4th Report Content

CHAPTER 1 INTRODUCTION

1.1 WIRELESS SENSOR NETWORKS (WSN) The vast advancement in semiconductor technology and in wireless communications have specifically give us the ability to produce small, low-cost sensor nodes that are connected to each other wirelessly. Today’s Wireless Sensor Networks (WSNs) are different from traditional networks. WSNs have low deployment and maintenance cost and is more rugged. WSN is a single purpose design and operate in harsh environment. Energy is the main constraint in designing these sensor nodes. WSN is a wireless network consists of spatially dispersed and dedicated autonomous devices or nodes that use sensors to monitor physical or environmental condition. The sensor transforms physical data into a form that would make it easier for the user to understand. Node is acting both as a sensor and a router. A usual WSN system is formed by combining the autonomous devices, or nodes with routers and a gateway. The dispersed measurement nodes communicate wirelessly to a central gateway, which provides a connection to the wired world where it can collect, process, analyze, and present measurement data. Here, routers are used to gain an additional communication link between end nodes and the gateway for extend distance and reliability in a wireless sensor network. The wireless sensor is networked and scalable, require very little power. It is also smart and software programmable, and also capable of fast data acquisition, reliable and accurate over the long term, but costs little to purchase and install, and requires approximately zero maintenance. Some hardware components in WSN are: (a) Embedded processor (b) Transceiver (c) Memory (d) Sensors (e) Power source WSNs are used in health monitoring, agriculture system, environmental monitoring, military surveillance and target tracking, traffic control, industrial sensing and also in infrastructure security. The block diagram of sensor node is shown in Fig.1.1.

1

Battery

Sensor

ADC

Microprocessor

RF Transceiver

Fig.1.1 Block diagram of sensor node in WSN 1.2 OBJECTIVE The Objectives to be fulfilled by the proposed dissertation work are: (a) To analyze different methods for the enhancement of network lifetime. (b) To propose a protocol which will reduces the energy consumption and increases lifetime of WSN. (c) To evaluate the performance of proposed protocol based on the parameters such as Duty cycle, Number of active neighbour nodes and Node density. 1.3 WORK PROCESS TO BE FOLLOWED To enhance network lifetime of WSN, the dissertation work has been segregated into the following steps: (a) Literature survey of WSN for basics [1, 3, 5, 7, 11, 20, 25]. (b) Mathematical analysis for enhancement of WSN parameters using different protocols [3, 4, 6, 8, 9, 10, 19, 24, 25]. (c) Identification of parameters affecting the network lifetime [2, 5, 6, 9, 12, 14, 15, 16, 21, 23, 25]. (d) Study of MATLAB using Math-works (e) Implementation of mathematical equations for duty cycle and network coding after defining the variables in MATLAB [19]. (f) Testing and verification of codes for duty cycle and network coding with random example. (g) Execution of assembled codes for all parameters using MATLAB [19]. (h) Simulation result of existing data as per base paper [19]. (i) Comparison of results obtained using MATLAB and results already published by the authors of base paper [19].

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1.4 REPORT ORGANISATION The report is organized into five chapters. The chapter 1 has given an introduction of WSN including the hardware components used in WSN and the areas where WSNs are used. Also the objective about the work and work process to be followed are discussed. Chapter 2 give brief description about literature survey of the concerned topic. This section discussed about the different techniques that were used earlier to enhance the network lifetime and how the evolution took place in the new era. Chapter 3 describes about the proposed technique known as adaptive duty cycle and network coding that works on the queue management process based on the incoming traffic rate and predefined threshold value. Chapter 4 describes about the experimental results. The results are obtained using MATLAB tool and further results are discussed and compared on the basis of input parameters. Chapter 5 describes about the overall summary of WSN, results and future advancement in the field of WSN. Lastly some of the references are listed used for this work.

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CHAPTER 2 LITERATURE SURVEY

2.1 BACKGROUND Ian F. Akyildiz et.al, (2002) [1] explained the basics related to WSN such as sensor network communication architecture, design factors, sensor network topologies, environment, trans-media etc. A WSN is a wireless network consists of spatially dispersed and dedicated autonomous devices that use sensors to monitor physical or environmental conditions. A usual WSN is formed by combining nodes with routers and a gateway. Some hardware components in WSN are embedded processor, transceiver, memory, sensors and power source. Few design factors are fault tolerance, scalability and production cost etc.

The problem in WSN is to route the data effectively. Jae-Hwang Chang et.al, (2004) [2] formulated the routing problem as a linear problem and proposed the shortest path algorithm. The goal was to enhance the lifetime of network. There are two models to generate the information. One considers the constant rate and another considers an arbitrary process. The parameter they considered is residual energy and simulation results showed the increased network lifetime. Chih Fan Hsin et.al, (2005) [3] discussed about partial clustering which is a generalize method of clustering. Comparison had also been done between partial clustering and standard clustering. Low energy consumption and good connectivity are the two main objectives in WSN. Partial clustering has a lower duty cycle and provides a better flexibility in the trade-off between energy efficiency and connectivity. In it network is also divided into cells and further each cell into sub areas. They considered the parameters such as death time, control data and number of nodes. Dongsook kim et.al, (2005) [4] discussed about asymptotic connectivity of a low duty cycled wireless sensor networks. Under this scheme, sensor nodes are made randomly duty cycled having fixed active probability. The necessary and sufficient conditions are also obtained to maintain the connectivity as the number of nodes increases to infinity. Two problems associated with duty cycle are loss of sensing coverage and los of network connectivity. To avoid this, asymptotic connectivity came into picture. A network is said to be asymptotically connected if there is a path having active nodes between two neighbouring active nodes as node density reaches to infinity. 4

Muralidhar Medidi et.al, (2007) [5] proposed differential duty cycle approach in which different duty cycles are assigned to nodes at different distances from Base Station (BS). Comparison was done between end to end delay of uniform and differential duty cycle and fully active MAC in two scenarios. Under this approach nodes are divided into different coronas having different traffic related energy consumption. It balances the energy consumption which leads to increased lifetime. The measurement parameters which they considered are end-to-end delay and energy consumption. Simulation results had shown that differential duty cycle approach extended the lifetime of network as compared to uniform duty cycle. Feng Wang et.al, (2008) [6] focussed on broadcast problem for large scale low duty cycle WSN. There is large no. of sensor nodes work in low duty cycle. Nodes are turn up and down before and during the broadcast process to reduce the energy consumption. They further proposed n adaptive algorithm which schedules message forwarding and find out lower bounds for time and messages costs. Parameters used were no. of messages forwarded during broadcast (message cost) and no. of nodes receiving messages (time costs). This technique carried out an efficient broadcast service with low delay and a reliable communication. Wooguil Pak at.al, (2008) [7] proposed an optimal duty cycle allocation algorithm for tier based anycast protocol. They used the concept of sub-tiering. An ID (identification no.) is assigned to each and every node in network according to distance from sink node. After getting tier ID, each node sends a data packet to sink and it starts to get ON and OFF periodically to save energy consumption. The measurement parameters are packet transmission rate, normalized energy consumption and no. of hopes. Simulation results had shown that lifetime increased by 30% compared to the original tier-based scheme. Qinghua Wang et.al, (2009) [8] analysed the effect of bottleneck zone (area near sink) on the performance of network and the performance bound in terms of network lifetime has been estimated. They also identified the bottleneck zone in an energy constrained WSN. A main disadvantage of WSN is energy constraint. The nodes sometimes fail to work because of exhausted energy. Nodes near the sink have high energy consumption than those further away due to which nodes near the sink fail earlier which degrades the performance of network. Nagajothy. M et.al, (2009) [9] proposed network coding as a power minimization technique for WSN in which XORing of the packets was performed. In network coding, the number of transmission required to communicate given information across the network has been minimized due to which energy consumption reduced which leads to increased lifetime. Some of the advantages of network coding are bandwidth, power efficiency, computational efficiency, robustness to network dynamics, high bit rate, less latency.

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Giuseppe Anastasi et.al, (2009) [10] proposed an Adaptive Staggered Sleep Protocol (ASLEEP). It is an independent sleep/wakeup protocol working above MAC layer. It needs a continuous co-ordination among nodes for maintaining the network wide sleep schedule. Adaptive schemes are complex than non-adaptive schemes. Under ASLLEP protocol, a sleep schedule is defined by communication period and talk interval of each individual parent node. Parameters which they considered are message latency, average latency and average delivery ratio. Simulation results had shown that ASLEEP protocol reduced the energy consumption which leads to increased lifetime. It had also reduced the message latency and increased the delivery ratio. Osameh M. Al-Kofahi et.al, (2009) [11] focussed on the problem of survivability of many-to-one flows in wireless networks such as wireless mesh networks (WMNs) and WSNs. They introduced a network coding-based protection technique to overcome the deficiencies of the previously used traditional networks. The process of decoding at the sink and the effect of proposed scheme on network performance was also discussed. The protection schemes are of two types: proactive protection and reactive protection. The parameter on which they worked is number of time slots. Scheduling algorithm showed the increased lifetime. Soobin Lee et.al, (2010) [12] focussed on Data Aggregation (DA) scheme in cluster based network. The network lifetime bound was also obtained. The effect of number of clusters and spatial was also taken into consideration. DA is used to remove the Energy Hole problem. In cluster based network, nodes transmit its data to Cluster Heads (CH’s). CH’s after compressing the data send it to sink. Parameters used were number of clusters and degree of spatial correlation. Energy balancing is done by rotating periodically the CH’s due to which lifetime had increased. Yun Li et.al, (2011) [13] presented one of the best clustering-based Low Energy Adaptive Clustering Hierarchy (LEACH) routing protocol. Clustering-based routing used the information aggregation mechanism. LEACH is simple in structure and also efficient. Under this protocol, whole network is divided into several clusters and run time is further partitioned into many rounds. In each round a Cluster Head (CH) is selected among the nodes on the basis of predefined criterion. After it all nodes send its data to CH which aggregate and compress the data and send it to Base Station (BS). All the nodes have same probability to become CH due to which nodes consume energy in a balanced way so as to enhance the lifetime. Parameters used in this paper are number of cluster heads and number of frames. Xiao Y. Wang et.al, (2011) [14] presented a Pulse Coupled Oscillator (PCO) system which is robust and scalable synchronization scheme for Impulse-Radio UWB (IR) network. They also discussed about practical implementation issues related to PCO’s. This system is created with low cost and less complex components having good synchronization performance. In PCO system, radios were synchronized automatically. Parameters used were transmission range, blackout-time and coupling strength. By reducing the power consumption of each node, self-synchronizing network prolonged the network lifetime. 6

Peng Guo et.al, (2012) [15] focussed on critical event monitoring. For the reduction in delay of alarm broadcasting from any sensor node, a novel sleep scheduling method was introduced. It followed the level-by-level offset based wake-up pattern The main aim is to minimize the broadcasting delay and energy consumption. The delay was reduced by minimizing the time consumed in waiting during broadcasting. Under this method, broadcasting of a message had done in two phases: one is uplink and second is downlink. Parameters which they considered are transmission delay, broadcasting delay. Jian Lin et.al, (2012) [16] proposed scheduling cooperative transmission MAC protocol (SCT-MAC) for multi-hop WSN to prolong the network lifetime. Further, a distributed duty cycle scheduling algorithm was also introduced to wake the nodes on demand. On-demand wake up decreased the contention over adjacent flows. SCT-MAC is a semi-synchronized duty cycle MAC protocol in which each sensor node is active at the start of its direct parent and two-hop parent node. The measurement Parameter was delivery ratio. Using SCT-MAC protocol the lifetime of network had enhanced by approximately 100% as compared to DW-MAC. Sanam Shirazi Beheshtiha et.al, (2012) [17] introduced an Opportunistic Routing with Adaptive Harvesting-Aware Duty Cycling algorithm (OR-HAD). The candidates or nodes are prioritized based on their zone and residual energy. Under this algorithm, to reduce the coordination delay nodes used a coordination message instead of original data packet. Energy model had also been made for the exchange of coordination message. Goodput and efficiency are the Parameters used in this paper. Experimental results had shown that OR-HAD has high goodput and efficiency. Sang H. Kang et.al, (2012) [18] introduced a distributed CH selection algorithm on the basis of distances from sensors to base station that balances the energy consumption. By using the minimum and maximum of the distances to the BS, a CH selection algorithm is developed. The parameters used in this paper are number of nodes and number of hops and it is concluded that this algorithm increased the network lifetime. Generally, duty cycle approach produces some latency in delivery of data. So to balance latency and energy consumption, an energy-efficient and delay-tolerant cooperative transmission algorithm (EDTCT) was proposed in Yu-Wang et.al, (2013) [19]. Under EDTCT, range extension property of cooperative communication had been exploited. This algorithm has two procedures: transmission modes determination procedure and relay selection procedure. Former procedure concerned with latency and latter one concerned with energy consumption. Parameters they considered were sleep latency and energy consumption. A Two-hop geographic node-disjoint multipath routing algorithm called TPGF Plus had proposed in Guangjie Han et.al, (2013) [20]. In TPGF Plus, a node that wants to communicate chooses its next hop node which is nearer to BS among all 1-hop and 2-hop neighbour node. This technique had two phases. One phase is responsible for guaranteed 7

routing path and another phase deals with finding the shortest path having least number of hops. Parameters used were number of paths and balanced energy. They had shown that TPGF Plus had more average number of paths. Jenq-Shiou Leu et.al, (2013) [21] introduced Regional Energy-Aware Clustering with Isolated Nodes method (REAC-IN). The process of selecting the CH was improved by REAC-IN which also helped in solving the problem of node isolation. CH’s are selected on the basis of weight. Weight is calculated by the residual energy and regional average energy of all sensors in each cluster. These two energies are calculated to determine whether the isolated node is sending the data to CH in previous round or to sink. They had shown the increased lifetime with more stability in their results. Rashmi Ranjan Rout et.al, (2013) [22] introduced a new scheme by combining the duty cycle and network coding to reduce the energy hole problem occurred in bottleneck zone (area near the sink). Network coding is a technique used to encode received data packets. The lifetime upper bounds were also calculated using duty cycle, combination of duty cycle and network coding. Failure of nodes inside bottleneck zone leads to wastage of network energy. This problem is overcome by network coding which decreases the number of transmission channel by reducing the number of transmission. The parameters used in this paper are Packet Delivery Ratio (PDR) and Packet Latency (PL). Simulation results had shown that the lifetime of the network had increased by 2.5% to 9.5%. The main important resource in battery powered WSN is energy that is sometimes ignored in prior multicast works. For real-time WSN, a novel energy efficient multicast protocol was introduced in Jianliang Gao et.al, (2013) [23]. They also introduced the virtual multicast sector which divides the region based on the distribution of multicast destinations. To minimize the number of hops in multicast protocol, a multicast tree was also designed. The process of data dissemination (distribution) to each and every member in the multicast group within the desired time deadline is known as real-time multicasting. Multicast refers to a transmission method used to disseminate the data in WSN applications. Basically data dissemination protocols are of three kinds: unicast, multicast and broadcast. The most common broadcast protocol used for the dissemination of commands is flooding. They considered the parameters such as number of hops and critical distance. According to the simulation results, the multicast protocol is an energy efficient protocol for real time WSN. Heejung Byun et.al, (2013) [24] proposed a control-based approach to the duty cycle adaptation for wireless sensor networks. The proposed method controls the duty cycle through the queue management to achieve high-performance under variable traffic rates. A feedback controller is designed which adapts the sleep time to the traffic change dynamically by constraining the queue length at a predetermined value. In addition, an efficient synchronization scheme was also proposed using an active pattern.. The simulation results showed that the proposed method outperforms existing schemes by achieving more power savings while minimizing the delay. 8

Different types of routing protocols and kinds of WSN had been introduced in Padmavati et.al, (2014) [25]. They discussed that sensor can be deployed on land, underground and underwater also. In static WSN, nodes remain fixed if once deployed. In mobile WSN, sensor nodes are movable and can interact with physical environment. Under terrestrial WSN, nodes are placed in a particular area either in a planned manner or in an Ad-hoc way. In underwater WSN, nodes deployed underwater. Under multimedia WSN, sensor nodes monitor and track the event in the form of multimedia data such as video, audio, and image. Kashif Saghar et.al, (2014) [26] had proposed robust analyzed protocol for WSN deployment called RAEED. Finite state model had also been introduced by them. RAEED removed the black hole attack problem occurred in WSN. Black hole attack is a Denial of Service (DOS) attack. In DOS attack, malicious node enters in the network and prevents the flow of data from source to sink. Parameters which they considered are average number of nodes and percentage of blocked nodes. Experimental results had shown that RAEED is more robust and also immune from black hole attack. An Energy- Balanced Routing Method based on Forward-Aware-Factor (FAFEBRM) had been introduced in Deg Zhang et.al, (2014) [27]. On the basis of link weight and forward energy density the next-hop node is selected. Comparison was also done between FAF-EBRM and LEACH. Under this method, transmission power of nodes varies according to distance to receiver. The parameters they considered are Energy Balanced Facto (EBF), number of last surviving nodes, Function Lifetime (FL) and Packet Reception Ratio (PRR). Simulation results had shown that FAF-EBRM has better performance in terms of energy consumption and lifetime as compared to LEACH. Andrea Castagnetti et.al, (2014) [28] focussed on Global Power Management (GPM) to reduce the energy consumption. By using an original predictive transmission power control, high packet delivery was obtained. A GPM system is a combination of duty cycle and transmission power control. It is also a unified and efficient adaptation strategy. Controlling of transmission power is very important in WSN. Both power consumption and interference were reduced by choosing an optimal transmission power level. The measurement parameter used was PRR. It had been observed that GPM system is approximately 15% more energy efficient than a fixed transmission power system. It had also high energy gain. Mohamed Amine et.al, (2014) [29] proposed different types of transport protocols or congestion protocols to remove the congestion and contention problem. Transport protocols have a great role in improving the reliability and throughput of network. Each node has a buffer to store the packet. Packet lost due to overflow of buffer is known as buffer based congestion. Traffic can be controlled by an avoiding manner or reacting manner. The delivery of traffic can be event driven, continuous, query driven and hybrid driven. Parameters which they considered are network fairness and packet latency

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Gaurav Gulhane et.al, (2014) [30] presented secure and authentic multipath routing protocols AOMDV, LIEMRO for secured data transmission which leads to increased lifetime. Researchers also developed an attack detection technique. Security is one of the most fundamental features of WSN. It provides protected and authenticated communication between sensor nodes. The vital security services are authentication, secrecy, confidentiality, integrity, anonymity and availability. Mehdi Tarhani et.al, (2014) [31] focussed on Scalable Energy Efficient Clustering Hierarchy (SEECH) protocol in WSN. It chooses the CHs and relays separately according to nodes eligibilities so that higher degree nodes and lower degree nodes are selected as CHs and relays respectively. The parameters they considered are area, number of nodes, packet size and location of data sink. Simulation results had shown that for SEECH protocol, the lifetime of network is 10% better than TCAC and 41% better than LEACH. 2.2 COMPARISON OF VARIOUS METHODS For the purpose of lifetime enhancement various methods are there which are shown in Table 2.1. In this table different methods for the network lifetime enhancement are listed year wise and parameters which are used in various methods are also shown. These methods are used to maintain the connectivity, to reduce the contention, to reduce the energy consumption, to decrease the packet latency and to increase the packet delivery ratio which leads to increased network lifetime. Table 2.1 Comparison of various methods AUTHORS

YEAR

METHOD

PARAMETER S No parameter

COMMENTS

Ian F.Akyildiz[1]

2002

Jae-Hwang Chang[2]

2004

Just discussed basics about WSN Shortest path algorithm

Residual energy

Network lifetime had increased

Chih Fan Hsin[3]

2005

Partial Clustering

No parameter

Asymptotic connectivity

Energy

Flexibility between energy and connectivity Less energy consumption

Dongsook Kim[4]

2005

10

Suitable for unattended area

Muralidhar Medidi[5]

2007

Differential duty cycle

End-to-End delay

Lifetime had increased

Continued on page no. 11 Table 2.1 continued Feng Wang[6]

2008

Reliable broadcast service

No. of messages broadcasted

Wooguil Pak[7]

2008

Tier based anycast

Qinghua Wang[8]

2009

Bottleneck zone analysis

Packet transmission rate, No. of hopes Energy consumption

Nagajothy.M[ 9]

2009

Network coding

Energy consumption

Guiseppe Anastasi[10]

2009

Adaptive sleep

Message latency

Osameh M. Al-Kofahi[11]

2009

Time slots

Increased lifetime

Soobin Lee[12]

2010

Network coding-based protection technique Data aggregation

No. of clusters

Balanced energy

Yun Li[13]

2011

LEACH protocol

Balanced Energy

Xiao Y. Wang[14]

2011

Pulse coupled oscillator

Peng Guo[15]

2012

Sleep scheduling

No. of cluster heads and no. of frames Transmission range, blackout time Transmission delay

11

Less delay and reliable communicati on Network lifetime increased by 30% Nodes near sink consumes more energy No. of transmission channel reduced Less energy consumption

Enhanced network lifetime Less delay

2012

SCT-MAC

Sanam Shirazi Beheshtiha [17]

2012

OR-HAD

Sang H. Kang[18]

2012

Yu-Wang[19]

2013

Distributed CH selection algorithm EDTCT

No. of nodes and no. of hops Sleep latency

Guangjie Han[20]

2013

Jenq-Shiou Leu

2013

REAC-IN

No. of nodes, no. of packets

Rashmi Ranjan Rout[22]

2013

Duty cycle with network coding

Jianliang Gao[23]

2013

Energy efficient multicast protocol

Packet delivery ratio (PDR), packet latency (PL) No. of hops and critical distance

Heejung Byun[24]

2013

No. of nodes

Padmavati[2 5]

2014

Queue management process Types of WSN

No parameter

No comment

Kashif saghar[26]

2014

RAEED

Average no. of nodes,

Removal of Black Hole

Jian Lin[16]

Packet transmission rate, no. of hops Message delay, efficiency

Network lifetime increased by 30% Enhanced throughput

Increased lifetime of network Increased network lifetime TPGF Plus No. of paths Balanced Energy Continued on page no. 12 Table 2.1 continued

[21]

12

Enhanced lifetime with more reliability Increased network lifetime by 2.5% to 9.5% Highly efficient in terms of energy Increased lifetime

Deg Zhang[27]

2014

FAF-EBRM

Andrea Castagnetti[ 28]

2014

Mohamed Amine Kafi[29]

2014

Global power managemen t Congestion control protocols

Gaurav Gulhane[30]

2014

Security system

Mehdi Tarhani[31]

2014

SEECH

percentage of nodes blocked EBF, FL, PRR

PRR

PL, network efficiency, received throughput No. of hops and nodes No. of nodes and packet size

CHAPTER 3 PROPOSED WORK

3.1 TECHNIQUE USED

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Attack problem FAF-EBRM has better performance over LEACH Highly energy efficient system Reduction of traffic

Confidentiali ty SEECH had 10% better lifetime than TCAC

To enhance the network lifetime there are various methods which are present in existing literature survey. Each of such methods produced results quantitatively as well as qualitatively. There are various parameters which effect the lifetime of network. Some of those parameters are PDR, PL, energy consumption, no. of nodes, no. of clusters, no. of hops, throughput. Various methods are shown in Fig.3.1

Network Lifetime Enhancement Techniques Partial clustering

LEACH

Asymptotic

PCO

Differential duty cycle scheduling

Sleep

RBS

SCT-MAC

Tier based anycast harvesting duty cycle

Adaptive

Network coding

EDTCT

Adaptive Sleep multipath routing

Geographic

Cluster based network

REAC-IN

Duty cycle with network coding FAF-EBRM

RAEED GPM

Congestion control protocol

Security

system Fig.3.1 Various techniques for network lifetime enhancement

3.2 PROPOSED WORK

There are various techniques for the enhancement of network lifetime. By considering these techniques an algorithm is proposed which enhances the network lifetime efficiently. The aim is to implement a new technique called Adaptive duty cycle with network coding using the same input, output and general parameters that are used in base paper. The proposed technique controls the duty cycle through the queue management 14

to achieve high-performance under variable traffic rates. To have high energy efficiency while minimizing the delay, a feedback controller will be designed. It adapts the sleep time to the dynamically changed traffic by constraining the queue length at a predetermined value.

The trajectories of the queue (the queue length and its changing trends) will be used as an implicit indicator of network status, such as traffic load, route depth. Based on the queue length and its variations, a dynamic duty cycle control scheme will be proposed to meet time-varying traffic loads by constraining the queue length at a predetermined threshold. The proposed controller is supposed to adjust the sleep time so that the queue length at the steady state is equal to the predetermined queue threshold. Specifically, the sleep interval time increases linearly as the queue length becomes smaller than the queue threshold. Meanwhile, the sleep interval time decreases as the forward difference of queue length becomes larger than zero because the increased forward difference of queue length induces a longer latency The queue threshold can be set according to the application requirement. When the queue threshold is low, a node increases the duty cycle by adding active periods, resulting in low delay. On contrary, as the queue threshold becomes larger, the delay increases because the proposed controller increases the sleep time to buffer the packets until the queue length reaches the queue threshold. The diagram of proposed work is shown in Fig. 3.2 and the comparison of base paper and proposed method is shown in Table 3.1.

Fig. 3.2 Node architecture of proposed work Table 3.1 Comparison of base paper and proposed method PARAMETERS

DUTY CYCLE (BASE PAPER[22])

15

WITH DUTY CYCLE AND NETWORK CODING (BASE PAPER[22])

PROPOSED WORK

Coding Layer

Not Present

Present

Lifetime

Decrease

Increase

Packet Delivery Ratio

20% with 0.015node density

60% with 0.02 node density

CHAPTER 4

16

Adaptive Duty Cycle with Network Coding To increase by 1% to 5% To increase with same node density

EXPERIMENTAL RESULTS

4.1 RESULTS AND DISCUSSION In bottleneck zone, the total energy consumption is mainly due to three reasons: (a) To relay the bits those are received from outside of the bottleneck zone (E1GD). (b) To sense the data bits inside the bottleneck zone (E2GD). (c) To relay the data bits those are generated inside the bottleneck zone (E3GD). 4.1.1 Network lifetime using duty cycle The total energy consumption inside the bottleneck zone in time t for a p duty cycle can be calculated as: ED = E1GD + E2GD + E3GD + (1-p) × t×N× (B/A)×Esleep

(4.1)

ED = [(m+1)/2]N×p×rs×t× (A-B)/A[α1(n/n-1)(D/dm)] + N×p × (B/A) ×rs×t× es + p(N/A) α ¿ ❑ ×rs×t ¿ 1× (n/n-1) × (x/dm)-α12] ×dS + (1-p) ×t×N(B/A) ×Esleep (4.2)

∬ B

The lifetime of a WSN is significantly depended on the energy consumption at the node level. Let Eb is the initial battery energy available at the each sensor node. In a network of N nodes, the energy reserve at the start is N×E b. The performance of a WSN strictly depends on the failure statistics of the sensor nodes. The failure pattern of sensor nodes depends on the rate of depletion of energy. The network lifetime demands that the total energy consumption is no greater than the initial energy reserve in the network. The upper bound on network lifetime can be achieved when the total battery energy N×E b available in a WSN is depleted completely. The following inequality holds to estimate the upper-bound of the network lifetime for a duty cycle based WSN. ED ≤ [((N×B)/A)×Eb] = t ≤ (dm×B×Eb /Qx ) = Tu×D

(4.3)

Where TuD is the lifetime upper bound of WSN with duty cycle (p) and Qx is given by: ❑

Qx = p×α1× (n/(n-1)) ×rs× [D× (A-B) × ((m+1)/2) + (1-p) ×Esleep]

17

∬ x × dS ¿ B

B×dm× [p×rs× (es-α12)+ (4.4)

The amount of energy consumption is maximum when p=1 (i.e. all node active condition) and the lifetime minimizes in a WSN. The energy efficiency of the network increases with low duty cycle which enhances the lifetime of the network. The duty cycle varies from 1% to 10%. As the duty cycle increases (i.e. more number of nodes are in active state) the lifetime decreases in the network. In a WSN with duty cycle more than 10%, the network lifetime further decreases. For a dense WSN the duty cycle generally varies from 1% to 10%. The parameters used in base paper [22] are shown in Table 4.1. Table 4.1 Parameter settings PARAMETER Number of nodes(N) Area(A) Path loss exponent(n) α11 α12 α2 Esleep Eb α1 es Data bits(H) H No. of packets(k) Active neighbour nodes (m) Duty cycle(p) Lifetime in seconds

TYPE General General General General General General General General General General General General General Input

VALUES 1000 200×200 2 0.937 µJ per bit 0.787 µJ per bit 0.0172 µJ per bit 30 µJ per bit 25 KJ 0.33 µJ per bit 1 µJ per bit 960 bits 2 2 1,3,5,7, 9

Input Output

0.01 to 0.1 ---------

The analytic results for duty cycle WSN obtained by authors on PROWLER using Eqn.4.3 are shown in Fig.4.1 (a). But after performing the simulation on MATLAB, the graph of network lifetime in seconds versus duty cycle is obtained which is shown in Fig.4.1 (b). The graph is plotted for different values of m and p that uses the parameter settings given in Table 4.1. The es is taken as negligibly small. The area of the bottleneck zone is B which is πD2 while considering it a complete round surface. The value of rs is set ❑

as H/ ((A-B) ×N/A) where H is 960 bits and

∬ x dS B

= (2/3) ×πD3. The network lifetime

decreases when the value of m increases. The increase of the value of m suggests that more amount of energy has been consumed in the bottleneck zone for transmissions of the redundant bits. The duty cycle varies from 1% to 10%. As the duty cycle increases (i.e more no. of nodes are in active state) the lifetime decreases in the network. In a WSN with duty cycle more than 10%, the network lifetime further decreases. For a dense WSN the duty cycle generally varies from 1% to 10%. However in some special cases the duty cycle may be increased to fulfil the requirements of specific coverage and connectivity in a WSN 18

BASE PAPER RESULTS[22]

SIMULATED RESULTS OF BASE PAPER[22]

(a)

(b)

Fig.4.1 Network lifetime using duty cycle (a) Base paper results (b) Simulated results For m=1, the lifetime obtained is 8.18×108 seconds at p=0.01 and 7.06×108 seconds at p=0.1. For m=9, the lifetime obtained is 7.52×108 seconds at p=0.01 and 4.01×108 seconds at p=0.1. 4.1.2 Network lifetime by combining network coding and duty cycle The network lifetime has been estimated with a proposed network coding algorithm for a non-duty cycled WSN. Furthermore, network coding and random duty cycle have been combined to estimate the network lifetime in a duty cycled WSN. Here, the lifetime upper bounds have been derived while considering a fraction of total traffic flows through the network coder nodes in the bottleneck zone. A network coding layer containing network coder nodes has been introduced around the Sink. The network coding layer is the most overloaded region (i.e. vulnerable region) of the bottleneck zone. So, reduction of energy consumption of the coding layer leads to higher network lifetime. A group of vulnerable nodes (i.e. the nodes which are nearest to the Sink and deplete their energy quickly) in the bottleneck zone transmits using network coding based communication. The other group of nodes in the bottleneck zone acts as simple relay nodes. These relay nodes help the Sink to decode the encoded packets. The upper-bound of the network lifetime in duty cycle based WSN with the proposed network coding approach, TuNCD, can be derived as follows: 19

ENCD = E1NCD + E2NCD + E3NCD + (1−p) ×t×N× (B/A) ×Esleep ≤ N× (B/A) ×Eb (4.5) = [(m+1)/2] ×p×N×rs×t×α1× ((A−B)/A(n−1)) × (D/dm)(1+k(h−1)/kh) + N(B/A) ×t×p×rs×es ❑ n x α1× × −α 12 dS + (1−p)t×N× (B/A) ×E ≤ (NB)/A×E ⇒ + p(N/A)rst ∬ sleep b n−1 dm B

( ( )( )

)

t ≤ (dm×B×Eb)/ Qδ = TuNCD

(4.6)

Where Qδ is given by ❑

Qδ = p×rs×α1(n/n-1)[(m+1/2) ×D× (A-B)((1+k(h-1))/(kh))+ α12)+(1-p)Esleep]

∬ x × dS B

] + B×dm[p×rs(es(4.7)

The analytical results obtained by authors on PROWLER using the combination of network coding and duty cycle are shown in Fig.4.2 (a) But after performing the simulation on MATLAB, the graph of network lifetime in seconds versus duty cycle is obtained which is shown in Fig.4.2 (b). The graph is plotted for different values of m and p by using Eqn.4.6 that uses the parameter settings given in Table 4.1. The value of k is set as 2 and the parameter h is set as 2 (i.e. the upper bound of lifetime for 50% of the network traffic through the network coder nodes). The duty cycle and the lifetime are shown in the X-axis and Y-axis respectively. As the duty cycle p increases, the lifetime decreases because more traffic flow in the WSN. The network lifetime decreases when the value of m increases. However, the lifetime in this case is found to be more than the duty cycled WSN without network coding. The packet processing procedure of a node in the network coding layer of the bottleneck zone is as follows: Each node in the network coding layer maintains a received queue (RecvQueue) and a sensed queue (SensQueue). On receiving a packet Pi, a node put the packet in RecvQueue(Pi). If the packet is already processed by the node than it is discarded, otherwise the node processes the packet further. The node check its role from EncoderNodeSet, whether it is an encoder or a simple relay node. If the packet is a native On successfully creating an encoding packet, the node transmit the coded packet to the Sink. The processed packet is inserted into the forwarding set ForwardSet which stores the forwarded packets and help in restricting further redundant transmissions. However, the received packet Pi is already an encoded packet, it is discarded by the node. Furthermore, if the node is not an encoder, it acts as a simple relay and transmits the received packet Pi to the Sink. The Sink node receives native packets from the simple relay nodes and coded packets from the network coder nodes. The intermediate nodes encode and decode packets. The decoding procedure is performed only at the Sink which processes all the gathered data in WSN. The Sink maintains a pool of packets, in which it stores each, received native packets. When the Sink receives an encoded packet consisting of k native packets, the Sink retrieves the corresponding native packets one by one from the pool of packets. The Sink 20

XOR’s the (k−1) native packets with the received coded packet to retrieve the missing packet which is totally lost or received with error at the Sink. BASE PAPER[22]

SIMULATED RESULTS OF BASE PAPER[22]

(a)

(b)

Fig.4.2 Network lifetime by combining network coding and duty cycle (a) Base paper results (b) Simulated results For m=1, lifetime obtained is 8.23×108 seconds at p=0.01 and 7.41×108 seconds at p=0.1. For m=9, lifetime obtained is 7.71×108 seconds at p=0.01 and 4.64×108 seconds at p=0.1. 4.2 SIMULATED RESULTS OF PROPOSED WORK A new technique is implemented called Adaptive duty cycle with network coding that controls the duty cycle through the queue management to achieve highperformance under variable traffic rates. To have high energy efficiency while minimizing the delay, a feedback controller will be designed. It adapts the sleep time to the dynamically changed traffic by constraining the queue length at a predetermined value. Based on the queue length and its variations, a dynamic duty cycle control scheme will be proposed to meet time-varying traffic loads by constraining the queue length at a predetermined threshold. The proposed controller is supposed to adjust the sleep time so that the queue length at the steady state is equal to the predetermined queue threshold. 21

Specifically, the sleep interval time increases linearly as the queue length becomes smaller than the queue threshold. While implementing the proposed technique the input, output and general parameters are used. 4.2.1 Network lifetime duty cycle In Eqn.4.3, the values of all the parameters are set according to the Table 4.1 and the graph and numerical values obtained after performing simulation are shown in Fig.4.3. The plotted graph is in between network lifetime and duty cycle. Again it is observed that as the value of m and p increases the network lifetime decreases.

Fig.4.3 Proposed work result of network lifetime using duty cycle For m=1, the obtained lifetime is 8.27×108 seconds at p=0.01 and 7.78×108 seconds at p=0.1. For m=9, the obtained lifetime is 7.84×108 seconds at p=0.01 and 5.15×08 seconds at p=0.1. 4.4 COMPARISON OF SIMULATED BASE PAPER AND PROPOSED WORK RESULTS After performing the simulation by using proposed technique, it has been observed that the network lifetime is increased for duty cycled WSN. As the value of input 22

parameters i.e. m and p increases, the network lifetime decreases. The comparison of simulated base paper and the proposed work results for p=0.01are shown in Table 4.2. Table 4.2 Comparison of results RESULTS

BASE PAPER RESULTS[22]

PROPOSED VALUE

(Lifetime in seconds)

(Lifetime in seconds)

RESULTS ACHIEVED BY PROPOSED TECHNIQUE[24] (Lifetime in seconds)

m=1, p=0.01

m=9, p=0.01

m=1, p=0.01

m=9, p=0.01

m=1, p=0.01

m=9, p=0.01

DUTY CYCLE

8.18×108

7.52×108

8.22×108

7.65×108

8.27×108

7.84×108

NETWORK CODING AND DUTY CYCLE

8.23×108

7.71×108

8.30×108

7.78×108

TECHNIQUES

23

----------------

CHAPTER 5 CONCLUSION AND FUTURE SCOPE

5.1 CONCLUSION

Wireless sensor must be designed to meet a number of challenging requirements including extended lifetime in the face of energy constraints, robustness, scalability and autonomous operation. WSN are getting smaller and faster, increasing their potential applications in commercial, industrial and residential environments. A new technique is introduced known as Adaptive duty cycle that works on the queue management process. The input parameters used in proposed work are duty cycle and no. of active neighbour nodes. Simulation results reveal that the network lifetime has been increased for duty cycled WSN by using the proposed technique.

5.2 FUTURE SCOPE

As the wireless sensor network is under research, number of improvements can be done. Further sensor node network can be extended by adding more nodes. This would allow the development and testing of advanced network layer functions, such as multi-hop routing. Alternative energy sources can also be used to extend node battery life. It includes solar cells and rechargeable batteries. These systems could provide a long term, maintenance free, wireless monitoring solutions. 24

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