Power Optimization of Wireless Network. 1 Rakesh Kumar Jha, 2Varun Mishra, 3Kuldeep Yadav, 4Shubham Manhas. 1,2,3,4. Katra, J&K 182320. Shri Mata ...
2013 Annual IEEE India Conference (INDICON)
Power Optimization of Wireless Network 1
Rakesh Kumar Jha, 2Varun Mishra, 3Kuldeep Yadav, 4Shubham Manhas 1,2,3,4
Shri Mata Vaishno Devi University Katra, J&K 182320 www.smvdu.net.in
Abstract— During the last few years there has been a significant growth in the field of wireless communication. Power optimization is an important task for NGN (Next Generation Network), because near about 7-8 % weightage of ICT (Information and Communication Technology) power are being used in mobile communication. In this paper tools and techniques are provided for optimizing total power consumption of a Wireless Network due to repeated data processing and inefficient transmission to a fixed end-to-end source. In this work it is shown that the improved transmission strategy could enhance and optimize the total power utilization considerably. Power optimization in wireless network has been analyzed on the basis of three case study which are: 1) Relay based Approach, 2) Battery Model, and 3) Routing Protocol. The proposed network is compared among various scenarios and on the basis of performance analysis, the proposed model provides a best solution for NGN with AODV routing protocol as compared with OLSR as far as power optimization is concerned. Keywords-- Power Optimization, Wireless Networking, Next Generation Network, Adaptive Approach, Wireless Relay Network.
I. INTRODUCTION In the face of increasing world-wide energy demand governments and research communities have acknowledged the need to save energy in every possible manner. Within the communications component of ICT, recent studies have shown that the largest elements of power consumption are access networks (wired and wireless) rather than core networks. Reducing the total power consumption of wireless networks is a well-recognized way to improve the energy efficiency and thus to contribute to the reduction of worldwide energy consumption [1]. Given the need to reduce the energy consumption, power can be optimized: • Using Peek-to-Average Power Reduction in OFDM system. • Cross-layer based approach. • On-demand wake up scheme. • Relay base network. The core challenge is how to reduce the overall power consumption of the NGN maintaining its Quality of Service and coverage area. Wireless relay networks can be an answer to this problem. Wireless relaying networks allow mobile terminals to participate in the transmission of information when they are neither the initial source final nor the destination. Initially the concept of relays came to increase the coverage area only, but it was seen that relaying can be
applied to cellular, WLAN, ad-hoc, and hybrid networks in order to increase: i. Coverage Area ii. Throughput of the network and iii. Capacity of the system [2]. As compared to a conventional wireless network wherein a mobile station (MS) directly communicates with a base station (in range), in a relay assisted network mobile stations are connected to a fixed relay station (RS) or to a base station depending upon the link strength (or distance). Moreover, relaying splits longer paths into shorter segments (by providing LOS communication), thus reducing the resulting total pathloss by exploiting the nonlinear relation of pathloss vs. distance. At system level (MAC), this potentially allows for a reduction of transmit powers, and, consequently, lower electromagnetic emission [3]. RS can be deployed at strategic locations having better link with the BS. This also helps in reducing dark spots and increasing the coverage area or periphery of the network cell. In rural areas with low population density instead of using a BS, RS can be used to reduce infrastructural cost and at the same time serve the small number of users. In particular motivation and problem statement of this work has been discussed in this section. This paper discusses the network model in section 2, section 3 gives a detailed mathematical analysis on relay number, relay positioning and battery model, section 4 lists the various simulation parameters, and section 5 presents the result of simulation and its analysis. Conclusion and future work are presented in section 6. II. NETWORK MODEL On investigating a WLAN system, in which all users simultaneously share the same radio resource. System assumptions are as follows. a) Node type: Three types of nodes are used namely Base Station (BS), Relay Station (RS, these are intermediate terminals that serve Mobile Stations while simultaneously performing their own communication with the BS) and Mobile Station (MS, user terminals). b) Routing: The pathloss is assumed to be two-ray model between all nodes. The routing scheme is AODV and OLSR. Different routing schemes are used for the purpose of comparison. c) Application Server: Three application servers namely VoIP (Voice over IP), FTP (File Transfer Protocol) and Media are assumed to cater the load generated by the network/users.
Fig. 1: Basic network model.
d) Radio Type: BS – MS and RS – MS communicate via IEEE 802.11b type radio and BS – RS are connected via a wireless Backhaul link. A. Basic Network Model: Current Scenario With all the systems in telecommunication area the basic system architecture being used is shown in Fig. 1. It comprises of mobile/sink nodes which generate application request and communicate via a common wireless link. These requests are forwarded to a fixed BS which again forwards these requests to the application service providing servers. These servers are linked together via an Ethernet link (IEEE 802.3) to a HUB which is connected to the base station through a BGP link. Servers are linked together for traffic load sharing. The major drawback of this kind of system is that if the power radiated from the base station is reduced (for power optimization) than the coverage area served by the BS shrinks and consecutively throughput decreases, since many mobile nodes won’t be in range. Also the link between MS and BS will be long and week and therefore will have higher pathloss. B. Proposed Network Model Fig. 2 shows the proposed network architecture. The wireless nodes in this model have a single transceiver and operate in half-duplex mode. Transmissions are assumed to be error free and omnidirectional. Proposed network architecture is relay assisted network where RS is deployed at strategic locations which have strong links between themselves and with BS (it is preferable to have Line of Sight). These RS communicate directly or in two-hops with BS via a backhaul link. The crux of having this kind of system is to reduce the pathloss component of the link. In the proposed network the mobile station doesn’t communicate directly with the BS (which is not in the range of BS) but the RS and RS in turn communicate with the BS. Since RS are communicating to MS, it helps in extending the coverage area of the BS.
III. MATHEMATICAL ANALYSIS This section provides the detailed study conducted for relay and its position in a zone of a network. Battery model is also discussed which gives a sub-optimum solution for calculating the total battery energy consumption of relay transmission and non-relay transmission. A comparison of the two will clearly show that the proposed relay network is optimizing the power consumption with respect to the current scenario. A. Power optimization using relays We consider a single-cell two-region model as shown in Fig. 3. The cell structure is approximated by a circle of radius d with BS at the center. No Relay Region is the region inside the circle of radius d 1 where d 1 < d. In No Relay Region MS directly communicates with the BS.
Fig. 3: Network cell site.
Relay Region is the region beyond the periphery of circle of radius d 1 . The MS in Relay Region communicates with BS in two hops via Fixed Relay Station (FRS). Such a scenario is most attractive from a practical perspective because system complexity is strongly related to the number of hops. This approach allows three different types of links: direct link refer to the connection between BS and MS, the relay link is between BS and RS, and access link between RS and MS. MS are stationary but randomly distributed over the cell. Our
Fig. 2: Proposed network model.
objective is to minimize the total transmit power used in a system with the help of relays while maintaining desired QoS which, in our case, is the minimum required power level at BS (P R-BS ) and RS (P R-RS ). We show analytically, in terms of relative transmission power, that placement of FRS has a significant effect on the energy minimization. Relative transmission power is defined as ratio of total power required for all the MS to communicate with BS in two hop scenario to total power required for all the MS to communicate with BS directly. Let, T be the number of MS in the cell of radius d. Number of MS in No Relay Region, M is given as: 𝑇𝑇𝑑𝑑 2
𝑀𝑀 = 21 𝑑𝑑 Number of MS in Relay Region, N is given as:
……1
𝑇𝑇(𝑑𝑑 2 −𝑑𝑑 2 )
1 ……2 𝑁𝑁 = 𝑑𝑑 2 Total power consumed by all the MS in the cell to communicate directly to the BS is given by: 2𝜋𝜋 𝑑𝑑 𝑇𝑇 ……3 𝑃𝑃𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 2 ∫0 ∫0 𝑟𝑟 (𝑛𝑛+1) 𝑃𝑃𝑅𝑅−𝐵𝐵𝐵𝐵 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝜋𝜋𝑑𝑑 Where n is the pathloss index. Power consumed by all the MS inside the inner region i.e. No Relay Region is given as: 2𝜋𝜋 𝑑𝑑 𝑀𝑀 ……4 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 2 ∫0 ∫0 1 𝑟𝑟 (𝑛𝑛+1) 𝑃𝑃𝑅𝑅−𝐵𝐵𝐵𝐵 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝑑𝑑
𝑃𝑃𝑅𝑅 = � 1 � 𝑑𝑑
(𝑛𝑛+2)
+
𝑃𝑃𝑅𝑅−𝑅𝑅𝑅𝑅
𝑃𝑃𝑅𝑅−𝐵𝐵𝐵𝐵
�
𝑑𝑑−𝑑𝑑 1 (𝑛𝑛+2) 𝑑𝑑
�
+
(𝑛𝑛+2) (𝑑𝑑 2 −𝑑𝑑 12 ) 𝑑𝑑 1 𝑛𝑛 2
𝑑𝑑 2
� � 𝑑𝑑
……8 This equation shows that the radiated power depends on pathloss factor n, and relay distance d 1 . Since the value of d 1 is smaller than d, this shows that the radiated power P R is reduced by a certain amount. The reduction factor depends on number of relay stations, location of relay stations and pathloss factor [4]. B. Mathematical Analysis For Battery Consumption Considering a network in Fig. 4 with channels modeled as the Rayleigh fading channel with Kth-power path loss having nodes S 1 as source, S 2 as destination and R as relay node. There are two links connecting source S 1 to destination S 2 namely: 1. Direct link S 1 -S 2 2. Relay link S 1 -R-S 2
𝜋𝜋𝑑𝑑 1
The power consumed for communication between the MS in outer region is given by sum of power consumed for communication from MS to FRS and power consumed for communication from FRS to BS. Power consumed by MS to communicate to FRS is: 2𝜋𝜋 𝑑𝑑 (𝑛𝑛+1) 𝑁𝑁 𝑃𝑃𝑅𝑅−𝑅𝑅𝑅𝑅 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ……5 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 −1 = 2 ∫0 ∫𝑑𝑑 𝑟𝑟 2 𝜋𝜋(𝑑𝑑 −𝑑𝑑 1 )
1
Power consumed by FRS to communicate with BS is: 𝑇𝑇(𝑑𝑑 2 −𝑑𝑑 2 )𝑃𝑃
𝑑𝑑 𝑛𝑛
1 𝑅𝑅−𝐵𝐵𝐵𝐵 1 ……6 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 −2 = 𝑑𝑑 2 Thus, power consumed by all the MS in the outer region (i.e. Relay Region) to communicate with BS is: ……7 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 = 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 −1 + 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 −2 Therefore, relative transmission power (total transmission power with relay/ total transmission power without relay) is given by:
Fig. 4: Link example in a relay network section.
Also consider the distance between direct link S 1 -S 2 as d, S 1 -R as d 1 =ϴ 1 *d and R-S 2 as d 2 =ϴ 2 *d. It is considered that the relay node can be anywhere in the 2-D plane, therefore ϴ 1 , ϴ 2 ≠ 0 (only for a special case i.e. straight line path). Also it can be said that ϴ 1 , ϴ 2 ˃ 0 and ϴ 1 + ϴ 2 ≥ 1. For comparing the battery energy comparison of these two links BER is considered as system performance indicator. The average BER for Direct as well as Relay link is taken to be P e . Let P 1 and P 2 be the individual average BER for link S 1 -R and R-S 2 respectively. Considering BPSK modulation the average BER is calculated as:
Pe = N0 /4ϵpr ……9 Also from [5], the relationship between the bit energy at the transmitter 𝛜𝛜 pt and the bit energy at the receiver 𝛜𝛜 pr is obtained as: ϵpt /ϵpr = Ml G1 dK ……10 Where M l is the link margin, G 1 is the gain factor at d= 1, and K≥ 2 is the path loss exponent. The realistic nonlinear model which describes the battery discharge process with battery efficiency factor𝜇𝜇(𝑖𝑖) = 1 − 𝜔𝜔𝜔𝜔, can be obtained from [5]. Also the total battery energy consumption formula from [6][5, Lemma 1]: 𝜔𝜔𝛾𝛾𝑝𝑝 (1 + 𝛼𝛼)2 2 1 + 𝛼𝛼 𝑃𝑃𝑐𝑐𝑐𝑐 𝑃𝑃𝑐𝑐𝑐𝑐 𝜖𝜖𝑝𝑝𝑝𝑝 + 𝜖𝜖𝑝𝑝𝑝𝑝 + 𝑇𝑇𝑝𝑝 + 𝑇𝑇 𝜖𝜖 = 2 𝑉𝑉𝜂𝜂 𝜂𝜂 𝜂𝜂 𝑑𝑑 𝜂𝜂 ……11 2 ≜ 𝐶𝐶2 𝜖𝜖𝑝𝑝𝑝𝑝 + 𝐶𝐶1 𝜖𝜖𝑝𝑝𝑝𝑝 + 𝐶𝐶0 , 2
𝑇𝑇
where 𝛾𝛾𝑝𝑝 ≜ ∫0 𝑝𝑝 �𝑝𝑝(𝑡𝑡)� 𝑑𝑑𝑑𝑑, is a parameter determined by the normalized transmit pulse shape 𝑝𝑝0 (𝑡𝑡) = 𝑝𝑝(𝑡𝑡)/ 𝑇𝑇𝑝𝑝 ∫0 |𝑝𝑝(𝑡𝑡)|𝑑𝑑𝑑𝑑, ω is a constant given in the expression for battery efficiency factor, V is the battery voltage, η is the transfer efficiency of the DC/DC converter, αis the extra power loss factor of the power amplifier while T p is the pulse duration and T d is the pulse duty-time. Also from the generalized equation (11) it is seen that C 0 depicts the transmitter/receiver circuit energy consumption, C 1 ϵ pt depicts the energy carried by the transmitting signal, 2 depicts the excess power loss due to nonlinear and 𝐶𝐶2 𝜖𝜖𝑝𝑝𝑝𝑝 battery discharge. Total battery energy consumption over direct link. From solving equation (11) with the values in equation (9) and (10), a quadratic function of d and P e is obtained: 2 𝜖𝜖𝑆𝑆1 −𝑆𝑆2 = 𝐶𝐶2 𝑀𝑀𝑙𝑙2 𝐺𝐺12 𝑑𝑑2𝐾𝐾 𝜖𝜖𝑝𝑝𝑝𝑝 + 𝐶𝐶1 𝑀𝑀𝑙𝑙 𝐺𝐺1 𝑑𝑑 𝐾𝐾 𝜖𝜖𝑝𝑝𝑝𝑝 + 𝐶𝐶0 =
𝐶𝐶2 𝑀𝑀𝑙𝑙2 𝐺𝐺12 𝑁𝑁02 𝑑𝑑 2𝐾𝐾 𝐶𝐶1 𝑀𝑀𝑙𝑙 𝐺𝐺1 𝑁𝑁0 𝑑𝑑 𝐾𝐾 + + 𝐶𝐶0 16 𝑃𝑃𝑒𝑒2 4 𝑃𝑃𝑒𝑒
≜ 𝐿𝐿2
𝑑𝑑 2𝐾𝐾 𝑃𝑃𝑒𝑒2
+ 𝐿𝐿1
𝑑𝑑 𝐾𝐾 𝑃𝑃𝑒𝑒
+ 𝐿𝐿0
The total battery energy consumption for the relay link S 1 R-S 2 can be expressed as the superposition of energy consumption of two direct links. It is found to be: 𝜖𝜖𝑟𝑟,𝑃𝑃1 = 𝜖𝜖1 (𝑑𝑑1 ) + 𝜖𝜖2 (𝑑𝑑2 )
𝑑𝑑12𝐾𝐾 𝑑𝑑22𝐾𝐾 𝑑𝑑1𝐾𝐾 𝑑𝑑2𝐾𝐾 + � + 𝐿𝐿 � + � + 2𝐿𝐿0 1 𝑃𝑃1 𝑃𝑃𝑒𝑒 − 𝑃𝑃1 𝑃𝑃12 (𝑃𝑃𝑒𝑒 − 𝑃𝑃1 )2 ……14 Equation (14) gives the total battery energy consumption for the relay link S 1 -R-S 2 . Solution to this equation which gives the optimum energy consumption for relay link can be obtained by taking derivative of 𝜖𝜖𝑟𝑟,𝑃𝑃1 with respect to P 1 and setting it to zero. Again to reduce the complexity, the higher order term in equation (14) is neglected, which gives: = 𝐿𝐿2 �
(𝑑𝑑2𝐾𝐾 − 𝑑𝑑1𝐾𝐾 )𝑃𝑃12 + 2𝑑𝑑1𝐾𝐾 𝑃𝑃𝑒𝑒 𝑃𝑃1 − 𝑑𝑑1𝐾𝐾 𝑃𝑃𝑒𝑒2 = 0 ……15 The above quadratic equation has its roots related only to the ratio of distances d 1 and d 2 , and the overall desired BER P e . From equation (13) it can also be noted that 𝑃𝑃1 = 𝑃𝑃𝑒𝑒 − 𝑃𝑃2 ……16 and 0 < 𝑃𝑃1 < 𝑃𝑃𝑒𝑒 ……17 With the above condition in (17), equation (15) possesses only a single positive root i.e.
𝑃𝑃1 =
𝑃𝑃2 =
……18
𝐾𝐾
𝜃𝜃 2 � 2�
𝜃𝜃 1 𝐾𝐾 𝜃𝜃 2 � 2 � +1 𝜃𝜃 1
𝑃𝑃𝑒𝑒
……19
Substituting these optimum values of P 1 and P 2 into equation (14), gives the optimum energy consumption for the relay link S 1 -R-S 2 . 𝐾𝐾
𝐾𝐾
2
𝐿𝐿
𝐾𝐾
𝐾𝐾
2
𝜖𝜖𝑟𝑟 = 𝑃𝑃22 �𝜃𝜃12 + 𝜃𝜃22 � (𝜃𝜃1𝐾𝐾 + 𝜃𝜃2𝐾𝐾 )𝑑𝑑 2𝐾𝐾 + 𝑃𝑃1 �𝜃𝜃12 + 𝜃𝜃22 � 𝑑𝑑 𝐾𝐾 + 2𝐿𝐿0 𝑒𝑒
Notice that the component L 0 is independent of distance of transmission as well as average BER of the link, and this equation can be seen as quadratic equation of 𝑃𝑃𝑒𝑒−1 with d as constant or known, or an equation of dK with 𝑃𝑃𝑒𝑒−1 as a constant. Total battery energy consumption for relay link. Let the received pulse energy for links S 1 -R be 𝜖𝜖𝑝𝑝𝑝𝑝 1 and R-S 2 be 𝜖𝜖𝑝𝑝𝑝𝑝 2 respectively. As seen in [7], the average BER for a relay link is upper bounded by: 𝑃𝑃𝑒𝑒 = 1 − (1 − 𝑃𝑃1 )(1 − 𝑃𝑃2 ) = 𝑃𝑃1 + 𝑃𝑃2 − 𝑃𝑃1 𝑃𝑃2 Practically, it is desirable that BER is very small (less than or of the order of 10-3). From this it can be concluded that 𝑃𝑃1 + 𝑃𝑃2 ≫ 𝑃𝑃1 𝑃𝑃2 and therefore the average BER for relay link can be: 𝑃𝑃𝑒𝑒 ≈ 𝑃𝑃1 + 𝑃𝑃2 ……13
𝑃𝑃𝑒𝑒
Accordingly from equation (16) and (18), 𝑃𝑃2 = 𝑃𝑃𝑒𝑒 − 𝑃𝑃1
𝐿𝐿
……12
1
𝐾𝐾 𝜃𝜃 ( 2 ) 2 +1 𝜃𝜃 1
𝑒𝑒
……20 From equation (13), (14) and (20) it can be clearly seen that part of energy that is responsible for signal transmission over direct link is getting reduced in case of relay link. And part of energy that is being used by the transmit/receive circuit is getting increased in case of relay link which is quite obvious since relay node retransmits the data of source node to the destination node. But this component is negligible while using a relay for extending the coverage distance, which is of prime concern.
IV. SIMULATION PARAMETERS QualNet 5.1 software is used for simulating the various scenarios. Two types of network namely relay assisted and without relay network are simulated. These networks had equal amount of mobile nodes and the traffic generated was also kept same for both the networks. The simulation parameters which we have configured for our network are summarized in table 1 below.
Radio type 802.11b Antenna height 1.5m Antenna model Omnidirectional Antenna Efficiency 0.8 Energy model Generic Path loss model Two ray No. of channels 2(2.4GHz, 2.6GHz) Routing protocol AODV and OLSR Application Layer Parameters Applications CBR and VoIP Packet Size 512 Bytes Items sent 1004 Average talking time 20 seconds Call status Accept Encoding CODEC G.711 Packetization Interval 20 milli-seconds MAC Layer Parameters MAC protocol 802.11 Station Scan Type Passive Network Layer Parameters Network Protocol IPv4 Routing Protocol AODV and OLSR Transport Layer Parameters TCP Enable TCP Variant Lite Maximum Segment Size 512 bytes
V. RESULT AND ANALYSIS The simulation results are gathered and compared in a graphical form. It is observed that with the help of proposed network following parameters are optimized: • Battery Consumption. • Energy used in Transmit and Receive mode. • Throughput of the network. A. Battery Consumption The data from simulation is used to plot a graph (Fig. 5) in Excel sheet which gives a better view for comparing the battery consumption factor.
Battery Total Charge Consumed (in mAhr)
2.5 AODV
2 1.5
OLSR
1
Without Relay AODV
0.5 0 1
2
3 4 5 Node ID
6
7
Withoout Relay OLSR
Fig. 5: Total battery consumed by different nodes.
Analysis: Node 1 is the BS where battery charge optimization is not of a big concern but still significant battery saving has been achieved (of the order of 0.40mAhr for a simulation time of 3min.).It can be seen that Nodes 2 & 3 which are RS, are consuming more battery. This energy can be saved by selecting different types of RS depending upon the network requirement [8]. Nodes 4, 5, 6 and 7 are MS where battery saving is of much bigger concern. User end battery charge is conserved in the proposed network model due to reduced radio link distance and establishing stronger links between RS and MS. Network level optimization has been done which shows a dip in the graph and variation at this dip is due to the implementation of routing algorithms which are AODV and OLSR-INRIA. Among the simulated scenarios AODV routing protocol best performs in saving battery. B. Energy consumed in Receive and Transmit mode Energy consumed in Receive mode Energy Consumed (in mJ) in Recieve Mode
Physical Layer Parameters
0.35 0.3
AODV
0.25
OLSR
0.2 0.15
Without Relay AODV Without Relay OLSR
0.1 0.05 0 1
2
3
4
5
6
7
Node ID Fig. 6: Energy consumed in receive mode.
Analysis: As it can be seen from Fig. 6, that energy consumption at BS has been brought down. Also MS’s (Node 4, 5, 6 and 7) energy requirements have been lowered while keeping the throughput of the network at par higher level. As far as routing protocol is concerned the proposed network best performs with AODV routing protocol under given simulation conditions. Energy consumed in Transmit mode It is observed from Fig. 7, that node 5 has zero energy consumption which is due to the fact that it has gone out of range of BS. Energy Consumed (in mJ) in Transmit Mode
Table 1
2 Without Relay OLSR Without Relay AODV OLSR
1.5 1 0.5
AODV
0 1
2
3
4
5
6
7
Node ID Fig. 7: Energy consumed in transmit mode.
Analysis: Above graph clearly shows that proposed network is more energy efficient than current scenario and AODV outperforms OLSR in terms of energy saving.
Throughput (in bits/s)
C. Throughput of the Network CBR client throughput Fig. 8 shows the throughput values for the various CBR clients present. Nodes 4 and 5 are absent in relay assisted network and same is the case with Nodes 6 and 7 in without relay network. 4160
AODV
4150 OLSR
4140 4130
Without Relay AODV
4120 4110
Without Relay OLSR
4100 4
5
6
7
VI. CONCLUSIONS In this paper power optimization has been discussed and implemented over a designed wireless network. The journey of this work starts from literature survey and mathematical analysis for different scenarios. And on the basis of both work done a network architecture is proposed which consumes less energy and overcomes the effect of power radiations in real environment without compromising with QoS and coverage of the network. On the basis of simulated results and analysis, it can be concluded that power optimization by AODV routing protocol is best for the designed network and simulation environment. So with this work it can be well inferred that relay assisted networks are a better alternative for rural to sub-urban area networks as far as ad-hoc network is concerned. The work done can be further advanced by implementing this architecture for dense urban area network with different routing protocols. Power optimization can be increased in this case by employing cross-layer optimization at MAC and Physical Layer. Careful selection of type and number of relay nodes can also help in this area.
Node ID
References
Fig. 8: Throughput of CBR client.
[1]
Analysis: Fig. 8 shows that MS which are communicating with BS through RS are achieving more throughput than the MS which are directly communicating with the BS. Achieved throughput is more in case of proposed network model.
Throughput (in bits/s)
CBR Server Throughput Fig. 9 shows the throughput of the CBR server 8 and 10 which are providing CBR packets to the clients. 9000 8000 7000 6000 5000 4000 3000 2000 1000 0
[2] [3]
[4]
[5]
8 10
AODV
OLSR Without Relay Wiyhout AODV Relay OLSR
Fig. 9: Throughput of CBR server.
Analysis: As can be seen in Fig. 9 above, throughput of our proposed network is better than the current network scenario. Obtained throughput of the relay assisted network is almost double of that of current scenario which is a significant improvement.
[6] [7] [8]
[9] [10]
[11]
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