Network Lifetime Measurement for Mobile Wireless Sensor Networks
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Network Lifetime Measurement for Wireless Sensor Networks Mandar S Karyakarte1,2
[email protected]
Anil S Tavildar1
Rajesh Khanna2
[email protected]
[email protected]
Sanket Lokhande1
[email protected]
1
VVishwakarma Institute of Information Technology, Pune, Maharashtra. 2 Thapar Univeristy, Patiala, Punjab.
ABSTRACT Abstract: Wireless Sensor Networks have attracted the attention of researchers in the recent years. In this paper we present design, implementation and deployment of WSN using waspmote and network lifetime measurements. The network lifetime measured is compared with network lifetime estimation approach. The estimation results show similar trend and are very close with the network lifetime measurement Keywords: network lifetime, WSN deployment
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INTRODUCTION
Wireless sensor network (WSN) consists of low power battery operated inexpensive sensor nodes capable of collecting information from the environment and communicating with each other via wireless communication links, to deliver the data to the sink. The dense and large-scale deployment, bandwidth and energy constraints pose a challenge for efficient allocation, utilization and management of resources. The typical applications for WSNs include environmental monitoring, habitat monitoring, natural calamity prediction, security and surveillance, health-care, manufacturing and process control in industries etc. Air pollution is one of the serious environmental concerns of many urban Asian cities, including India, where majority of the population is exposed to poor air quality. The health related problems such as respiratory diseases, risk of developing cancers and other serious ailments etc. due to poor air quality are known and well documented. Besides the health effects, air pollution also contributes to tremendous economic losses, especially in the sense of financial resources that are required for giving medical assistance to the affected people. The rapid urbanization in India has also resulted in a tremendous increase the number of motor vehicles. This is the main source of air pollution and poor ambient air quality impacting millions of dwellers. We have designed, implemented and deployed a WSN for monitoring air quality levels for gases like carbon monoxide (CO), carbon dioxide (CO2), oxygen (O2) and Ozone (O3) affected due to vehicular pollution. Also temperature and humidity are measured. The sensor nodes were used over the waspmote platform[1] For WSNs, the network lifetime is the crucial issue, since in many applications it is not feasible to recharge or replace the
power source of the sensor nodes. As the sensor energy conservation is much important for WSN, efficient utilization of the energy to prolong the network lifetime has been the focus of much of the research on WSNs. Network lifetime has been defined in many different ways [2]. From the energy dissipation viewpoint the network lifetime is defined as the time instance since the network deployment until the first node exhausts its energy below the minimum energy required for transmission under any channel condition. In this paper, WSN routing protocols namely Connectivity-based Cross-layer Opportunistic Forwarding (CCOF)[3], Energy Efficient Opportunistic Routing (EEOR)[4] and Ad-hoc On-demand Multipath Distance Vector (AOMDV) [5], are implemented on WSN hardware setup consisting of programmable nodes called waspmotes[1]. The approach presented in our paper [6] is used to measure the network lifetime of WSNs using the deployed network. Also the network lifetime measured helps to validate the simulation results of above protocol presented in by us in [3]. The paper is organized as follows. Section 1 has given the introduction and summarized the work done by other researchers. Section 2 presents the brief description of routing protocols used. Section 3 elaborates the network deployment and results followed by the conclusion. 2.
DISCUSSION ON ROUTING PROTOCOLS
2.1. EEOR Mao Xuefi et. al. [4] has focused on how to select and prioritize the forwarding list to minimize the total energy cost of forwarding data to the sink node in a wireless sensor network. The authors study two complementary cases (1) the transmission power of each node is fixed (known as nonadjustable transmission model) and (2) each node can adjust its transmission power for each transmission (known as adjustable transmission model). Optimum algorithms to select and prioritize forwarder list in both cases are presented and analyzed. Using the similar mechanism of distance vector routing, the calculations of the expect cost for each node will be carried out periodically and every node updates its expected cost and forwarder list periodically. When a node needs to send or relay a packet to some destination node, it will simply broadcast the packet and let some node(s) in its forwarder list (constructed according to the destination node) to recursively forward the data packet. 2.2 CCOF
Network Lifetime Measurement for Mobile Wireless Sensor Networks We have presented Connectivity based Cross-layer Opportunistic Forwarding (CCOF), an opportunistic routing protocol in [3]. Design and implementation of CCOF routing protocol for MWSNs aims to reduce packet duplicates, overheads and energy consumption. We calculate the expected cost of forwarding as combination of node connectivity & energy consumption; and also use wireless broadcast advantage (WBA) to reduce packet retransmissions. The node connectivity is calculated using algebraic connectivity of the network graph. A prioritized forwarder list is generated and the first node in the list is selected as next relay for data forwarding. A forwarding agreement mechanism implemented among the nodes in the forwarder list enables coordination among the forwarder nodes to have single copy of packet being forwarded towards the sink. 2.2. AOMDV Ad-hoc On-demand Multipath Distance Vector Routing (AOMDV) [5] protocol is an extension to the AODV protocol for computing multiple loop-free and link disjoint paths. There can be multiple next hops for the same destination with same sequence number. This helps in keeping track of a route. An advertised hop count is maintained for each destination by node. Advertised hop count is the maximum hop count for particular destination. Each duplicate route advertisement received by a node defines an alternate path to the destination. Loop freedom is assured for a node by Advertised hop counts. Alternative paths are only considered if they have less hop count than advertised hop count. Because the maximum hop count is used, the advertised hop count therefore does not change for the same sequence number [7]. When a route advertisement is received for a destination with a greater sequence number, the next-hop list and the advertised hop count are reinitialized. AOMDV can be used to find nodedisjoint or link disjoint routes. To find node-disjoint routes, each node does not immediately reject duplicate RREQs. Each RREQs arriving via a different neighbor of the source defines a node-disjoint path. This is because nodes cannot be broadcast duplicate RREQs, so any two RREQs arriving at an intermediate node via a different neighbor of the source could not have traversed the same node. In an attempt to get multiple link-disjoint routes, the destination replies to duplicate RREQs, the destination only replies to RREQs arriving via unique neighbors. After the first hop, the RREPs follow the reverse paths, which are node disjoint and thus link-disjoint. The trajectories of each RREP may intersect at an intermediate node, but each takes a different reverse path to the source to ensure link disjointness [5]. The advantage of using AOMDV is that it allows intermediate nodes to reply to RREQs, while still selecting disjoint paths. But, AOMDV has more message overheads during route discovery due to increased flooding and since it is a multipath routing protocol, the destination replies to the multiple RREQs those results are in longer overhead.
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Figure 1. WSN network diagram
Figure 2. Gases sensor board for monitoring the vehicular pollution.
Figure 3. Waspmote communication board 3. NETWORK TOPOLOGY WSN topology deployed using waspmotes[1] is shown in Figure 1. The network consists of 4 sensor nodes, 4 router nodes and a gateway. The gases board is attached to sensor
Network Lifetime Measurement for Mobile Wireless Sensor Networks nodes and routers only have communication board. Each sensor node used is capable of monitoring vehicular pollution in cities. The sensor board is equipped with CO2, CO, O2 and O3 gas sensors to monitor the levels. The gas board used is shown in Figure 2. The communication board is shown in Figure 3. The communication is done using IEEE 802.15.4 standard in 2.4GHz ISM band. The sensor nodes are sleeping most of the time, in order to save battery. After some minutes (programmable by the user), the node wakes up, reads from the sensors, implements the wireless communication and goes again to sleep mode. Each device can be powered with rechargeable batteries and a solar panel, making the system very autonomous. The sensor node, Gases Sensor Board, Sensors devices were placed inside an IP65 enclosure to be able to be mounted at busy square or bus roof top. With such a position, constant readings of these parameters are performed. With the help of GPRS data communication, all readings are delivered to the server where it is processed and stored. The protocols CCOF, EEOR and AOMDV were programmed in each sensor node and router. The network was kept operational until the battery level was reduced till no more packets could be sent. For experimental setup we monitor the gases at every 15 seconds interval but practically the monitoring interval needs to be dynamic based on time of day. Monitoring can be at short intervals during peak traffic hours and at longer intervals during low traffic hours to prolong the operational lifetime of the network. 3.1. Results and Discussion The Figure 4. Shows the lifetime measurement for CCOF, EEOR and AOMDV routing protocols. The lifetime measured for CCOF, EEOR and AOMDV was 48 hours, 37 hours and 44 hours respectively. The better performance pf CCOF as against EEOR and AOMDV was in accordance with protocol ranking done on the basis simulation results presented in [3].
Also our approach proposed for estimating lifetime presented in [6] is used to estimate the lifetime for comparison with experimental measurement. The rate of battery energy depletion was calculated using the time duration required for reduction of battery level from 100% to 75% for all the three protocols viz CCOF, EEOR and AOMDV. The expected network lifetime was estimated using rate of depletion, energy consumption for each transition and initial battery energy. The results obtained are summarized in Table 1. CCOF EEOR AOMDV 48 37 44 Lifetime measurement 46 36 41 Lifetime estimation[6] Table 1. Summary of lifetime measurement and estimation in hours
CONCLUSION WSN for vehicular pollution monitoring has been successfully designed, implemented and deployed. The network will be useful in monitoring the vehicular pollution. Further the network can redeployed at any desired location as well as can be enhanced to any other application. The use for any other application requires change in the sensors. The network lifetime measurement exhibits the similar trend like lifetime estimation approach for all the three protocols.
ACKNOWLEDGMENT This work is supported through a research grant from Rajiv Gandhi Science and Technology Commission (RGSTC), Government of Maharashtra, India for research project to establish a system for monitoring air quality for vehicular pollutant using wireless sensor networks. The authors are thankful for the support extended by RGSTC, Government of Maharashtra
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REFERENCES
Battery Level (%)
CCOF 100
[1]
Waspmote Datasheet, Libelium Inc, Spain http://www.libelium.com/downloads/documentation/waspmote_datashe et.pdf accessed on 16th March 2015@ 11:10 pm.
[2]
D. ISABEL and D. FALKO, "On the Lifetime of Wireless Sensor Networks," ACM Transactions Sensors Networks, vol. 5, no. 1, January 2009.
[3]
M.S. Karyakarte, A.S. Tavildar and R. Khanna, “Connectivity-based cross-layer opportunistic forwarding for MWSNs”, accpeted and to be appear in Taylor and Francis IETE Jurnal of Research.
[4]
M. Xufei, T. Shaojie, X. Xiahua, L. Xiang-Yang and M. Huadong,”Energy-efficient opportunistic routing in wireless sensor networks.” IEEE Trans. Parallel and Distributed Systems, Vol. 22, No.11, 2011, pp 1934-1942.
[5]
M. K. Marina and S. R. Das, ”Ad hoc on-demand multipath distance vector routing”, Wireless Communications and Mobile Computing, Vol.6, 2010, pp 969-988..
[6]
M.S. Karyakarte, A.S. Tavildar and R. Khanna.” A Deterministic Approach for Prediction of Network Lifetime for Mobile Wireless Sensor Networks”, ICEIT national conference, Sept 2013.
EEOR
80
AOMDV
60 40 20 0 0
20
Time(hours)
40
3
60
Figure 4. Measurement of lifetime using the experimental WSN.
Network Lifetime Measurement for Mobile Wireless Sensor Networks
Mandar S Karyakarte is pursuing Ph.D degree from Thapar University, Patiala, Punjab, India. He has completed his Masters degree in Computer Engineering in the year 2007 and Bachelors degree in Computer Engineering in the year 2003.
Anil S Tavildar is working as Professor Emeritus in Electronics and Telecommunication Engineering at Vishwakarma Institute of Information Technology, Pune, India. He has obtained his B.E. (Electronics and Telecommunication Engineering) from University of Pune and PhD (Communication Engineering) from Indian Institute of Technology, Delhi in 1984. He as 28 years of industrial, research and development experience and 13 years of teaching experience. His research interests are in signal processing, wireless and mobile communication systems. Prof Tavildar is Senior Member, IEEE USA, Fellow Member of IETE, India, Founder Member ICIET and Life Member of ISTE, India Rajesh Khanna is working as Professor in Electronics and Communication Engineering Department, Thapar University, Patiala, Punjab India. He has obtained his B.Sc (Electronics and Communication Engineering) from National Institute of Technology (formerly Regional Engineering College) Kurukshetra in 1988. He has completed Masters Degree from Indian Institute of Science, Bangalore, India in 1998 and PhD degree from Thapar University in 2006. He has professional experience of 10 years and teaching experience of 12 years. His research interests include wireless and mobile communication, antenna design, handover issues in heterogeneous networks and fractional Fourier transform based communication systems.
Sanket Lokhane has obtained Bachelor of Engineering (EnTC) from Pune University 2012. He has worked in industry as a ASIC SOC IC design and verification Engineer. Presently Employed at CERD lab as a Research Assistant at VIITPUNE.
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