Wireless Pers Commun (2015) 81:503–517 DOI 10.1007/s11277-014-2141-0
Optimized Battery Models Observations for Static, Distance Vector and On-Demand Based Routing Protocols Over 802.11 Enabled Wireless Sensor Networks Vinod Kumar Verma · Surinder Singh · N. P. Pathak
Published online: 4 November 2014 © Springer Science+Business Media New York 2014
Abstract We analyze a wireless sensor network system to address the impact of different battery models on five routing protocols. We present an analytical model to understand the key performance metrics like average jitter, first and last packet received, total bytes received, average end to end delay, throughput and energy consumption. We have proposed the various parameters of our model for static, distance vector and on demand based routing protocols along with linear and service life estimator battery model. The validation of proposed parameters through simulation and derive substantial investigations in wireless sensor network system. Keywords
Bellman-ford · RIP · DSR · AODV · DYMO · WSN
1 Introduction Wireless sensor networks (WSNs) is an emerging technology which finds applications in many scenarios such as monitoring physical environments tracking animal, migrations in remote-areas [1], weather conditions in national parks [2], habitat monitoring on remote islands [3], city traffic monitoring, military surveillance, healthcare etc. Over the last few years, wireless sensor networks [4,5] have gained more and more attention due to their wide spread usages both in the industry and academia. The reason behind the popularity of WSNs is the ease of development provided towards wireless communication. The objective is to collect information and deliver to the specific access point of the underlying structure.
V. K. Verma Department of Computer Science and Engineering, SLIET, Longowal, India S. Singh (B) Department of Electronics and Communication Engineering, SLIET, Longowal, India e-mail:
[email protected] N. P. Pathak Department of Electronics and Communication Engineering, IIT, Roorkee, India
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A wireless sensor network consists of a group of sensor nodes connected through a linked mechanism in order to perform distributed sensing task. In context of power constrained in wireless sensor networks, a significant research has concentrated on developing distributed data processing and cooperative communication strategies. Jain et al. [7] estimated Mobility exploitation for energy efficient wireless sensor network. Several models for energy efficient estimations of wireless sensor networks have been proposed recently. Sensor observation based on the quantized evaluation was reported in Ref. [8,10,11]. Power allocation scheme for sensor network was suggested by Cui et al. [9]. Massachusetts Institute of Technology [6] reviewed sensor networks as top ten technologies that will change the world. Power scheduling scheme for minimization of outage probability of estimation distortion in cluster based WSN was proposed in Ref. [12–14]. Positional based routing for wireless ad-hoc network was presented by Ivan [15]. Efforts for designing guidelines and estimating physical layer impact were discussed in Ref. [16,17]. Initiative towards the research based on scalable quorum based location service and optimizing cost over progress ratio for localized network layer protocols for sensor network was reported in Ref. [18,19].A classification of energy efficient routing protocols was suggested in Ref. [20]. For optimal performance of wireless sensor networks, challenging issues like energy consumption, network routing, localization, coverage and physical environment must be addressed. Low power and inexpensive nodes are required to meet the performance goal of the wireless sensor network system. Analytical modeling of WSN and real performance prediction is extremely critical to measure. Here, we emphasized towards the network routing protocol estimation with battery model to achieve the optimal resultant for the five routing protocols. The breakdown of the paper is as; Sect. 2 provides a brief description about two of the battery models for wireless sensor network. In Sect. 3, we described five different routing protocols with related works in context of wireless sensor networks and Sect. 4 highlights our proposed scenario. We estimated scenario parameters and examine the behavior of five routing protocols under linear, service life estimator battery models in Sect. 5. Finally, in Sect. 6, we conclude with a resultant about the analytical assessment of five routing protocols with battery models in wireless sensor networks.
2 Battery Models with Related Work This Section provides the background and related work on linear and service life estimator battery models with assumptions required on the designed frameworks for the later sections. 2.1 Linear Model This model uses coulomb counting technique as its basis for operation. The coulomb counting technique accumulates the dissipated coulombs from the beginning of the discharge cycle and estimates the remaining capacity by measuring the difference between the accumulated value and a prerecorded full-charge capacity [21–23]. In the variable load condition, this method might lose accuracy as it ignores non- linear discharge effect. The battery is discharged in a linear fashion as a function of discharge current load. 2.2 Service Life Estimator Model This battery model uses modular approach and can estimate the service life of a battery operated node with time varying load for an event driven scenario [21–23]. On the underlying
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side, this battery model deploys the tightly coupled component methodology as suggested by Sarma and Rakhmotav [21]. For the evaluation, Rakhmotav model remains the most accurate model than other models using partial differential equation. For estimation purposes, one can utilize the following equations under constant load [22].
2 2 10 1 − e−β m L α ≈ I L +2 β2m2
(1)
m=1
The battery voltage changes with time from open-circuit value (Vopen ) to some cutoff value (Vcuto f f ) for a mentioned load. The observed lifetime denotes that battery voltages reaches Vcuto f f and predicted timedenotes the time for which equality (1) holds for a given set of constant loads corresponds to observe lifetimes. To achieve the objective to match predicted lifetime closely to observed lifetimes which is hard as for (1)? Another way is to fit the load value for a given set of observed lifetimes. Assume I ∧ (k)be the fitted value for I(k) and according to [21] I ∧ (k) ≈
L (k) + 2
α 10 m=1
2 2L
1−e−β m β2m2
(2)
α and β represent objective specific parameters. One can employ a standard least-squares estimator method to find matches I (k) as closely as possible for all. The selection should be such that the model parameters as given below M ∧ I (k) − I (k)
(3)
k=1
is minimized.
3 Static, Distance Vector and On-Demand Routing Protocols Overview This Section highlights the background and related work on static, distance vector and on demand based routing protocols with related work in wireless sensor networks. 3.1 Static Protocol- Bellman-Ford This protocol is based on the Bellman-ford algorithm also called Bellman-ford Moore algorithm. It computes a shortest path tree (SPT) and calculates the minimum path for all vertices in a weighted diagraph through a single source vertex from each router to other routers in a routing area [24]. In contrast with Dijkstra algorithm, it is slower but more versatile as it handles the negative edge weights. For many applications, we need negative cycled graphs hence it becomes useful [25]. In case of negative cycled graphs, early detection is possible through the Bellman - ford but correction is not possible for the same [26]. For the implementation of list where the nodes based on first come first serve principle, bellman-ford is surely beneficial. Cheng et al. [27] analyzed the bellman ford algorithm for its extended version without bouncing effect. A wireless sensor network evaluation with loop free bellman ford protocol was reported in 2009 [28].
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3.2 Distance Vector Based Protocol—RIP A widely used protocol for both local and wide area network is routing information protocol (RIP) which is similar to open shortest path first routing protocol (OSPF). It can be categorized as an interior gateway protocol (IGP) using distance vector routing algorithm. Hedrick [29] proposed at the initial stage of this protocol in 1988, which was further refined by Malkin in 1998 [30]. Advanced techniques such as OSPF and OSI protocol IS–IS have been supported by routing information protocol [31]. As far as the merits of RIP are concerned, it is easily configurable, support load balancing and loop free. On the contrary, RIP shows slower performance specifically when used for very large network and at the most it can measure 15 hops maximum [32]. 3.3 On Demand Based Protocol -I- DSR Dynamic source routing (DSR) protocol is an on-demand routing protocol specifically designed for the multi-hop wireless networks [33]. The major difference between this protocol and other on demand routing protocol is that it is beaconless and does not require periodic beacons. DSR Protocol provides a lot of characteristics like self configurability, self adaptability that makes a network efficient [34]. DSR protocol allows the dynamic discovery of a source node and a destination node in the network. It constitutes order lists of nodes that contain all the information about the data packet life cycle like the beginning stage, intermediate stage and the final stage. From functionality point of view, DSR contains two mechanisms namely—route discovery and route maintenance. In the first mechanism, a node wishing to send a packet to a particular node obtains a source route from that node. Further, route will be discovered only if it does not already exist. In the second mechanism, a node detects the route with earlier discovered route provided there should be condition of network topology change incorporated. Route maintenance mechanism required only when there is packet transmission breakage between nodes. Route maintenance mechanism and route discovery mechanism are demand specific in their nature i.e. on demand type. As comparison with other protocols, DSR requiring no periodic packets as well as no periodic routing advertisement like link status or neighboring packet detection [35]. These properties take packet overhead to minimum value correspond to the stationary nodes. When the nodes are mobile, the routing packet overhead scales automatically to the required number of track as needed. This allows the routing protocol to behave appropriately in both the conditions either nodes are static or dynamic. Verma et al. [36] evaluated the performance of dynamic source protocol in wireless sensor network. 3.4 On Demand Based Routing Protocol-II-AODV The ad-hoc on demand distance vector (AODV) routing protocol basically extends Bellmanford distance vector algorithm concept in a relative manner. The AODV routing protocol was specifically designed for the highly dynamic wireless networks [37–39] but the un-predictable topology change in wireless sensor network by node failure makes them virtual dynamic networks. Hence reactive routing protocols represent an adequate choice for event driven or periodic data driven WSN applications especially. Being a reactive type of protocol, routes here are created only when required. It contains one entry per table and a sequence number as similar to the traditional approach of routing to maintain up to date routing information. AODV ensures loop free routing in the different situations and stick towards the time based state information with each node, so that any node that is not recently used should be treated
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as dead node. The AODV routing protocol constitutes the traditional concept of routing table which stores the parameters such as routing information, next hop address, a sequence number and node usages because the node maintains specific time spam thereafter its entry should be discarded [40]. In case of any link failure, the neighboring should be notified about it. In AODV, routing can be determined by two cycles: query and reply with four control type of messages namely Routing request message (RREQ), Routing reply message (RREP), Routing error message (REPP) and HELLO message. During execution a node broadcasts RREQ message to another node, after that RREP message is received in the unicast manner, and error message RERR regarding the failure of a link should be notified to the neighboring nodes [41]. The HELLO message is used to evaluation and detection of the links between the various nodes. The behavioral assessment of AODV protocol over temporal constraints in wireless sensor network was reported by Verma et al. in 2012 [42]. Cross layer design of AODV for multi-hop flow in wireless network was suggested by Chou et al. [43]. 3.5 Dynamic On-Demand Based Protocol -DYMO One of the simple and fast routing protocols for multi-hop networks is dynamic MANET on demand routing protocol (DYMO) [44,45]. It discovers the routes in an on demand fashion and offering enhanced coverage for dynamic topologies within the networks. Similar to AODV, source sends a data packet with RREQ message to discover the route. DYMO router waits for a route after issuance of the RREQ message. If during the waiting period route is not obtained, it may issue another RREQ. It uses exponential back off mechanism to reduce the congestion in the network. Data packets are buffered which are still to be routed as per the predefined size whereas older packet being discarded accordingly. An REER message is issued if a data packet cannot be delivered to the destination due to missing route. In each DYMO router, little state information like the active source and destination is maintained because the applicable devices such as WSN have memory constraints. 4 Motivation for Current Work To choose adequate battery model with particular routing remains the top priority for the performance assessment of wireless sensor networks. An optimal choice of battery model surely enhances the performance of the overall system, but the wireless sensor network system may not be dependent on the same. Simple battery model may give the better result for single instance but we have to deploy such a model that provides optimal results in most of the situations. Improper selection of battery model may overload the entire network and consume more resources both in terms of energy and computation resulting in the entire system performance degradation. There always remains dire influence of battery model selection on the entire operating environment when evaluating a specific routing protocol. The goal remains there is to carefully choose and examine the battery models for routing protocols and present an optimal solution without compromising any constraints than expected outcome. Therefore, a typical investigation should be required to access the scope of a particular battery model with specific routing protocol in wireless sensor networks. 5 Proposed Evaluation Model We discussed two different battery models over five of the routing protocols in wireless Sensor Networks. Now, it is time to evaluate these routing protocols on performance determining
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parameters basis like average jitter, first packet arrival time, average end to end delay, total byte received, throughput, and energy consumption. For this, we have developed a whole scenario focused on three main targets. First, we are interested in finding out how much accurately our model is able to work with different WSNs routing protocols. In other words, we would like to know about the tolerance limit of the system in contrast with bellman ford, RIP, DSR, AODV and DYMO specific WSNs. Since our model has a strong basis on random or probabilistic decisions, we considered that it would be also quite interesting to take care about the average jitter, packet reception, end to end delay, throughput and energy consumption of the entire system. Parameters like lesser jitter and shorter delay are always given due consideration as it consumes fewer resources. Finally, as a possible measure of the adaptability of our model specifically to WSNs, we gathered as well the energy consumption of routing protocol of our concerns with different battery models. In the simulation model, there were 100 nodes connected to one wireless station with terrain dimensions 1,500 m × 1,500 m as flat area and attitude range above and below sea level is 1,500 m. The entire area was further divided into 225 square shaped cells. Nodes can be either static or dynamic one. This simulation used IEEE standard 802.11 with Distributed Coordination Function (DCF) as MAC layer protocol [46]. The propagation model used was two-ray with 2 Mbps radio bandwidth and one communication channel with 2.4 GHz frequency. The traffic type was constant bit rate (CBR).The selection of source and destinations for each CBR were in a random manner. The flow of data for each source and destination node remains constant during lifetime of a simulation execution which lasted for 300 s. The mobility model was random waypoint with the speed ranging from 0 to 20 m/s and a pause time of 30 s. Numbers of CBR flows were 10 with mobility interval 100 ms in all simulation sets. Table 1 shows the summarization of the parameters used within our proposal.
6 Performance Evaluations and Results We conducted extensive simulations to evaluate the performance of two battery models with bellman ford, RIP, DSR, AODV and DYMO routing protocols. Simulations were implemented on Qualnet 5.0.2 [47], a discrete event simulator and capable of simulating both the wired or wireless scenarios from simple to the complex situations. We collected data for seven performance metrics, namely, average jitter, first packet received, last packet received, total bytes received, average end to end delay, total byte received, throughput and energy consumption. The first six metrics were evaluated in all simulation sets. The energy consumption was evaluated separately for linear and service life estimator battery models within the proposed scenario. 6.1 Average Jitter In our evaluation, we judged average jitter accuracy of five protocols with service life estimator model as shown in Fig. 1. Average Jitter denotes the time variation measured between the arrival of the packets due to the congestion of the network, timing drift or route change. We calculated value of average jitter at ten randomly selected nodes 47, 50, 54, 63, 74, 84, 87, 95, 98, 99 with constant bit rate assuming at least ten percent nodes are participating in communication and rest of nodes are connected to central base station. These nodes act as a representative for the entire network. We observed that if turn by turn, we change the node value then we get the same resultants in all the cases. Average jitter value of bellman ford protocol outperforms other protocols for nodes 47, 50, 54, 74, 82, 98.
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Parameters
Value
Terrain dimensions
1,500 m × 1,500 m
Attitude above sea level
1,500 m
Simulation time
300 s
No. of nodes
100
Mobility interval
100 ms
No. of channel
1
Channel frequency
2.4 GHz
No. of CBRs
10
MAC protocol
802.11
Node placement
Random
Traffic type
CBR
Data rate
2 Mbps
Mobility model
Random waypoint
Network protocol
IPv4
Routing protocol
Bellman-ford, AODV, DSR, DYMO, RIP
Battery models
Linear, service life estimator
Battery type
Duracell (AA)
Battery charge monitoring interval Temperature (K)
60 s
Antenna model
Omni directional
Path-loss model
Two ray
290
0.045 Bellman Ford RIP DSR AODV DYMO
0.04
Average Jitter (s)
0.035 0.03 0.025 0.02 0.015 0.01 0.005 0
40
50
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70
80
90
100
Nodes
Fig. 1 Graph of average jitter versus routing protocols over service life estimator model
In context of Bellman ford protocol, a novel analytical model for wireless sensor network based on M/G/I/k queuing system and bellman ford routing strategies to predict average message latency was reported by Barloo et al. [56]. We extended similar concept towards estimation of bellman ford protocol performance based on average jitter, packets delivery, throughput and end to end delay for the same in contrast with other protocols. RIP protocol
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First packet Received (s)
4.5 Bellman Ford RIP DSR AODV DYMO
4 3.5 3 2.5 2 1.5 1 40
50
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100
Nodes
Fig. 2 Graph of first packet reception versus routing protocols over service life estimator model
outperforms the other protocols for the node value 63, 87 and 95. DYMO performs better in case of node 99 than rest of protocols. Authors in [58] made a comparative analysis among DSR and RIP protocol and investigated that RIP outperforms DSR in case of average jitter which also remains true in our case as well. We enhanced this aspect towards comparisons of RIP with four other protocols. 6.2 Packet and Total Bytes Reception Analysis Secondly, we estimated the time spam for the first packet reception in all the protocols as shown in Fig. 2. We can rank them in order of first packet reception as (1) Bellman-ford, (2) AODV, (3) DSR, (4) DYMO, (5) RIP. As far as last packet reception is concerned, small change in sequence as (1) Bellman ford (2) DSR (3) RIP (4) DYMO (5) AODV. Again Bellman ford outperform rest of the protocols at the first node i.e. node 47 as depicted in Fig. 3 whereas DYMO outperform other protocols at last node i.e. node 99. We analyzed that functionality involved in a specific protocol for its operation is responsible for the same. We observed that for a small network, Bellman ford outperforms other and in case of large networks, DYMO shows better behavior. We estimated the total bytes received at the nodes after the deployment of these five protocols and reported that DSR outperforms rest of the protocols because of byte destruction rate remains quite less than other protocols. The order of error proneness during bytes transmission for these protocols can be mentioned as (1) DSR, (2) DYMO, (3) AODV, (4) RIP, (5) Bellman-ford as shown in Fig. 4. In context of AODV protocol, three optimizations were studied by Lee et al. [54]. Theses optimization includes ring search, a query localization protocol and local repair mechanism. Researchers in [55] made a comparison among AODV and DSR protocols and proposed a new protocol incorporating features of these two protocols. We extended this work towards the evaluation two battery models with five different routing protocol including AODV and DSR. 6.3 Throughput This refers to the number of delivered packets per unit of time within the network. Figure 5 shows throughput of Bellman-ford, RIP, DSR, AODV and DYMO for our proposed model. Raghuvanshi et al. [57] reported an average throughput remains highest for DYMO protocol
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Last Packet Rececived (s)
24.08 Bellman Ford RIP DSR AODV DYMO
24.07 24.06 24.05 24.04 24.03 24.02 24.01 24
40
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100
Nodes
Fig. 3 Graph of last packet reception versus routing protocols over service life estimator model
1.24
x 10
4
Total Bytes Received
1.22 1.2 1.18 1.16 1.14 Bellman Ford RIP DSR AODV DYMO
1.12 1.1 1.08 1.06
40
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60
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100
Nodes
Fig. 4 Graph of total byte reception versus routing protocols over service life estimator model
and energy consumption remains least when used with 25 our evaluation, we extended this work a bit more intricate level and evaluated DYMO protocol in contrast with AODV, DSR, RIP and bellman ford. We observed that DYMO exhibit the maximum throughput than other protocols for most of the times, where as bellman ford shows the decrement in behavior for throughput in approximately all the cases. We analyzed that the enhanced mechanism involved in DYMO protocol responsible for that which lowers the route failure rate, resulting in better throughput all the time than other protocols in the scenario. 6.4 Average End-To-End Delay This refers to the time from source to destination node taken by a packet across the network. This includes transmission resultant, propagation and processing delay or all possible delays
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V. K. Verma et al. 4500 Bellman Ford RIP DSR AODV DYMO
Throughput (bits/s)
4400
4300
4200
4100
4000
3900 40
50
60
70
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100
Nodes
Fig. 5 Graph of throughput versus routing protocols over service life estimator model
Average End-to-End Delay (s)
0.08 Bellman Ford RIP DSR AODV DYMO
0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 40
50
60
70
80
90
100
Nodes
Fig. 6 Graph of average end-to-end delay versus routing protocols over service life estimator model
which can occur during packet transmission. Figure 6 shows the average end-to-end delay of bellman ford, RIP, DSR, AODV and DYMO. We observed that Bellman ford exhibits the lowest end-to-end delay for most of the times. At the end point i.e. node 99, AODV protocol shows better behavior than other protocols and exhibits lower end to end delay. In the Fig. 6, the end-to-end delay does not show an obviously increasing trend due to constant number of CBR sources used in our scenario. We again observed that for smaller networks, bellman ford outperforms other protocols because of the shorter average route even if uses more congested networks. On the other hand, we analyzed that for larger networks, AODV performs better due lesser congested networks even if consumes more average route length. Manju et al. [57] reported that there exists a tradeoff between delay and throughput in case of DYMO protocol which remains true in our case as the delay remains most of the times. Raghuvanshi et al. [59] shows that DYMO exhibits better performance when using linear battery model than AODV, DSR and Bellman-ford in terms of throughput on the scarification of end to end
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Residual Battery Capacity (mA.h)
2800 2600 2400
Linear Model Service Life Estimator Model
2200 2000 1800 1600 1400 1200 1000 1
1.5
2
2.5
3
3.5
4
4.5
5
Rouing Protocols Fig. 7 Energy consumption analysis battery models versus routing protocols
delay aspect. We extended this work by incorporation RIP protocols and using service life estimator battery model in the comparative evolution of these protocols. 6.5 Energy Consumption Energy consumption issue always remains as a major concern in wireless sensor networks. We calculated the average energy consumption by bellman ford, RIP, DSR, AODV and DYMO protocols over linear and service life estimator models in wireless sensor network. Energy consumption of a network represents the amount of energy consumed by all the nodes in the network including the summation of energy consumption in the idle mode, transmit mode and receive mode. As per ref. [48,49], power requirement of a sensor node can be analyzed as a function of distance. For most of the models, energy consumption E by a message at a distance d is given by [50,51]: E (d) = d a + C
wher e a denotes attenuation f actor and C constant
(4)
used for radio signal and dimensionless. Figure 7 compares the five routing protocols from energy consumption aspect. A comparative analysis of the energy consumption with respect to sensors value increment was reported in Ref. [40]. In our proposal, we extended this concept towards the different WSN routing protocols by correlating these protocols with different battery models. We observed that DYMO protocol consume minimum power in both the cases of linear and service life estimator battery model than other protocols because of the fact that the residual battery capacity remains maximum in the same case. Wangle et al. [50] reported that under the linear model, DYMO protocol represents best behavior when compared with bellman ford, DSR and AODV. We further extended this comparative analysis to RIP protocol with service life estimator model in our consideration. We analyzed that as far as the maximum power consumption concerned, bellman ford consumes maximum power in both the cases as it shows less residual battery capacity. According to Fig. 7, x-axis represents routing protocols and y-axis denotes as battery capacity in mAh. We assigned numbers to routing protocols in Fig. 7 as 1 to bellman-ford, 2 to RIP, 3 to DSR, 4 to AODV and 5 to DYMO.
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7 Conclusion and Future Directions Wireless sensor networks have emerged as a novel and innovative research field of work in the last few years. We evaluated a WSN framework incorporating linear and service life estimator battery models for quantifying and comparing their performance with five routing protocols namely: Bellman ford, AODV, DSR,RIP and DYMO Nevertheless, as far as we know, this is one of the first works mainly focused toward the comparative analysis of WSN battery models with routing protocols. We reported initial simulation based experiments demonstrating our proposed model. Our research on wireless sensor network assessment continues along certain directions. Firstly, we surveyed about two WSN battery models with five different routing protocols in details. After surveying the current state of art in these models, a number of aspects like average jitter, packets delivery, throughput, end to end delay and energy consumption have been calculated and analyzed. Finally, we investigated towards the implementation and assessment of these models in our proposed framework. We observed that DYMO routing protocol performance overweighs rest of the protocols in our proposal and service life estimator model outperform the linear model in all the cases of energy consumption. Our future work will emphasize on utilization of heuristic methods towards selection of routing protocols for a particular WSN system. Also, we will incorporate the criteria of more routing protocols, different security threats [52], collusion i.e. trust probability among nodes [53] and effort towards further standardization of network routing protocols. Additionally, we would like to focus on three major aspects as enhancement, restructuring and extending the experiments carried out to demonstrate accuracy of our proposed scenario. Finally, we expect this work seems to a benchmark for designer towards the development of new routing protocols development and evaluation. Acknowledgments We would like to thank Department of Electronics and Communication Engineering, SLIET, Longowal, India for providing us Wireless SignalPro software. Any opinions, view, findings and conclusions or recommendations expressed in this research work are those of the authors only and do not reflect any other agencies viewpoints. Last but not least, we would like to thank the reviewers for their valuable suggestions which bring the manuscript in present form.
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Vinod Kumar Verma born in Kalka (Haryana), India. He received the B.Tech degree in Computer Engineering from Kurukshetra University, India. He received M.S degree from BITS Pilani, India. He is currently working towards the Ph.D. degree at Sant Longowal Institute of Engineering and Technology, Longowal, India. His fields of interests are Wireless Sensor Networks, Distributed Computing, Cryptography and Software Systems.
Surinder Singh born in Hoshiarpur (Punjab), India, on December 27, 1975. He received the B.Tech. degree from Dr B. R. Ambedkar Regional Engineering College, Jalandhar in 1997 and M.Tech. degree from Guru Nanak Dev Engineering College, Ludhiana in 2003. He obtained the Ph.D. degree from Thaper University, Patiala, India. His field of interest is optical amplifiers, sensor and antenna for broadband communication system & networks. He is a Senior Lecturer in Giani Zail Singh College of Engineering and Technology, Bathinda (Punjab), India from 1998 and now acts as Associate Professor at Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, Punjab, India in the Department of Electronics and Communication Engineering. He has over 88 research papers out of which 35 are in international journals and 53 are in international and national conferences. Mr. Singh is a member of Indian Society for Technical Education, Institution of Engineers (India).
N. P. Pathak born in Azamgarh, Uttar Pradesh, India. He received the B.Tech. and M.Tech. degrees in Electronics and Communication Engineering from the University of Allahabad in 1996 and 1998, respectively. From 1999 to 2000, he was a Junior Research Fellow with the Photonics Division, Instrument Research and Development Establishment (IRDE), Dehradun, India, where he was involved in the area of integrated optics. In December 2000, he joined the Centre for Applied Research in Electronics, Indian Institute of Technology Delhi. Dr. Pathak was the recipient of a 2004 Institution of Electronics and Telecommunication Engineers (IETE) (India) Research Fellowship. He was Post Doctoral Research fellow at the NRD Broadband Research Centre, Tohoku Institute of Technology, Sendai, Japan. Dr. Pathak is now working as Associate Professor in the Department of Electronics and Communication Engineering at Indian Institute of Technology, Roorkee. His current research interests are development of adoptable microwave/millimeter circuits and dielectric integrated guides at millimeter-wave and optical frequencies.
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