐿𝐿𝐿𝐿

(23)

Figure 5: Fuzzy Inference System

Figure 6: Input Membership Functions for Frame and Buffer Size The path priority is applied on available paths which selects based on the energy drain rate and distance measures are obtained as in EE-PWDGR. Further, the path priority is converted to fuzzy membership value. Fuzzy inference system is shown Figure 5. Fuzzifier converts the buffer size and frame size into fuzzy membership value which is represented as the labels of fuzzy sets. The path priority value is also converted as fuzzy value asks like buffer size and frame size. In Figure 6, the input parameters frame size and buffer size is represented as fuzzy parameters. The frame size of the video frames is represented as f0,f1,f2 and f3. The buffer sizes initialized for sensor nodes during transmission are referred as b0, b1, b2 andb3. The rule for priority to the arrived frame is shown in Table 1. After crisp inputs are mapped to the linguistic values by way of membership functions within the fuzzification step, the inference rule is applied to check the output by using rule base. The guideline-base is an algorithm that emulates the choice making the approach of the human trained controlling the procedure.

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Table 1: Fuzzy Rule Base Frame Size L L L M M M H

Buffering Size H M L H M L H

Path Priority L M M M H H H

Algorithm: Proposed Routing Model Initialize nodes N Set coordinates as (0,0) //Geographical information using SPA Compute local coordinates (p,q) of nodes using equations (3) and (4) Construct transformation matrix using equations (9) Estimate global coordinates of nodes using transformation equation (10) For Triangulation of nodes i, j, p, q If ( 𝛼𝛼𝑗𝑗 − 𝛼𝛼𝑘𝑘 < 𝜋𝜋 and 𝛽𝛽𝑗𝑗 − 𝛽𝛽𝑖𝑖 > 𝜋𝜋 or 𝛼𝛼𝑗𝑗 − 𝛼𝛼𝑘𝑘 > 𝜋𝜋 and 𝛽𝛽𝑗𝑗 − 𝛽𝛽𝑖𝑖 < 𝜋𝜋) Correction angle = 𝛽𝛽𝑖𝑖 − 𝛼𝛼𝑘𝑘 + 𝜋𝜋. Else if (𝛼𝛼𝑗𝑗 − 𝛼𝛼𝑘𝑘 < 𝜋𝜋 and 𝛽𝛽𝑗𝑗 − 𝛽𝛽𝑖𝑖 < 𝜋𝜋 or 𝛼𝛼𝑗𝑗 − 𝛼𝛼𝑘𝑘 > 𝜋𝜋 and 𝛽𝛽𝑗𝑗 − 𝛽𝛽𝑖𝑖 > 𝜋𝜋) Correction angle = 𝛽𝛽𝑖𝑖 + 𝛼𝛼𝑘𝑘 Else Correction angle = 0 End if End for Determine position of nodes using equation (11) For any node A in coordinate of i Estimate 𝑑𝑑𝐷𝐷𝐴𝐴 , 𝑓𝑓𝐷𝐷𝐴𝐴 , 𝑠𝑠𝑠𝑠𝐷𝐷𝐴𝐴 , next-hop If (𝑠𝑠𝑠𝑠𝐵𝐵𝐷𝐷 > 𝑠𝑠𝑠𝑠𝐷𝐷𝐴𝐴 &&𝑠𝑠𝑠𝑠𝐵𝐵𝐷𝐷 = 𝑠𝑠𝑠𝑠𝐷𝐷𝐴𝐴 ∧ 𝑑𝑑𝐷𝐷𝐵𝐵 < 𝑓𝑓𝑓𝑓𝐷𝐷𝐴𝐴 ) Node A→ 𝐿𝐿𝐿𝐿𝐴𝐴 Else Next node i+1 End if End for //Disjoint route construction For node i Compute 𝑃𝑃𝑖𝑖 for nodes in LP using equation (14) For each parent ip in coordinate system of i Compute probability Pip using equation (15) Check for minPLP If (If Pip (𝑖𝑖) ≥ minPLTP Node i→ 𝑃𝑃𝑃𝑃𝑃𝑃ℎ 𝐷𝐷𝐷𝐷𝑖𝑖 (disjoint route) Else Node= i+1 End if End for End for //EE-PWDGR //Route Selection For node i Compute Edr using equation (16) Compute distance di between node i to neighbor nodes If ( 𝐸𝐸𝑑𝑑𝑑𝑑𝑑𝑑 ≥ 𝐸𝐸𝑑𝑑𝑑𝑑 𝑚𝑚𝑚𝑚𝑚𝑚 && 𝑑𝑑 ≤ 𝑑𝑑𝑚𝑚𝑚𝑚𝑚𝑚 ) Add Node i in to FWNlist // FWN –Forwarding Node Else i=i+1 End if End for

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//EELB-PWDGR //Route Selection For paths 𝐷𝐷𝐷𝐷𝑖𝑖 in DR// DR -Disjoint routes ComputePPpth using equations equation (17) //Generation of fuzzy set value for Pp and Fuzzy set value Apply Fuzzy rule (Table 1) If ( Frame size = = L) If (Buffering Size = = L) Path priority =M Else If (Buffering Size = = H) Path priority =L Else Path priority =M Else If ( Frame size = = M ) If (Buffering Size = = L) Path priority =H Else If (Buffering Size = = M) Path priority =H Else Path priority =M Else Path priority =H End if End for End

EELB-PWDGR is the same as EE-PWDGR except for the QoS parameter and the usage of fuzzy logic based decision making for load balancing. The local coordinate system collects information, global coordinate’s information collection then calculating the position of node information and energy calculation. The calculation of QoS parameter based on path priority calculation and the fuzzy based decision are described in this algorithm in a step by step manner.

IV.

Performance Evaluation

The experiments are conducted using the NS-2 simulator. The performance of the proposed routing techniques EE-PWDGR and EELB-PWDGR are evaluated and compared with PWDGR in terms of end-to-end delay, PSNR, energy per packet, hop count and network lifetime in order to determine the efficiency. 4.1. Simulation Environment The system model of EE-PWDGR and EELB-PWDGR are similar to that of PWDGR. In the proposed simulation network model, only some video monitoring nodes (VN) cover the monitoring field and the ability of battery geared up for VN node is better than that of common nodes. The tasks of other common nodes are transmitting data to the sink by means of many hops and the sink node contains endless energy. Let us assume that all source nodes coordinate to send data packets at different time slices. The video monitoring node (source node) and sink are placed on two edges of simulation are. In the proposed approach, both the reception end node and common sensor node are both assumed to be static. The realized sensor node is a 4-layer protocol. Sensor application module is composed of a data source with a fixed bit rate and its responsibility is to produce a kind of multi-medium stream with certain QoS demands. Table 2 shows the energy consumption model. The energy of all the common nodes is equal except for the sink node. The node energy consumption computed using the sending time, receiving time, idle time and overhearing time for the packet transmission. Table 2: Energy Consumption Model Primary energy of common node Primary energy of video node Energy consumption at sending unit time Energy consumption at receiving unit time Energy consumption at overhearing unit time Energy consumption at idle unit time

0.2 w 5w 0.660 w/s 0.395 w/s 0.195 w/s 0.035 w/s

The evaluation of the PWDGR, EE-PWDGR, and EELB-PWDGR is performed in NS-2 with the following simulation environment shown in Table.3.

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Table 3: Simulation Environment Network Area (Size) Network Topology MAC Layer (IEEE Standard) Data rate at MAC layer Link Layer IFQ Type IFQ Length Energy model Propagation model Bandwidth Application Type CBR interval No. of Packets packet generation rate Packet loss rate Simulation Time Data Transfer Protocol No. of Sink Nodes No. of Source Nodes No. of Sensor Nodes Maximum Transmission range Packet Size No. of Paths

1000X1000 m2 Randomized IEEE 802.11 2 Mbps LL (Link Layer) Queue/DropTail/PriQueue 50 Energy Model (Table 4 ) Free-space propagation model 2 MB Constant bit rate (CBR) 1.0 (second) 1500 125 – 140 packets /seconds 0.15% 350 s TCP/UDP 1 6 30 100m (35m-100m is used in simulation ) 2MB 9

The simulation parameter is initialized by referring the simulations in Al-Ariki and Swamy(2017), PWDGR (Wang et al., 2015), and DGR (Chen et al., 2007).The network area of 1000 m x 1000 m is considered for the simulation. The network area consists of maximum 30 sensors randomly deployed and maximum transmission range of each sensor is assumed to be 100 m. The 6 source nodes (video nodes) are initialized for multimedia transmission to sink. The sink is located at a distance D from the center of network area. In the simulation, the value of D is considered to be equal between center and sources, center and sink nodes. The free space propagation model is used in this simulation. The transmission range of sensor nodes, packet loss rate, and packet generation rate and are initialized for conducting simulation. 4.2. Performance Metrics End-to-end delay refers to the time taken for a packet to be transmitted across a network from source to destination generally due to queuing and retransmission due to collision. ∑𝑛𝑛 (𝑡𝑡 −𝑡𝑡 )

𝐸𝐸𝐸𝐸𝐸𝐸 − 𝑡𝑡𝑡𝑡 − 𝑒𝑒𝑒𝑒𝑒𝑒 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = 𝑖𝑖=1 𝑛𝑛𝑟𝑟𝑟𝑟 𝑠𝑠𝑠𝑠 (24) Where t ri is the receive time of i-th packet, t si is the sending time of i-th time and n is the total number of packets.

End to End Delay in ms

200

PWDGR 1-hop EE-PWDGR 1-hop EELB-PWDGR 1-hop PWDGR 2-hop EE-PWDGR 2-hop EELB-PWDGR 2-hop PWDGR 3-hop EE-PWDGR 3-hop EELB-PWDGR 3-hop PWDGR 4-hop EE-PWDGR 4-hop EELB-PWDGR 4-hop

180 160 140 120 100 80

10

15 20 25 Number of sensor nodes

30

Figure 7: End-to-end Delay

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Figure.7. composes PWDGR, EE-PWDGR, and EELB-PWDGR in terms of End-to-end delay. The delay of EELB-PWDGR is always lower than EE-PWDGR and PWDGR though the average path length in EELB-PWDGR is higher than the length of the shortest path. This is become that the average bandwidth provided to the selected path is very close to the bandwidth required by a video stream. Therefore the link congestion and video frame corruption due to burst packet losses are handled by QoS based load balancing technique in EELB-PWDGR. Thus the delay of EELB-PWDGR stays relatively constant as the packet number of node changes. It can be seen that endto-end delay from 1-Hop to 4-Hop of the two proposed approaches are less than that obtained in PWDGR due to the adoption of considering energy efficient and QoS-aware path selection strategy in the proposed approaches. PSNR Peak signal-to-noise ratio (PSNR) is the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation, and is expressed in terms of the logarithmic decibel scale. 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (𝑑𝑑𝑑𝑑) = 20 log10

𝑀𝑀𝑀𝑀𝑀𝑀 = 𝑁𝑁

1

1 ×𝑁𝑁2

2 𝑠𝑠 −1

√𝑀𝑀𝑀𝑀𝑀𝑀

∑𝑖𝑖𝑁𝑁1 ∑𝑖𝑖𝑁𝑁2 [𝑋𝑋(𝑖𝑖, 𝑗𝑗) − 𝑋𝑋�(𝑖𝑖, 𝑗𝑗)]2

(25) (26)

�(i, j)are the pixel value of the reconstructed Where N1 × N2 is the number of pixels in an image, X(i, j) and X image at the encoder and decoder. Logically, a higher estimation of PSNR is good since it implies that the proportion of Signal to Noise is higher. The comparison of PWDGR, EE-PWDGR, and EELB-PWDGR in terms of PSNR with respect to the number of nodes 1-Hop, 2-Hop, 3-Hop and 4-Hop level is shown in Figure.8.

50

PWDGR 1-hop

45

EE-PWDGR 1-hop

PSNR in db

EELB-PWDGR 1-hop

40

PWDGR 2-hop

35

EE-PWDGR 2-hop EELB-PWDGR 2-hop

30

PWDGR 3-hop

25

EE-PWDGR 3-hop EELB-PWDGR 3-hop

20

PWDGR 4-hop

15

EE-PWDGR 4-hop EELB-PWDGR 4-hop

10 10

15

20

25 30 Number of sensor nodes

Figure 8: PSNR Figure.8. shows the comparison of PWDGR, EE-PWDGR, and EELB-PWDGR in terms of PSNR value. EELBPWDGR is higher than that of PWDGR and EE-PWDGR with varying number of nodes. This is because the average path length of EELB-PWDGR, which is equal to the total path length, is more stable than the path length of the PWDGR and EE-PWDGR. This somewhat increased energy consumption is the price paid for considerably improved real-time video quality. In addition, the overall performance is greatly improved when energy, lifetime and video quality are jointly considered. From the figure, it can be seen that EE-PWDGR achieves higher PSNR than PWDGR which on an average is about 3 dB.EELB-PWDGR achieves the highest PSNR, which on average is about 3 dB higher than that of EEPWDGR, and6 dB higher than that of PWDGR for all hop level with varies number of a node used for simulation.

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Lifetime Lifetime refers to the time during which a network operates until the first sensor node or the group of nodes in the network runs out of energy. It can be simply defined as the overall network lifetime that is determined by the remaining energy in the network. 𝜀𝜀 −𝔼𝔼[𝐸𝐸 ]

0 𝑤𝑤 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝔼𝔼[𝐿𝐿] = 𝑃𝑃+𝜆𝜆𝜆𝜆[𝐸𝐸

(27)

𝑟𝑟 ]

where P is the constant continuous power consumption of the whole network, ε0 the total non-rechargeable initial energy, λ the average sensor reporting rate, 𝔼𝔼[Ew ] is the expected wasted energy or unused energy, when the network dies and 𝔼𝔼[Er ] is the expected reporting energy consumed by all sensors.

In simulation, Lifetime is considered for two cases for three approaches. The comparison of PWDGR, EEPWDGR, and EELB-PWDGR in terms of Lifetime with respect to Maximum transmission range for 1-Hop, 2-Hop, 3-Hop and 4-Hop level are shown in Figure.9. 140

PWDGR 1-hop EE-PWDGR 1-hop EELB-PWDGR 1-hop PWDGR 2-hop EE-PWDGR 2-hop EELB-PWDGR 2-hop PWDGR 3-hop EE-PWDGR 3-hop EELB-PWDGR 3-hop PWDGR 4-hop EE-PWDGR 4-hop EELB-PWDGR 4-hop

130

Lifetime in Sec

120 110 100 90 80 70 60 50 40 35

48

61

74

87

100

Max transmission rage in m Figure 9: Lifetime The network life shown in Fig.9compares the network life of PWDGR, EE-PWDGR, and EELB-PWDGR for 1Hop, 2-Hop, 3-Hop and 4-Hop levels. From the results, it can be concluded that the network life of EELB-PWDGR and EE-PWDGR is significantly prolonged and is stable when compares to PWDGR in all Hop levels. This is because the EE-PWDGR takes into consideration the rate of energy drain of the nodes while selecting the path. The GPS free localization helps in improving the longevity of the network. Furthers, the consideration of QoS parameters also helps in increasing the longevity of the network. An analysis of lifetime at different ranges of transmission from 35m to 100m for both PWDGR.1-Hop to EELBPWDGR 4-Hop when 9 paths are used, EE-PWDGR and EELB-PWDGR found to increase the lifetime by two times when compare to PWDGR. Further, from one hop to 4 hops, the proposed strategies were obtained to increase the lifetime of network by 50% comparison to the existing PWDGR. Further, the EELB-PWDGR increases the lifetime by 5% when compared to the EE-PWDGR. Hop Count Hop count is the number of intermediate nodes through which a data must pass between the source and the destination, rather than flowing directly over a single path. The fewer hop counts reduce the energy consumed. The number of hops to the sink node is one count less than the expected number of regions. 𝐻𝐻𝐻𝐻𝐻𝐻 = �𝑊𝑊 2

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𝐷𝐷

cos ( arcsin

4 ) 𝜌𝜌 𝑊𝑊 2

�−1

(28)

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�𝑊𝑊 2

where D is the distance to the sink node, W is the radio range, ρ is the density of node deployment, 1 2

𝐷𝐷

cos ( arcsin

� is the expected number of regions.

4 ) 𝜌𝜌 𝑊𝑊 2

The comparison of PWDGR, EE-PWDGR, and EELB-PWDGR in terms of Hop count with respect to Maximum transmission range for1-Hop, 2-Hop, 3-Hop and 4-Hop level is shown in Figure.10.

70

PWDGR 1-hop EE-PWDGR 1-hop

60

EELB-PWDGR 1-hop PWDGR 2-hop

50

Hopcount

EE-PWDGR 2-hop EELB-PWDGR 2-hop

40

PWDGR 3-hop EE-PWDGR 3-hop

30

EELB-PWDGR 3-hop PWDGR 4-hop

20

EE-PWDGR 4-hop EELB-PWDGR 4-hop

10 0 35

48

61

74

87

100

Max transmission rage in m Figure 10: Hop count Figure.10. shows the comparison of PWDGR, EE-PWDGR, and EELB-PWDGR in terms of Hop count value. The hop count of PWDGR is higher than the hop count of EE-PWDGR and EELB-PWDGR.A higher hop count means that the energy consumption is also high. Here in the route scheme, the average number of hop is 15.5 for PWDGR, while EE-PWDGR and EELB-PWDGR obtained13.5 and 10.5 respectively. The result from experiments, it can be concluded that the average number of hop exist from 1-Hop to 4-Hop is 4-10, 3-7 and 2-5 for PWDGR, EE-PWDGR, and EELB-PWDGR respectively for a maximum transmission range. EELB-PWDGR transmits the packets with flexible distance and transmitting angle by utilizing QoS parameters that reduce the overall hopcount. This clearly indicates that the EELB-PWDGR shows better performance when compares to PWDGR and EEPWDGR in terms of Hop count value. Energy Per Packet Energy per packet is the average energy consumption required for sending, receiving or forward operations of a packet to a node in the network during the period of time. 𝐸𝐸(𝑝𝑝𝑝𝑝) = [(2 ∗ 𝑝𝑝𝑝𝑝 − 1)(𝑒𝑒𝑡𝑡 + 𝑒𝑒𝑟𝑟 )𝑑𝑑 ∝ ]

(29)

where pi is the data packet, et is the energy for transmission of packet i, er is the energy for receiving the packet i and d is the distance between transmission node and the destination node.

The comparison of PWDGR, EE-PWDGR, and EELB-PWDGR in terms of Energy per packet with respect to Maximum transmission range for 1-Hop, 2-Hop, 3-Hop and 4-Hop level is shown in Figure.11.

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450 Energy per packet (w/10^6)

PWDGR 1-hop

400

EE-PWDGR 1-hop EELB-PWDGR 1-hop

350

PWDGR 2-hop

300

EE-PWDGR 2-hop

250

EELB-PWDGR 2-hop PWDGR 3-hop

200

EE-PWDGR 3-hop

150

EELB-PWDGR 3-hop PWDGR 4-hop

100

EE-PWDGR 4-hop EELB-PWDGR 4-hop

50 35

48 61 74 Max transmission rage in m

87

100

Figure 11: Energy per packet Figure.11. shows the comparison of PWDGR, EE-PWDGR, and EELB-PWDGR in terms of Energy per packet value. Energy per packet is estimated by the distribution of the total transmit energy required per data packet. This metric measures how much energy is utilized to reliably transmit one packet over a wireless link. Due to the selection of a reliable path in EELB-PWDGR, retransmission is also minimized, thus energy consumption per packet is 13% lesser than that obtained in PWDGR and 8% lesser than that obtained in EE-PWDGR. The experimental results conclude that the probability of obtaining reliable path is higher in EE-PWDGR and EELBPWDGR.

V.

Conclusion

This paper introduced the EE-PWDGR and. EELB-PWDGR routing protocols for resolving the problems of energy bottleneck and load balancing. The novel ideas used in these models enhanced the routing performance which is evident from the experimental results.EE-PWDGR and EELB-PWDGR provide better performance when compared to the PWDGR in terms of delay, PSNR, hop count, lifetime and energy. Though the proposed techniques are highly effective in reducing energy consumption and resolving energy bottleneck and load balancing problem, the performance of the proposed approaches with error estimation schemes has to be evaluated. Similarly, the route selection using optimization in order to transmit high priority multimedia content could also be an interesting concept for research. An appropriate scheduling algorithm has to be developed to handle simultaneous transmission of multimedia data at pairwise node for multiple sources. Such improvements can be of high interest for future researches.

Acknowledgement The authors wish to acknowledge J.S.S Research Foundation, J.S.S Technical Institutions Campus, S.J.C.E, Mysore-570006, Karnataka, India for all the facilities provided for this research work.

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Authors Biography Hasib Daowd Esmail Al-Ariki Completed his B.Sc. degree in Computer Science & Information System from University of Technology-Republic of Iraq in the year 2000, M.Sc. in Computer Communication from Bharathiar University-India in the year 2011. And he is presently working as an Assistant Professor in the Department of Computer Networks Engineering and Technologies, Sana’a Community College, Republic Of Yemen. He is doing his Ph. D in the area of Wireless sensor networks under the guidance of Dr.M.N. Shanmukha Swamy. His area of interest includes Wireless Sensor Networks, Wireless Communication, MANET, VANET, and Security in Networks. Dr. Abdo Saif Mohammed Completed his B.Sc. degree in Computer Science & Information System from University of Technology-Republic of Iraq in the year 2001, M.Sc. in Computer Communication from Bharathiar University-India in the year 2009 and obtained his PhD in the field of Wireless Sensor Networks from University of Mysore, in 2015 under the guidance of scientist Dr.M.N.Shanmukha Swamy. And He is presently working as Assistant Professor in the Department of Computer Science and information technology, Thamar University, thamar, Republic Of Yemen. His area of interest includes Wireless Sensor Networks, Wireless Communication, MANET, VANET and Security in Networks. Dr.M.N. Shanmukha Swamy completed his B.E. degree in Electronics and Communication from Mysore University in the year 1978, M.Tech in Industrial Electronics from the same university in the year 1987 and obtained his PhD in the field of Composite materials from Indian Institute of Science, Bangalore in 1997. He is presently working as Professor and HOD in the Department of Electronics and communication, Sri Jayachamarajendra college of Engineering, Mysore, Karnataka, India. He is guiding several research scholars and has published many books & papers both in National & International conferences & journals. His research area includes Wireless Sensor Networks, Biometrics, VLSI and composite materials for application in electronics.

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