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International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) ________________________________________________________________________________________________ Comparative Study of Duty Cycle and Network Coding based Analysis of Lifetime for Wireless Sensor Networks 1 Shashikala A, 2Manoj P B PG Student1 , Assistant Professor2 Email: [email protected], [email protected] Abstract- Wireless Sensor Network (WSN) consists of autonomous sensor nodes which are equipped with limited battery and requires energy management for enhanced Network Lifetime. The area around the sink forms a bottleneck zone due to heavy traffic-flow will be in demand of more power which limits the network lifetime. This work attempts to enhance the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by considering an adaptive duty cycled WSN. In this paper, an efficient communication paradigm has been adopted in the bottleneck zone by combining adaptive duty cycle and network coding. Comparative studies carried out to estimate the upper bounds of the network lifetime by considering (i) Adaptive duty cycle, (ii) Network coding and (iii) Combinations of adaptive duty cycle and network coding. The node which adapts the sleep time to the traffic changes dynamically by considering the queue length at a predetermined value. Network coding is not simply relaying the packets of information they receive, the sensor nodes of a network take several packets and combine them together for transmission and applied in bottleneck zone. By applying the above techniques the overall lifetime of the node will eventually increases. Energy efficiency of the bottleneck zone increases because more volume of data will be transmitted to the sink with the same number of transmissions. A theoretical analysis and simulation results have been carried out to show the efficacy of the proposed approach in terms of network lifetime. Energy is a scarce resource in wireless sensor networks and conservation of energy has been the subject of extensive research. While a variety of solutions have been proposed, duty cycling and network coding have proven to be two of the most successful techniques in this field. In most applications of sensor networks, the sensing information is transported only to a sink node, where information that is helpful for users conveyed to users of the applications. The sensor nodes near a sink node suffer from the heavy traffic load imposed on them and their energy is depleted strongly. This phenomenon is called the energy hole problem. In a typical WSN, the network traffic converges at the Sink node S (Fig. 1). There is a large amount of data flow near the Sink. The area near the Sink is known as the bottleneck zone. Heavy traffic load imposes on the sensor nodes near the Sink node. The nodes in the bottleneck zone vacate their energy very quickly, referred as energy hole problem in WSN. Failure of such nodes inside the bottleneck zone leads to wastage of network energy, bandwidth and reduction of network reliability. The bottleneck zone needs special attention for reduction of traffic which improves the network lifetime of the whole WSN. Index terms- Duty cycle, network coding, network lifetime, Wireless sensor networks. I. INTRODUCTION Wireless Sensor Networks (WSNs) consist of distributed autonomous sensor node that can be deployed to monitor the inaccessible and accessible areas such as forest fire, deserts, air pollution monitoring, deep oceans water quality monitoring, glaciers. Each sensor network node generally equipped with a radio transceiver with an internal or external antenna ,memory Unit, micro controller, and each node having own battery with limited energy to reducing the power consumption of each node is a difficult task. Fig.1. Heavy traffic-flow and roles of sensor nodes in WSN. ________________________________________________________________________________________________ ISSN (Print): 2278-5140, Volume-3, Issue-2, 2014 6 International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) ________________________________________________________________________________________________ The all-node-active condition is not practical for energy constraint WSN. The senor nodes are saving the energy by active and sleep states. The total time of sensor nodes are active and dormant state is called duty cycle. The duty cycle depends on the network coverage and connectivity. Usually for a dense WSN the duty cycle of a node is kept very low. A duty cycle WSN can be loosely categorized into three main types: random duty-cycled WSN, co-ordinated duty cycled WSN, Adaptive duty-cycle WSN, in random duty -cycle the sensor nodes can be turned active or sleep state independently in random fashion. The random duty-cycle required, but the disadvantage of random duty cycled WSN will not go to the sleep state based on their network condition. It will be generating the heavy traffic. It will not use better utilization of bandwidth. In coordinated duty cycle the sensor node coordinates among themselves through the communication and message exchange However, it requires additional information exchange to broadcast the active sleep schedules of each node. It will generate the heavy traffic and overhead. This paper propose an adaptive duty cycle control mechanism based on the queue management with the aims of power saving and delay reduction. The proposed scheme does not need explicit state information from the neighboring nodes, but only uses the possessive queue length available at the node. The network condition or traffic variations changes implicitly occurs because the queue states having possibility or power of the network states. Using the queue length and its variations of a sensor node, it present a design of distributed duty cycle controller. Therefore the adaptive duty-cycled based WSN has been considered for its design. Specifically the problem of reduction of traffic in bottleneck zone has been considered. Fig 2.Adaptive duty cycle in a WSN. In this work, the primarily goal is to gain certain analytical understanding on the upper-bound of the network lifetime and comparative studies carried out to estimate upper bound of the network lifetime between adaptive duty cycle WSN without network coding and adaptive duty cycle WSN with network coding technique. A. Network Coding resilience to attacks and eavesdropping. Instead of simply relaying the packets of information they receive, the nodes of a network take several packets and combine them together for transmission. This can be used to attain the maximum possible information flow in a network. The intermediate nodes of a network can appropriately encode the incoming data packets before forwarding the coded packets to the next node reduces the traffic without increasing delay. The network coding theory started with R.Ahlswede, N. Cai, S. Y. R. Li, and R. Yeung seminal paper [4]. The encoding and decoding methods of network coding described below.. Fig 3.Encoding and Decoding operation of Sensor nodes. Encoding operation: A node that wants to transmit encoded packets, choose sequence of bits ‘b1’, ‘b2’ from nodes Ni (i=1,2,3….n).The output encoded packet is given by Y= Ni (b1⊕b2) Where i=1,2,3…..n (1) The encoded packets are transmitted with n coefficient in the network. Decoding Operation: A node receives encoded packets, and it will get bits either ‘b1’or ‘b2’ based on that it will performing decoding operation A source node A send the information to the destination F and G , the node A will be generating information ‘b1’and ‘b2’ and it should be multi casted . The node D receives the information of ‘b1’, ‘b2’ and it will perform XOR operation. The Destination F receiving the‘b1’bit and also get XOR result. It will again perform XOR operation b1⊕ (b1⊕b2 ) for getting b2 bit. The XOR Network coding, a special case of linear network coding [5], has been used. The fundamental idea of coding in intermediate nodes in network has been shown to have advantages in other scenarios such as minimizing network resources, network diagnosis and packet communication in wireless network. Network coding technique only needs a few linear operations and several bytes storage to reduce much power consumption, increasing the lifetime of sensor networks. Network coding is a technique which can be used to improve a network's throughput, efficiency and scalability, as well as ________________________________________________________________________________________________ ISSN (Print): 2278-5140, Volume-3, Issue-2, 2014 7 International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) ________________________________________________________________________________________________ II. LITERATURE SURVEY Duty cycle facilitates in reduction of energy consumption in a dense WSN. Furthermore, network coding technique has been drawn its attention for improvement of throughput, bandwidth and energy efficiency in resource constraint wireless networks. The Adaptive Duty cycled with queue management in WSN has been estimated by Heejung Byun ,Junglok Yu [2]. Q. Wang and T. Zhang [6] proposes that bottleneck zone in a sensor network is considered as the intersection area between the sensor deployment area and a bottleneck zone centered at the sink. In a sensor network deployment, the whole network relies on the nodes inside the bottleneck zone to relay messages. Estimation of upper bounds of the network lifetime through bottleneck zone analysis in (a) random duty cycled WSN (b) non-duty cycled WSN using network coding in the bottleneck zone (c) random duty-cycled WSN using network coding in the bottleneck zone has been done by R. R. R.R.Rout and S.K.Ghosh [1]. There have been studies on the network lifetime in WSNs. Y.Wu,S.M.Das,and R.Chandra.[3] presented routing protocols to aggressively exploit coding opportunities in the network. The basic idea is to route the flows in the network to a region where network coding can be performed. This improves the throughput, however it greatly affects the network lifetime. Concentrating large amounts of traffic to a small region in the network burdens the involved nodes. This routing approach can eventually lead to a network breakdown. Therefore, such an approach cannot be applied to energy constrained networks. The network lifetime upper bounds in a cluster based WSN has been estimated by S. Lee and S. H. Lee [7]. Bhardwaj et al. [9] and Q. Wang and T. Zhang [6] have derived upper bounds on network lifetime for a non-duty cycle based WSN. A duty cycle based broadcasting scheme with reliability has been proposed by F. Wang and J. Liu. [8]. A random duty cycle based WSN has been considered for dynamic coverage by C. F. Hsin and M. Liu. [10].A random linear network coding based scheme that provides packet-level capacity for both single unicast and single multicast connections have been proposed by D. Lun, M. Medard, R. Koetter, and M. Effros [11] for wireless networks. R.R.Rout and S.K.Ghosh. [1] have also presented a network coding based probabilistic routing scheme which provides gains of network coding in a WSN. The focus of the present work is to estimate the upper bounds of network lifetime in WSN, considering (i) adaptive duty cycle, (ii) network coding, and (iii) combinations of the duty cycle and network coding. A network coding based communication paradigm in the bottleneck zone has been proposed to reduce the traffic load which enhances the network lifetime. The theoretical analysis, simulation and performance analysis have been done to show the efficacy of the proposed approach. III. ESTIMATION OF NETWORK LIFETIME USING ADAPTIVE DUTY CYCLE In this section system model has been explained. Based on the system model, an energy consumption model for duty cycle based WSN has been developed. The upper bound of the network lifetime has been estimated and energy savings due to duty cycle has also been shown. A. System Model A system is considered with N sensor nodes scattered uniformly in area A. The area A with a bottleneck zone B with radius D is shown in Fig. 1. Adaptive Duty Cycled in WSN in Fig 2.All the N sensor nodes are duty cycle enabled . The nodes are named based on their roles in the network as shown in Fig. 1. The sensor nodes in the bottleneck zone are divided into two groups: relay sensor and network coder sensor nodes. The relay sensor nodes (R) transmit data, which are generated outside as well as inside the bottleneck zone. The network coder sensor nodes (N) encode the raw native data which are coming from outside the zone B before transmission. The sensor nodes outside the zone B are marked as I and L in Fig. 1. The leaf sensor nodes (L) periodically sense data and transmit them toward the Sink. The intermediate sensor nodes (I) relay the data in the direction of the Sink S. In the bottleneck zone, the relay nodes can communicate with the Sink using a multihop communication. However, the network coder nodes use a single hop to communicate with the Sink. The radius, D, should be at least equal to the maximum transmission range of a sensor node, so that the data generated outside the bottleneck zone can be relayed through the zone B. Each sensor node has a number of Received queue and sensed Queue attached to it, one or more to other nodes, more to the sink. On each sensor node the packets are arrived and depart except the Leaf (or) Terminal node and Sink node. The proposed approach is to dedicate the buffer at each node to a single FIFO queue. When the buffer occupancy exceeds a threshold the switch begins to the sensor node as an active state to do so until buffer occupancy falls below the threshold. If the buffer size below the threshold means the sensor node going to the dormant (sleep) state. B. Energy Consumption Model Using Adaptive duty cycle A sensor node consumes energy at different states, such as, sensing and generating data, transmitting, receiving and sleeping state. Energy savings are done at the node level through switching between active and sleep states. Energy consumption by a source node per second across a distance d with path loss exponent n is, ________________________________________________________________________________________________ ISSN (Print): 2278-5140, Volume-3, Issue-2, 2014 8 International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) ________________________________________________________________________________________________ Etx = Rd (α11 + α2dn) (2) where ‘Rd’ is the transceiver relay data rate, ‘α11’ is the energy consumed per bit by the transmitter electronics and ‘α2’ is the energy consumed per bit in the transmit op-amp. Moreover, the total energy consumption in time ‘t’ by a source node (leaf node) without acting as a relay (intermediate node) is, Es = t [p(rses + Etx) + (1 − p)Esleep] (3) where ‘Esleep’ is the sleep state energy consumption of a sensor node per second, ‘rs’ is the average sensing rate of each sensor node and it is same for all the nodes, ‘es’ is the energy consumption of a node to sense a bit, the probability ‘p’ is the average proportion of time ‘t’ that the sensor node devotes in active state. Thus, ‘p’ is the duty-cycle. A sensor node remains in the sleep state with probability (1- p) till time ‘t’. The energy consumption per second by an intermediate node which act as a relay is given by Etxr = Rd (α11 + α2 dn + α12) = Rd (α1 + α2dn) (4) where‘α12’ is the energy consumed by the sensor node to receive a bit. Total energy consumption till time ‘t’ by an intermediate (relay) node is ER = t [p(rses + Etxr) + (1 − p) Esleep] (5) TABLE I Simulation Parameters S.No 1 2 3 5 6 7 8 9 PARAMETERS Number of nodes(N) Area Bottleneck Zone radius(D) Path loss exponent(n) α11 α12 α2 Esleep Eb 10 Software 4 VALUES 103 200 x 200 m2 10 meters 2 0.937 μjoule per bit 0.787 μjoule per bit 0.0172 μjoule per bit 30 μjoule per second 25Kjoule MATLAB R2013a (8.1.0.604) IV. ESTIMATION OF NETWORK LIFETIME USING ADAPTIVE DUTY CYCLE AND NETWORK CODING region simply forward the data to sink. This relay node helps the Sink to decode the encoded packets. Whenever a node in the WSN, it will check the queue. Adaptive Duty cycling and the packet processing in the network coding layer of the bottleneck zone has been explained below. Each node in the network maintains the received queue (receiveQueue()) And a sensed queue (senseQueue()). On sensing a information a node put the packet in the senseQueue(Pj). On receiving a packet ‘Pi’ a node put the packet in the receiveQueue(Pi). The node check the queues length whether it is below the threshold or above. If the threshold is below means the node gone to sleep state otherwise It should be active state. If the received packet already processed by the node than it is discarded, otherwise node processes the packets further. The node check the role from the Nodeset(), whether it is relay node or network coder node. If the node is relay node in network coding layer it will forward the packet to the sink otherwise forward the packet to the network coding layer. The node is an encoder if the packet received non coded packet then it performs the XOR operation. On successfully generating encoded packets, the network coding node transmits the encoded packet to the Sink. The processed packets inserted into Transmitted set Transmitset(). This stores the transmitted packet and helps restricting further redundant transmission. However the received packet ‘Pi’ is already processes then it is discarded by node. Decoding packet at the sink : The sink node receive the packet from the relay nodes and encoded packets from the network coding node. The decoding procedure is executed at the sink which processes the gathered data in WSN. The sink maintains a packet storage in which it stores the relayed packets. And the sink receives an encoded packet containing of ‘k’ relayed packet, the sink retrieves the relayed packet one by one from the packet storage.The sink XORs the received encoded packet with the (k-1) native packets to retrieve the missing packet. V. SIMULATION RESULTS This section presents the simulation results of the proposed algorithms. The energy efficiency and improvement of lifetime of the proposed approach have been discussed. A. Packet transmission via Relay Node The Fig 4 shows that packet transmission via relay node. The relay sensor nodes transmit data which are generated outside as well as inside the bottleneck zone. The relay nodes simply forward the received data. In this section the network life time has been estimated with a proposed network coding algorithm for a adaptive duty cycled WSN. A network coding layer (refer Fig.1) containing network coder nodes has been introduced around the Sink. The network Coding layer is the most excessive load in bottleneck region. So, the reduction of energy consumption of the network coding layer leads to higher network life time. The group of relay nodes in the bottleneck ________________________________________________________________________________________________ ISSN (Print): 2278-5140, Volume-3, Issue-2, 2014 9 International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) ________________________________________________________________________________________________ network. It has been observed from Fig 6.that the network lifetime with adaptive duty cycle and network coding is more than using adaptive duty cycle. The improvement of network lifetime is due to the introduction of network coding nodes near the Sink. In Fig 7 energy consumption has been shown for adaptive duty cycle without network coding and with network coding. The per node energy consumption with adaptive duty cycle is more than a with adaptive duty cycle and network coding. Fig.4 Packet transmission via relay node. A. Packet transmission via Network Coder Node The Fig 5 shows that packet transmission via network coder node. Instead of simply relaying the packets of information they receive, the nodes of a network take several packets and combine them together for transmission. The incoming data packets before forwarding the coded packets to next node. This can be used to attain the maximum possible information flow in a network. Fig.6 Network Lifetime (a) with Adaptive Duty Cycle(TuD) and (b) with Adaptive Duty cycle and network coding (TuNCD). With the use of MATLAB ,in this paper have studied different parameters for two cases. Fig .6 and Fig.7 which uses the parameter settings given in Table I and Table II shows the comparison of simulation results. TABLE II S. No 1 2 Shows the Comparison of Simulation Results VALUES VALUES (for Adaptive (for PARAMETERS duty cycle) Adaptive (If duty cycle duty cycle p= 5) with Network Coding) (If duty cycle p= 5) Network Lifetime(Seconds) 0.2*e4 2.2*e4 Per node Energy Consumption(Mic 3.0*e5 1.5*e5 ro Joules) As the duty cycle increases from 1 to10 the number of transmissions and receptions in the network increases. Thus, the energy consumption of the nodes are also increases in the Fig.7 Per node energy consumption (a) with Adaptive Duty Cycle and (b)with Adaptive Duty cycle and network coding. VI. CONCULSION In a wireless sensor network the area near the sink forms the bottleneck zone where the traffic flow maximum, so reducing the power consumption. In this paper, propose adaptive duty cycle control for WSN and network coding. The proposed approach controls the Adaptive duty cycle through the queue management to achieve high performance under network condition changes. The sensor node save the energy by dynamically changing the sleep time based on the predetermined value of queue length. These results in minimum power consumption and faster adaptation to traffic changes. The proposed scheme only ________________________________________________________________________________________________ ISSN (Print): 2278-5140, Volume-3, Issue-2, 2014 10 International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) ________________________________________________________________________________________________ requires the local queue length for computing the duty cycle, which adds good scalability, improved efficiency to the system. In addition, propose a network coding approach which is used to improve the capacity of information flow with the better utilization of bandwidth in a multi hop communication. [4] R. Ahlswede, N. Cai, S. Y. R. Li, and R. Yeung, “Network information flow,” IEEE Trans. Inf. Theory, vol. 46, no. 4, pp. 1204–1216, July 2000. [5] [9] S.-Y. Li, R. W. Yeung, and N. Cai, “Linear network coding,” IEEE Trans. Inf. Theory, vol. 49, no. 2, pp. 371–381, 2003. [6] Q. Wang and T. Zhang, “Bottleneck zone analysis in energy constrained wireless sensor networks,” IEEE Communications Letter, Vol. 13, No. 6, pp. 423– 425, June 2009. [7] S. Lee and S. H. Lee, “Analysis of network lifetime in cluster-based sensor networks,” IEEE Commun. Lett., vol. 14, no. 10, pp. 900–902, 2010. [8] F. Wang and J. Liu, “RBS: a reliable broadcast service for large-scale low duty-cycled wireless sensor networks,” in Proc. 2008 IEEE ICC, pp. 2416–2420. [9] Heejung Byun, Junglok Yu, "Adaptive Duty Cycle Control with Queue Management in Wireless Sensor Networks,"IEEE Transactions on Mobile Computing, vol. 12, no. 6, pp. 1214-1224, June 2013, doi:10.1109/TMC.2012.102 M. Bhardwaj, T. Garnett, and A. Chandrakasan, “Upper bounds on the lifetime of sensor networks,” in Proc. 2001 IEEE ICC, pp. 785–790. [10] C. F. Hsin and M. Liu, “Randomly duty-cycled wireless sensor networks: dynamic of coverage,” IEEE Trans. Wireless Commun., vol. 5, no. 11, pp. 3182–3192, 2006. Y. Wu, S.M. Das, and R. Chandra, "Routing with a Markovian Metric to Promote Local Mixing,"' MSR Technical Report, Nov 2006. [11] D. Lun, M. Medard, R. Koetter, and M. Effros, “On coding for reliable communication over packet networks,” Physical Commun., vol. 1, pp. 3–20, 2008 Network coding is not simply relaying the packets of information they receive, the sensor nodes of a network take several packets and combine them together for transmission and applied in bottleneck zone. That the proposed algorithm improves significantly both energy efficiency and delay performance by adapting the duty cycle properly under network changes and derived performance figures using MATLAB. REFERENCES [1] [2] [3] R.R.Rout and S.K.Ghosh,"Enhancement of lifetime using duty cycle and Network Coding in wireless Sensor Network,"IEEE Transactions on Wireless Communications, Vol.12, No.2, pp.656-667, February 2013. ________________________________________________________________________________________________ ISSN (Print): 2278-5140, Volume-3, Issue-2, 2014 11