2017 2nd International Conference for Convergence in Technology (I2CT)
Implementation of Energy Efficient Algorithm in Delay Tolerant Networks Nimish Ukey
Lalit Kulkarni
Department of Information Technology Maharashtra Institute of Technology Pune, India
[email protected]
Department of Information Technology Maharashtra Institute of Technology Pune, India
[email protected] of DTN. Hence, in the year of 2003, the Delay Tolerant concept was proposed. Later on, Internet Research Task Force (IRTF) formed a DTN research group (DTNRG) based on the Interplanetary Network Research (IPNRG). As a result in 2007, they proposed the Bundle Protocol (BP) and DTN architecture. DTN is a kind of network which supports a high level of delay. DTN has various applications such as underwater networks [4], Vehicular Ad hoc NETworks (VANETs) [5], military networks [6], and inter-planetary networks [7]. The two-hop routing algorithm is proposed by the Groenevelt and Nain [8], where the source is able to replicate the message and other nodes is only able to forward the message. The energy efficiency grabs the attention of various researchers, recently [9]. In paper [10] and [11], the continuous and discrete time Markov model analyzes the energy efficient optimal forwarding problem. The selection and prioritization of the message forwarding list problem is addressed by the Mao et al. [12] to reduce the total energy cost of data which is forwarded to the sink node in wireless sensor networks (WSN).
Abstract— Delay Tolerant Networks is an emerging technique which supports for the intermittent connectivity. Previously, for transmission of bundles the end-to-end connection was required to carry out the transmission successfully. But now, using the store-carry-forward mechanism and the node mobility it is possible to transmit the data from the source to the destination even if there is no end to end connection between them. There are different routing strategies which are formed in a DTN environment to reduce the message overhead and for increasing the message delivery probability, but very few take the energy into a consideration while designing. DTN nodes have a very limited amount of energy resources which holds a great significance. To perform any kind of operation between various nodes, i.e. sending bundle, searching for the neighbor node, storing bundle, etc the energy is used. Hence, in this paper, we proposed a new energy efficient algorithm to improve the energy efficiency. This algorithm forwards the packet to the nearest neighbor node which also uses the probability distribution function to forward the bundle to the next best hop which requires the least energy for the transmission. It also narrows down the specific region for the transmission of data by using the angle based transmission. The freshly energized node is redeployed in the network. With the help of Opportunistic Network Simulator results, it is proven that the designed algorithm is more energy efficient than the previous distance based algorithm. Keywords—Delay Tolerant Network, energy efficient routing protocols, efficiency, store and forward.
I.
INTRODUCTION
The Transmission Control Protocol/Internet Protocol (TCP/IP) is considered as the end-to-end, bidirectional with the symmetric data rate network. However, the new emerging networks consist of the intermittent connectivity or opportunistic networks, which are not considered as end-to-end networks, comes under the Delay Tolerant Networks (DTNs). Transmitting data is very easy with the end-to-end connected network, but problems occur when the network is not fully connected or it is wireless. The delay and network interruption are the common problems nowadays because of the mobility of nodes, change in network topology or harsh environment. And most of the routing protocols are designed by considering the fully connected network. Hence, many researchers propose various solutions regarding it. However, all the proposed work was not feasible, which results into the concept
978-1-5090-4307-1/17/$31.00 ©2017 IEEE
Fig. 1. Illustration of DTN layered architecture
Store-carry-forward mechanism is used in the bundle layer that is introduced in Delay Tolerant Network. Bundle layer is situated below the application layer. In DTN new communication paradigm is being used, i.e. hop by hop (asynchronous message delivery) instead of using end-to-end
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paradigm, which is synchronous. The data unit, which is combined into one or more variable length data units, is called a bundle. The bundle is sent to the next hop, which in turn saves the time. In end-to-end connection if round trip time is more, then the time required by multiple round trips for sending the entire information will be eventually more.
In this given paper Section II illustrates the related work. Section III tells you about the proposed algorithm strategies to reduce the energy consumption. Section IV tells you about the proposed system. Section V will brief you about the experimental setup, i.e. scenario setting, the requirement of experiment setup, evaluation parameters, and evaluation metrics. And Section VI includes the result and Section VII includes the final part, i.e. conclusion and future work.
The traditional routing protocols were developed for endto-end connections hence they will not work here. Therefore, the store-carry-forward technique has been used in DTNs. DTN overcomes the problem of high or variable delay, intermittent connectivity, and high error rates by using the store-carry-forward mechanism. Each and every node of the network of DTN has the storage for storing the data. The data are stored and then carried forward from one node to another when both the nodes meet until all packets are sent to destination node from the source node, as shown in Fig. 2. The source is denoted as S. M is the Mobile node and D denotes the destination in the below Fig. 2.
II.
RELATED WORK
In [14], node forwards the packet when and only when it has n neighbor nodes in its maximum transmission range to reduce the energy consumption. But it is difficult to decide the value of n. In [15], F. D. Rango, S. Amelio, and P. Fazio implement the protocol, which was an improvement over the [14]. Previously the value of n has to be set statically however in [15] the value has been chosen dynamically. It chooses a value based on the current neighbor node and the energy level. In [16], the trade-off has been achieved between the forwarding efficiency and the energy preservation. Energy plays very important role in any kind of network. Most of routing protocols and strategies are designed by considering the well-connected network. But because of the mobility of nodes these routing protocols do not work effectively. For such kinds of intermittent connectivity, Delay Tolerant Network is used. Amongst all the mobile nodes, many have a very limited amount of energy and to perform any kind of operation energy is required. So, it is necessary to reduce the consumption of energy. The effective and efficient use of energy increases the lifetime of node and the network. The DTN nodes commonly operate on low power battery resource; hence, there is a need of improving the energy efficiency by using appropriate technique to increase the lifetime of the node and also to increase the probability of delivering the bundle. Most of the routing protocols comes under below these three categories: A. Message-ferry-based In the Message-ferry-based method, some extra mobile nodes are used as ferries for delivering the message. The route of these ferries is controlled with store-carry-forward mechanism to increase the probability of delivering the message.
Fig. 2. Store-carry-forward mechanism in DTN
The consumption of energy is a major factor that is responsible for the performance of the network. Nowadays, people use smart phones, which are used for the communication purpose (e.g. sending the message and receiving the message with the help of Wi-Fi, Internet or Bluetooth). These smart phones consist of the lithium-ion batteries that is capable of storing a very limited amount of energy. So, various attempts have been done to increase the energy and battery life. In many cases, the hardware resources are very limited in nature. This needs to be taken into consideration while transmitting the bundle. And whether the bundle needs to be transmitted or not, it has to be decided beforehand or if bundle is to be transmitted then predict when to transmit the bundle. So, the energy will be conserved. Hence, this paper will tell you about the implementation of algorithm by considering certain parameters to increase the energy efficiency.
B. Opportunity-based In the Opportunity-based method, messages are transferred when and only when both nodes come in the contact or meet at the same place and the messages are flooded to increase the probability of delivery. In Opportunistic based method, messages are sent randomly hop by hop in the hope that it will reach the destination, but without any guarantee. C. Prediction-based The prediction-based method is based on the observation history and the estimation has been made for the relay selection for successful delivery. It estimates the probability of the delivery and delay for the delivery.
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III.
OPPORTUNISTIC NETWORK ENVIRONMENT 3.
The opportunistic networking Environment (ONE) is specifically designed for evaluating DTN routing and application protocol. It offers the framework for implementing routing protocol and it also allows creating a scenario based on different movement model. Depending on the distance between source and destination, the mobility of nodes and how dense the mobile nodes are, the performance of an opportunistic network depends on. Simulation is very useful for analyzing the behavior of DTN routing.
4.
There are many parameters which are considered for evaluating the performance. Some of them are as follows: Time to live (TTL), message delivery probability, latency and energy consumption. A message has a finite TTL & it gets discarded after the TTL ends,
Calculate the forwarding equivalent energyefficiency distance (FEEDs i.e. to define the relationship between the two nodes and delay for equivalence energy efficiency) and P (The probability that the distance to the source node is smaller by at least one relay). If P is smaller than the threshold value, then it forwards the copy of the packet to the nearest relay node. Else there is no need to forward the packet copy.
It is based on probability distribution function (p.d.f.) of forwarding energy efficiency. It can predict how much better energy efficiency is achieved with more accurate prediction and hence the better forwarding decision can be taken by analysing the p.d.f. To derive the p.d.f. of the forwarding energy efficiency, it is necessary to derive the p.d.f. of the distance between two nodes. For one-dimentional region, the p.d.f. of the distance is: Here, L is the region, and d is the distance between nodes. In two-dimensional region for deriving the p.d.f. of distance between two nodes (x1,y1) and (x2,y2), use and As x and y are independent, the joint probability density function of x and y is:
Fig. 3. Overview of the ONE simulation environment
IV.
PROPOSED ALGORITHM
The source can establish communication when and only when it comes into its maximum transmission range. Source calculates the forwarding energy efficiency ηf (tk) for the nearest relay node. The source predicts the p.d.f. in future. Source determines the forwarding of the packet copy to relay. Ψ=η+α.σ Ψ is threshold of η (tk), η is mean and α is used for the forwarding decision. Tuning α is a tradeoff between energy efficiency.
As energy-efficiency is a major thing to achieve, it is not necessary to make multiple copies of a message for sending it to the destination. The most important thing that needs to be considered while designing an energy efficient algorithm is delay-energy tradeoff and Mobility of nodes. In Distance-based energy-efficient opportunistic forwarding algorithm [1], if there is any relay node existed within a maximum transmission range of the source node and its value is less than the threshold value, then the source node forwards the packet to that nearest node without copying the data. This algorithm is performed at the source at every contact time. Algorithm : Distance-based opportunistic forwarding 1. 2.
x
Angle based transmission range selection
Node (vehicle) moves only in one direction. Hence, there is no need to consider the node that is placed 3600. The selection of the specific region for the transmission of data reduces the overhead of transmission. The range of this mechanism [2] is increased by 300 to 1800 to find the optimal range of nodes. Firstly, source node finds its location along with the information about the destination and neighbors. Initially, the angle of 300 is initialized and can be increased up to 1800. If within the transmission range the node is not found, then the transmission angle is increased by 150 on both the sides considering the source and destination node vector.
energy-efficient
Set the threshold frequency Check if there is any relay node within its maximum transmission range. If yes, then the distance of relay node is calculated without copying the packet. It measures the distance of the relay without packet copy.
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Fig. 5. Working scenario architecture [3] Fig. 4. Angular Transmission Range Selection
V.
VI.
EXPERIMENT SETUP
Opportunistic Network Environment (ONE) simulator is used for the simulation purpose. ONE is java based simulation environment which includes mobility modelling, routing, application support, interfaces, reporting and visualization, and simulation scenario.
PROPOSED SYSTEM
When a source wants to send the bundle to the destination, then source node find the nodes which are nearer to the source node. The source node calculates the distance amongst all the nodes which come under its maximum coverage area and the node which is most nearer to the source is selected for the further communication. Hence, the source node chooses the node which is closer to a source. This whole mechanism comes under the Distance-based Energy-Efficient Opportunistic Forwarding (DEEOF) algorithm [13].
A. Simulation Parameters In the simulation, the performance is checked by considering the specified amount of nodes, i.e. 100 in the initial stage. Later on the nodes are increased by 20 each time until the nodes reach to 200. Then, on each stage the performance is evaluated. All others are common group setting for the simulation environment and for the energy setting.
If node ‘i’ is considered as mobile node, then it may be possible that initially, node will not be in the range of source node ( i.e. only one node in source network range). As node i is mobile node it may come in the range of source node in the future. This may be more nearer to the source node rather than the current node. The contact where the node will come into the range of source is called as the Contact Opportunity. If the mobile node comes into the network area before the specified threshold time then the source node will wait for the node i to come into the network range. This is based on the probability distribution function which is used to predict how efficiently the energy efficiency is being achieved. In the proposed algorithm the energy of the node has been monitored and if this energy has been reduced, then it gets recharged with a certain amount of energy that is provided in the setting to bring that node into the network. If some nodes has less energy than the energy required for the transmission of the bundle then that node is not considered for further transmission purpose. So to allow that node for further participation in the network, the energy of that node and other nodes need to be updated in a frequent manner.
Fig. 6. Playfield of ONE Simulator with underlay image
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TABLE I.
ONE SIMULATION PARAMETERS
Parameters
C. Metrics The basic thing that needs to be focused is energy consumption of nodes. It is necessary to keep the track of energy consumption of nodes for monitoring the network performance.
Value
Simulation Area Number of Nodes
6000 m * 6000 m 100, 120, 140, 160, 180,
VII. RESULTS
200 MAC Mobility Model
802.11 Random Wave point
Simulation Time
21200 Seconds
Transmission range Buffer size Bundle size
100 meter 5 Mb 500 KB – 1 Mb
TABLE II.
Perform the Simulation for 21200s for DEEOF Routing Algorithm, Energy Aware Routing, and also for the Proposed Algorithm and the analysis for Packet delivery ratio with constraint energy (E=1500) has been done.
ENERGY MODULE SETTINGS
Parameters
Value
Initial Energy Scan Energy Transmit Energy Receive Energy Recharge Energy
4800 0.92 0.08 0.08 3000
Fig. 7. Performance measurement with constraint energy (E=1500)
B. Evaluation Parameters The parameters that are considered for comparative study are Simulation Time, Packet Delivery Ratio, and Average Energy Consumption. Proposed Routing Algorithm strategy is tested on different Routing strategies for Packet delivery ratio and Energy consumption. These parameters are used for performance evaluation.
Perform the Simulation for 21200s for DEEOF Routing Algorithm, Energy Aware Routing, and also for the Proposed Algorithm and do the analysis for Packet delivery ratio with energy (E=3000)
1) Simulation Time Time for which the scenario is run 2) Packet Delivery Ratio (PDR) The ratio of packets that are successfully delivered to a destination compared to the number of packets that have been sent out by the sender.
Fig. 8. Performance measurement with energy (E=3000)
3) Average Energy Consumption (AEC)
Table III shows the obtained values of Packet Delivery ratio for DEEOF Algorithm, Energy Aware Routing, and also for the Proposed Algorithm when simulated from 5000s – 20000s with unconstrained energy (E=4800) for 200 DTN nodes and according the generated results the graph has been designed.
The ratio of total energy consumed by all the nodes to the total number of nodes.
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TABLE III.
PACKET DELIVERY RATIO FOR DIFFERENT ROUTING STRATEGIES
Simulation
Packet Delivery Ratio (PDR)
Time (sec) DEEOF Algorithm
Energy Aware Routing
Proposed Algorithm
5000
0.3630952
0.3630952
0.3630952
10000
0.3727273
0.3693694
0.4894895
15000
0.266
0.2435644
0.5881188
20000
0.1757143
0.1819527
0.6044444
Fig. 11. Performance measurement with unconstraint energy (E=4800)
Table IV shows the obtained values of Packet Delivery Ratio for different Routing strategies when simulated from 5000s – 20000s with unconstraint energy (E=4800) for 200 DTN nodes. VIII. CONCLUSION This paper shows the way to improve the energy efficiency with increasing network performance. Which is evaluated based on the given set of nodes, simulation time, packet delivery ratio, and average energy consumption. If the set of nodes, the size of a message, the interval of message generation, and the nodes mobility increases or varies then it will affect the performance of network and routing protocols. So, the performance is evaluated in a controlled scenario. The proposed algorithm aims to maximize the energy efficiency which is based on the already existed distance based algorithm, by updating the node energy level to energize the node and redeployment of it in the network and the transmission of data by using the angle based transmission. The result proved that proposed energy efficient algorithm improves the network performance and reduces the energy consumption in DTN.
Fig. 9. Performance measurement with unconstrained energy (E=4800)
Perform the Simulation for 21200s for DEEOF Routing Algorithm, Energy Aware Routing, and also for the Proposed Algorithm and do the analysis for Average Energy Consumption in percentage with constraint energy (E=1500).
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[4] Fig. 10. Performance measurement with constrained energy (E=1500) [5]
Perform the Simulation for 21200s for DEEOF Routing Algorithm, Energy Aware Routing, and also for the Proposed Algorithm and do the analysis for Average Energy Consumption in percentage with constraint energy (E=1500).
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