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1. INTRODUCTION.

1.1 WIRELESS SENSOR NETWORK.

Wireless sensor networks(WSNs) consists of large number of sensor nodes which are low power, small in size with limited energy and computational power 1.These sensor nodes can sense, measure, and gather information from the environment and transmit the sensed data to the base station. Smart sensor nodes are low power devices equipped with one or more sensors, a processor, memory, a power supply, a radio, and an actuator. Wireless sensor networks have several applications like process management, Area monitoring, Health care monitoring, Environmental/Earth sensing, Air pollution monitoring, Forest fire detection, Landslide detection, Water quality monitoring, Natural disaster prevention, Industrial monitoring, Machine health monitoring, Data logging, Water/Waste water monitoring, Structural Health Monitoring.

1.2 ARCHITECTURE OF WIRELESS SENSOR NETWORK.

Wireless sensor network nodes are deployed over a large geographic area for sensing the data. Figure 1.2 shows a WSN having of sensor nodes and a sink. Wireless sensor network produces a large amount of data or information that is needed to be routed. These information passes through multiple hops to reach the sink.

Figure 1.2 Sensor Network Architecture
The architecture of node in wireless network has four components:
Sensing subsystem: It consisting of many sensors for acquiring data.

Processing subsystem: It consists of memory and a microcontroller for processing of data.

Radio subsystem: it is used for communication.

A power source.

Sensor node may include additional components, depending upon some specific applications:
To determine a position, a location finding system is used.

A mobilizer to change their location and configuration.

1.3 ENERGY HARVESTING WIRELESS SENSOR NETWORK.

Energy harvesting sensor (EHS) nodes provide an attractive and green solution to the problem of limited battery life of wireless sensor networks (WSNs). Unlike a conventional node
that uses a non-rechargeable battery and dies once it runs out of energy, an EHS node can harvest energy from the environment like solar power, thermal energy, wind energy, salinity gradients, and kinetic energy and replenish its rechargeable battery.

1.4 Components of a ENERGY HARVESTING WIRELESS SENSOR Node.

Each EH-WSN node uses one or more energy harvesting devices to harvest
environmental energy. The EH-WSN is composed by different components as
it is shown in Figure 1.4. As we know, the energy needed for sensors should be electrical. So the first is to convert environmental energy to electrical one. Inside the node there are different components for sensing the area, collecting data and processing the data. So the part in this kind of nodes which is different when compared to WSN nodes, is the power part. Once the stored energy reach to a certain amount, the power supply for micro controller and transceiver will start to work. They will continue to work until they have energy, as soon as the energy finish, they stop and energy storage device start to save energy again.

Figure 1.4: Components of EH-WSN node 2
The other fact should be consider about EH-WSN node is that the available energy will be different in different nodes. So every node has its own harvesting rate, some nodes may have high energy and other may have low energy. Hence routing should be done in an energy efficient way. This issue rises the need for designing optimized energy routing algorithms
2.LITERATURE SURVEY.

2.1 WIRELESS SENSOR NETWORKS.

The flexibility, high sensing fidelity, low-cost and rapid deployment characteristics of sensor networks create many new and exciting application areas for remote sensing1. Applications include, but are not limited to, environmental monitoring, industrial machine monitoring, surveillance systems, and military target tracking. Each application differs in features and requirements. To support this diversity of applications, the development of new communication protocols, algorithms, designs, and services are needed 3. The different issues in wireless sensor networks are,
Energy efficiency criterion there is a need to discover different routing techniques to eliminate energy inefficiencies that may shorten the lifetime of the network.

Routing Protocols should incorporate multi-path design technique.

Fault tolerance is another desirable property for routing protocols. Routing protocols should be able to find a new path at the network layer even if some nodes fail or get blocked due to some environmental interference.

To maximize energy savings there is need to provide a flexible platform for performing routing and data management.

The routing protocol should exploit such redundancy to improve energy and bandwidth utilization.

Routing Protocols should take care of heterogeneous nature of the nodes i.e. each node will be different in terms of computation, communication and power 4.

To overcome the issues different routing algorithms have been designed.

2.2 ROUTING IN WIRELESS SENSOR NETWORKS.

Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network which work under the limited resources available for sensor nodes. LEACH5 is one of the most popular hierarchical routing protocol for wireless sensor networks. It is completely distributed and it does not require global knowledge of network. LEACH uses single-hop routing where each node can transmit directly to the cluster-head and the sink. Therefore, it is not applicable in networks which are deployed in large regions. Furthermore, the idea of dynamic clustering brings extra overhead, e.g. cluster head changes, advertisements etc., which may cause the increase in energy consumption.

PEGASIS 6 outperforms LEACH by eliminating the overhead of dynamic cluster formation, minimizing the distance, non leader-nodes must transmit, limiting the number of transmissions and receival among all nodes, and using only one transmission to the BS per round. Nodes take turns to transmit the fused data to the BS to balance the energy depletion in the network and preserves robustness of the sensor web as nodes die at random locations. PEGASIS use multi-hop routing by forming chains and selection only one node to transmit to the base station instead of using multiple nodes. However, PEGASIS introduces excessive delay for distant node on the chain. As well as bottleneck can occur in presence of only single leader.

H-HEED 7 has significant improvement in the life time in comparison with HEED protocol. H-HEED sense more effective data packets to the base station. Factors like noise, Physical obstacles and collision may affect the received power are ignored. When compared to multi-level H-HEED protocols the performs level is low in other level of HEED i.e., H-HEED and HEED protocol.

There are other routing distributed algorithms like DEBR AND ESCFR 8,9 which balance the energy consumption and extend the network lifetime. Although, the DEBR balances the Energy consumption very well, but a node may select a next hop node in opposite direction of the BS and the selected next hop node can do the same. Thus, it can unnecessarily increase the delay in transmitting the data to the BS. Another disadvantage of DEBR is that for balancing energy consumption it may select a node which has no next-hop node within its communication range and hence data packets cannot be reached to the BS.
A self-clustering method for heterogeneous network using Genetic Algorithm that optimizes the network life is proposed in 10. An energy efficient region-based hybrid routing protocol under distance, energy and load parameter have been proposed in 11, which minimizes the energy consumption of sensors in every round and further increases the throughput and lifetime of network. An overview of the design objectives of Wireless Sensor Networks is given in 12. And some of the design objectives are identified and compared with Routing protocols. Frequent modification on node placement requires more energy consumption. It is identified that the efficient energy utilization and optimal path can be achieved by hierarchical routing algorithms while comparing with other routing algorithms.

A new maximum lifetime routing algorithm based on uneven clustering, linear programming and fuzzy set theory is proposed in 13. The objective of these routing protocols is either minimizing the energy consumption or maximizing the network lifetime.

2.3 ENERGY HARVESTING WIRELESS SENSOR NETWORKS(EH-WSNs).

A new observation related to energy aware routing is the availability of the so-called energy scavengers which are devices able to harvest energy from ambient sources such as solar, wind, water etc 14. Harvesting energy for low-power devices like wireless sensors presents a new challenge as the energy harvesting device has to be comparable in size (i.e. small enough) with the sensors. There are complex tradeoffs to be considered when designing energy harvesting
circuits for WSNs arising from the interaction of various factors like the characteristics of the energy sources, energy storage device(s) used, power management functionality of the nodes and protocols, and the applications requirements. The change of power supply calls for a different version of network protocol for routing.

2.4 ROUTING IN ENERGY HARVESTING WIRELESS SENSOR NETWORKS.

Energy replenishment in EH-WSN is uneven so data should be routed in efficient way which require efficient routing algorithms. Algorithms proposed in 15232 are extension of LEACH5 which extend LEACH and make it suitable for EH-WSNs. Solar-aware Wireless Sensor Network are proposed in1516. 15 utilizes solar power in wireless sensor networks and extend LEACH a well-known cluster-based protocol for sensor networks to become solar-aware. Voigt, et al. 16 designed two solar-aware routing protocols that preferably route packets via solar powered nodes and showed that the routing protocols provide significant energy savings.

A distributed framework for the sensor network to adaptively learn its energy environment was presented in 17. An example study of routing is showed that the proposed framework is able to utilize the extra knowledge about the environment to increase system lifetime. The optimal paths calculated in 1617 is based on each node having global knowledge of the whole network, which is usually inapplicable in WSNs. In 18, two geographic routing protocols are proposed in the presence of lossy wireless channel and energy renewable nodes. A forwarding node is selected based on its location, residual energy, and potential energy harvesting rate. A model to characterize the performance of multihop radio networks in the presence of energy constraints and design routing algorithms to optimally utilize the available energy is developed in19. The proposed algorithm is shown to achieve a competitive ratio that is asymptotically optimal with respect to the number of nodes in the network. A new threshold-based scheme is proposed to reduce the routing overhead while incurring only minimum performance degradation.

In 20 Battery estimation is presented to determine the current amount of charge units present in the battery , which can be used to determine optimized paths and control to maximize network lifetime. Three state-of-the-art routing algorithms for energy harvesting wireless sensor network are analyzed and compared in 21. Routing protocol taking into account three factors, transmission quality, energy consumption for data transfer and wasted energy is proposed in22 . When different weights are assigned to the three factors and a new route will be selected.
Energy Potential Function which is utilized to measure the node’s capability of energy harvesting is proposed23 , it extends LEACH to Energy Potential LEACH which is suitable for energy harvesting WSNs. Adaptive Energy-Harvesting Aware Clustering (AEHAC) routing protocol2 for perpetual-operated EH-WSNs. The protocol elects cluster head distributed and combines with energy harvesting rate and the node residual energy making it more efficient for networks which are completely powered by EH-WS nodes.
While EHS nodes are attracting considerable interest, several new challenges need to be overcome before they can be widely deployed. First, the energy harvesting process can be sporadic. Second, an EHS node needs additional circuitry to harvest, store, and to provide a regulated supply of the harnessed energy to its battery or supercapacitor 25. Hence, EH nodes are likely to be more expensive than conventional nodes, which come equipped with pre-charged, non-rechargeable batteries. Given the above challenges, hybrid WSNs, which comprise of a mixture of EHS nodes and conventional nodes, are likely. Up gradation of the legacy WSNs, in which conventional nodes are gradually replaced by EHS nodes, also naturally leads to hybrid WSNs. However, relatively less research has been done on hybrid WSNs. In the hybrid WSN considered in 26, the EH functionality is used only to relay information from a cluster head to the sink. A performance metric called k-outrage duration is introduced in 27 to compare hybrid and conventional WSNs. Analysis show that as the number of EHS nodes increases, the k-outrage duration increases more rapidly, while as the number of conventional nodes increases the k-outage duration begins to saturate.

Although recent developments in energy harvesting can be employed to replenish the power supply of sensor networks, energy management is still an important issue since the replenishing rates are typically small and the stochastic energy renewal process only provides a partial solution to the aggressive power demands of a large-scale network. Hence there is a need to develop routing algorithms which are energy efficient.

3. PROBLEM STATEMENT AND MOTIVATION.

EHS are sporadic which makes energy management still a major issue. Therefore, reducing energy consumption of sensor nodes is very important. In order to be energy efficient, the algorithm is supposed to route the sensed data via other sensor nodes which are closer to the base station or on some energy efficient path. But this arrangement of data routing can lead the sensor nodes of the energy efficient path die quickly for frequently used as relay node. This uneven energy consumption dramatically reduces the network lifetime and decreases the coverage ratio.
To solve above problem there is need to route a network in such a way that the network remains balanced. In the proposed system we consider a hybrid network consisting both conventional WSN nodes and EH-WSN nodes. Sensor nodes select the next-hop relay nodes in such a way that it requires less energy and also takes care of balancing the energy consumption. The next hop relay node is selected by considering the cost function, which consists of three parameters residual energy, energy density and distance to receiver. A sensor node sj chooses its next hop relay node as sk by considering the cost function as in the following equation,
Cost(s j, sk) = EDensity(sk) × EResidual(sk)
dis(s j, sk)
Sensor node sj selects the node with maximum cost function as the next hop relay node. The above cost function efficiently addresses both the issues of energy balancing as well as energy consumption of the network. The first and second parameters (EDensity(sk) and EResidual(sk)) of the cost function, take care of the energy balancing of the network. The third parameter (dis(s j, sk)) tries to reduce the energy consumption of the network. Thereby, it helps the routing algorithm to be an efficient one.

For the EH-WSN nodes we assume that, once node energy falls below Ethreshold it replenishes itself immediately. But if a conventional WSN node energy falls below threshold value then network will be rerouted . EH-WSN nodes save the energy required in rerouting the network. This solution helps in efficient use of energy and also keeps the network balanced and hence helps in increasing the network lifetime.

4. WORK CARRIED OUT SO FAR.

4.1 SYSTEM MODEL.

We assume a hybrid model which consists of conventional WSN nodes and EH-WSN nodes where all the sensor nodes are deployed randomly and once they are deployed, they become stationary. All sensor nodes have same initial energy. Similar to 5, the data gathering operation is divided into rounds. In each round, all sensor nodes collect the local data and send it to the base station via other sensor nodes as a next hop relay node. First order radio model for energy is used as in 29 which considers the energy consumption in transmitting and receiving the data.

4.2 PROPOSED SYSTEM.

Routing is done by the sensor nodes by selecting the next-hop relay nodes in such a way that it requires less energy and also takes care of balancing the energy consumption. A sensor node may have many possible next-hop relay nodes within its communication range. However, in our approach only those neighbour sensor nodes are eligible to serve as a relay node which has lesser hop count. Now, a sensor node s j chooses the next hop relay node sk based on the cost function. We denote the cost function as Cost (sj,sk) which indicates the cost of selecting the sk as next hop relay node by the sj. To define the cost function, we consider the following parameters as shown in figure 4.1.

Energy density: The general principle behind energy balancing is that the data packets should move forward to the base station through the region of the network where sensor nodes have higher remaining energy. In order to achieve this objective we calculate energy density of a sensor node and the data packets are force to flow through the higher density region. Energy density is calculated as follows,
EDensity(sk)={EResidual(si), ?si ? Neighbor(sk) ? HC(si) ; HC(sk)} + EResidual(sk)

HC(si) is the hop count of si to the sink. The higher the EDensity(sk), the higher is the chance of getting selected as a relay node. Therefore, the cost function is proportional to the EDensity(sk).

Cost(s j, sk) ? EDensity(sk) ——-(1)
Residual energy: Although sk may have sufficient energy density due to comparably higher residual energy of its next-hop neighbors, it may have lesser remaining energy to serve as a relay node. In order to balance the energy consumption of the network, sk should have also sufficient residual energy. Higher the residual energy, the higher is the chance of getting selected as a relay node. Therefore, the cost function is proportional to the EResidual(sk). In other words,
Cost(s j, sk) ? EResidual(sk) ——- (2)
Distance to receiver: As s j consumes its energy due to long-haul transmission with sk, the distance between s j and sk should be lesser to reduce energy consumption of s j. The lesser the transmission distance, the higher is the chance of getting selected as a relay node. Therefore, the cost function is inversely proportional to the dis(s j, sk). In other words,
Cost(s j, sk) ? 1 ——- (3)
dis(s j, sk).

Combining equations (1),(2) and (3), we obtain cost function
Cost(s j, sk) ? EDensity(sk) × EResidual(sk)
dis(s j, sk)
s j selects the sk having maximum cost value as relay node.

The sensor nodes which are in communication range of the sink (base station) send the data directly to the base station and don’t take part in the relay node selection algorithm. 4.2 shows the proposed algorithm.

Distance to receiver.

Residual Energy.

Energy Density.

Next Hop Relay Node Selection.

Figure 4.1:Next hop relay node selection.

1. Start
2.if(dis(sj, sink) ? dmax) then // here dmax is the maximum range of the node
3.Relay=sink
else
4.Nj= Neighbour(sj)
5.max=-1.0 // max is the maximum cost
6.while(Nj ? Ø) do
7. Select a neighbour sk from Nj.

8. if( cost(sj, sk) > max)
9.relay=sk
10.max=cost(sj, sk)
11.end if
12.end while
13.end if
14.Stop

4.2 Algorithm for next hop relay node selection.

When residual energy of a sensor node falls below threshold value Ethreshold the node is checked weather it is an energy harvesting node or a normal wireless sensor node. If the low energy node is conventional WSN node then network will be rerouted or if the low energy node is EH-WSN then it is replenished to Einitial( i.e. to initial energy). EH-WSN nodes save the energy required in rerouting the network. 4.3 represents the algorithm for replenishing.

1.Start
2.if (replenish==1) then
3. if(NODE_ENERGYk< Ethreshold)then
4. NODE_ENERGYk = Einitial
else
5.if (replenish==0) then
6.if(NODE_ENERGYk< Ethreshold)then
7. ROUTE_DISCOVERY
8.end if
9.Stop

4.3 Algorithm for replenishment.

5.SIMULATION RESULTS AND DISSCUSSIONS.

The performance of proposed algorithm is compared with existing routing algorithm EEBR28 by evaluating with different parameters. The table below shows the simulation parameters.

Parameters Values
No. of Nodes 50
Einiti (initial energy of each node) 2000J
TX_ENERGY(energy required for transmitting a packet) 2J
RX_ENERGY(energy required for receiving a packet 2J
Dmax (communication range) 100m
Packet size 512bytes
Table 5.1 Simulation parameters

A round can be considered as total time to form a route, followed by data sensing and transmitting the data to the base station.

Figure 5.1 illustrate the lowest energy node in the network after several rounds. The x axis comparison graph indicates the number of rounds and the y axis indicates the lowest energy node in the network. Lowest energy node of PROPOSED SYSTEM has higher energy than the lowest energy node of EEBR even after 200 rounds as PROPOSED SYSTEM transmits data through energy efficient path. EEBR the node energy falls very quickly as it don’t have EHS nodes and no recharging of energy takes place the battery energy is the only source of energy.

Figure 5.1 Lowest energy node Vs Number of rounds.

Figure.5.2 Energy balancing Vs Number of rounds.

Figure 5.2 illustrate the nodes available for energy balancing after several rounds. The x axis comparison graph indicates the number of rounds and the y axis indicates the no. of nodes available for energy balancing. PROPOSED SYSTEM makes higher number of nodes available for energy balancing since it considers energy density and residual energy of nodes while selecting the route.

Figure 5.3 Residual energy Vs Number of rounds.

Figure 5.3 illustrate the residual energy of the network after several rounds. The x axis comparison graph indicates the number of rounds and the y axis indicates the residual energy in the network. Here lifetime parameter residual energy of the network in considered. PROPOSED SYSTEM has higher residual energy in the network even after 180 rounds. The residual energy of the network in PROPOSED SYSTEM doesn’t reduce quickly as it consists of EHS nodes in it and data is transmitted through energy efficient and energy balanced path. In EEBR the residual energy of the network falls very quickly after 36 rounds as it doesn’t have EHS nodes in it and the battery energy is the only source. EEBR has 1941J of energy after 36rounds and PROPOSED algorithm has same energy after 180rounds which shows that the PROPOSED SYSTEM increases the lifetime of the network.

From the above comparisons it is proved that PROPOSED SYSTEM works more efficiently than existing routing algorithm EEBR28 as it is a hybrid network which consists of EHS nodes and conventional WSN nodes and transmits data through energy efficient and balanced path. And hence increase the performance and the network lifetime.

6.FUTURE WORK.

The proposed algorithm considers that when a EH-WSN node’s energy falls below threshold it recharges itself immediately. But energy harvesting nodes are sporadic and the harvesting rate varies from node to node which should be considered. In future work sensor capabilities of harvesting energy from the environment and their capacities of storing energy will be taken into consideration and a routing algorithm will be designed . The algorithm could also be extended towards efficient utilization of sporadic and opportunistic energy availability in a performance aware manner.

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