ENHANCEMENT OF LIFETIME USING DUTY CYCLE AND NETWORK CODING IN WIRELESS SENSOR NETWORKS

Transcription

1 ENHANCEMENT OF LIFETIME USING DUTY CYCLE AND NETWORK CODING IN WIRELESS SENSOR NETWORKS Dr.C.Kumar Charliepaul 1 G.Immanual Gnanadurai 2 Principal Assistant professor / CSE A.S.L Pauls College of Engg & Tech, Coimbatore. Dhaya College of Engineering, Madurai charliepaul1970@gmail.com imman8601@gmail.com Introduction: - A fundamental challenge in the design of Wireless Sensor Network (WSN) is to enhance the network lifetime. The area around the Sink forms a bottleneck zone due to heavy traffic-flow, which limits the network lifetime in WSN. This work attempts to improve the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by considering a duty cycled WSN. An efficient communication paradigm has been adopted in the bottleneck zone by combining duty cycle and network coding. Studies carried out to estimate the upper bounds of the network lifetime by considering Duty Cycle Network Coding and Duty Cycle and Network Coding. The sensor nodes in the bottleneck zone are divided into two groups Simple Relay Sensors Network Coder Sensors The relay nodes simply forward the received data, whereas, the network coder nodes transmit using the proposed network coding based algorithm. Energy efficiency of the bottleneck zone increases because more volume of data will be transmitted to the Sink with the same number of transmissions. This in-turn improves the overall lifetime of the network. In a typical WSN, the network traffic converges at the Sink node S. There is a significant amount of data flow near the Sink ref. Figure. 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 deplete 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 and reduction of network reliability. The bottleneck zone needs special attention for reduction of traffic which improves the network lifetime of the whole WSN. The all-node-active condition is not practical for energy constraint WSN. The sensor nodes save energy by switching between active and dormant (i.e. sleep) states. The ratio between the time during which a sensor node is in active state and the total time of active/dormant states is called duty cycle. Fig 1: Traffic flow, bottleneck zone and roles of sensors in a typical WSN. The duty cycle depends on the node density of the monitored area for better coverage and connectivity. Usually for a 427

2 dense WSN the duty cycle of a node is very low. A duty cycled WSN can be loosely categorized into two main types Random Duty-cycled WSN Co-ordinate Duty cycled WSN In the former, the sensor nodes are turned on and off independently in random fashion. In the later, the sensor nodes co-ordinate among themselves via communication and control message exchanges. They are potentially efficient for communication. However, it requires additional information exchange to disseminate the active/sleep schedule of each node. The random duty-cycled WSNs are simple to design as no additional overhead is required. In this work, the primarily goal is to gain certain analytical understanding on the upper-bound of the network lifetime. Therefore, the random duty cycle based WSN has been considered for its simplicity in design. Specifically, the problem of reduction of traffic in the bottleneck zone has been considered. The network coding technique improves the capacity of an information network with better utilization of bandwidth. In a multi-hop communication with network coding, the intermediate nodes of a network can appropriately encode the incoming data packets before forwarding the coded packets to the next node. The network coding technique also improves reliability of the network. NETWORK CODING Network coding is a technique which allows the intermediate nodes to encode data packets received from its neighboring nodes in a network. The encoding and decoding methods of linear network coding are described below. Encoding operation A node, that wants to transmit encoded packets, chooses a sequence of coefficients q = (q 1, q 2 q n ), called encoding vector, from GF (2 s ). A set of n packets G i (i = 1, 2, 3... n) that are received at a node are linearly encoded into a single output packet. The output encoded packet is given by The coded packets are transmitted with the n coefficients in the network. The encoding vector is used at the receiver to decode the encoded data packets. Decoding operation A receiver node solves a set of linear equations to retrieve the original packets from the received coded packets. The encoding vector q is received by the receiver sensor nodes with the encoded data. Let, a set (q 1, Y 1 )... (q m, Y m ) has been received by a node. The symbols Y j and q j denote the information symbol and the coding vector for the j th received packet respectively. A node solves the following set of linear equations with m equations and n unknowns for decoding operation. At least n linearly independent coded packets must be received by the recipients for proper decode of the original packets. The only unknown, G i, contains the original packets that are transmitted in the network. The n number of original packets can be retrieved by solving the linear system in equation after getting n linearly independent packets. The XOR network coding, a special case of linear network coding has been used in this work. The coded packets that are transmitted in the network are elements in GF (2) = {0, 1} and bitwise XOR in GF (2) is used as an operation. A fundamental challenge in the design of Wireless Sensor Network (WSN) is to enhance the network lifetime. The area around the Sink forms a bottleneck zone due to heavy traffic-flow, which limits the network lifetime in WSN. This work attempts to improve the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by 428

3 considering a duty cycled WSN. An efficient communication paradigm has been adopted in the bottleneck zone by combining Adaptive Duty Cycle and Linear Network Coding. The nodes in the bottleneck zone deplete 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 and reduction of network reliability. The bottleneck zone needs special attention for reduction of traffic which improves the network lifetime of the whole WSN.Wireless sensor Network Consist of distributed autonomous sensor node that can be deployed to monitor the inaccessible and accessible areas such as Forest fire deduction,deserts, Air pollution monitoring, Land slide deduction, Water Quality monitoring.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. Wireless sensor Network the node transmitting the data to the central gathering point (i.e. called as sink). There is a large amount of data flow near the sink so the nearer node s taking more power than the other node shown in Figure. The area nearer the central gathering point is called as bottleneck zone shown in Figure.The sensor nodes in the bottleneck zone deplete their energy very quickly. It is called as energy hole problem in WSN. Failure of sensor node in bottleneck region leads to wastage of network energy, bandwidth, and reduction network reliability. The bottleneck zone needs special cognizance for decrement of traffic which improves the network lifetime of the WSN. In WSNs the entire node in active condition is not practical for energy constraint. 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 crowded WSN the sleep time of a node is very low. Fig2 :Roles of Sensor in typical WSN. Fig3 :Bottleneck Zone in typical WSN. MODULES Modules of the proposed system is Adaptive Duty Cycle Linear Network Coding ADAPTIVE DUTY CYCLE We propose an adaptive duty cycle control mechanism based on the queue management with the aims of power saving and delay reduction shown in Figure. The proposed scheme does not need explicit state information from the neighboring nodes, but only uses the possessive queue length available at the node. 429

4 Decoding Operation A node receives encoded packets, and it will get bits either b 1 or b2 based on the bit. It will perform decoding operation b 2 = Y b 1 b 1 = Y b 2 The b1 and b2 using {0, 1} information s A node receives encoded packets, and it will get bits either b 1 or b 2 based on that it will perform decoding operation. A source node A send the information to the destination F and G, the node A will be generating information b 1 ={ } and b 2 ={ } and it should be multi casted shown in Figure. Fig 4 : Adaptive Duty Cycle in a typical WSN 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, we 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. LINEAR NETWORK CODING Network coding is a technique which allows the intermediate node to encode the packet before forwarding to the neighbor node. The encoding and decoding methods described below. Encoding Operation A node wants to transmit the encoded packets, choose a sequence of bits b 1, b 2 from Nodes N i (i =1, 2, 3 n). The output of encoding packet is given by Y= N i (b 1 b 2 ) Where i = 1, 2.n The encoded packets are transmitted with k coefficient in the network. Fig 5 : Encoding and Decoding operation of sensor node The node D receives the information of b 1, b 2 and it will perform XOR operation. The result will be { }. The Destination F receiving the b 1 bit and also get XOR result. It will again perform XOR operation b 1 (b 1 b 2) for getting b 2 bit. HARDWARE One major challenge in a WSN is to produce low cost and tiny sensor nodes. There are an increasing number of small companies producing WSN hardware and the commercial situation can be compared to home computing in the 1970s. Many of the nodes are still in the research and development stage, particularly their software. Also inherent to sensor network 430

5 adoption is the use of very low power methods for data acquisition. In many applications, a WSN communicates with over a LAN and WAN through a gateway. The Gateway acts as a bridge between the WSN and the other network. This enables data to be stored and processed by device with more resources, for example in a remotely located Server Computing. SOFTWARE Energy is the scarcest resource of WSN nodes, and it determines the lifetime of WSNs. WSNs are meant to be deployed in large numbers in various environments, including remote and hostile regions, where ad hoc communications are a key component. For this reason, algorithms and protocols need to address the following issues Lifetime maximization Robustness and fault tolerance Self-configuration Lifetime maximization: Energy/Power Consumption of the sensing device should be minimized and sensor nodes should be energy efficient since their limited energy resource determines their lifetime. To conserve power the node should shut off the radio power supply when not in use. OPERATING SYSTEMS Operating systems for wireless sensor network nodes are typically less complex than general-purpose operating systems. They more strongly resemble embedded systems, for two reasons. First, WSNs are typically deployed with a particular application in mind, rather than as a general platform. Second, a need for low costs and low power leads most wireless sensor nodes to have lowpower microcontrollers ensuring that mechanisms such as virtual memory are either unnecessary or too expensive to implement. It is therefore possible to use embedded operating systems such as ecos or uc/os for sensor networks. However, such operating systems are often designed with real-time properties. TinyOS is perhaps the first operating system specifically designed for WSNs. TinyOS is based on an event-driven programming model instead of multithreading. TinyOS programs are composed of event handlers and tasks with run-to-completion semantics. When an external event occurs, such as an incoming data packet or a sensor reading, TinyOS signals the appropriate event handler to handle the event. Event handlers can post tasks that are scheduled by the TinyOS kernel some time later. SIMULATION OF WSNs At present, agent-based modeling and simulation is the only paradigm which allows the simulation of complex behavior in the environments of wireless sensors (such as flocking). Agent-based simulation of wireless sensor and ad hoc networks is a relatively new paradigm. Agent-based modelling was originally based on social simulation. Network simulators like OPNET, NetSim, NS2 and OMNeT can be used to simulate a wireless sensor network. NS2 Introduction Network Simulator (Version 2), widely known as NS2, is simply an event driven simulation tool that has proved useful in studying the dynamic nature of communication networks. Simulation of wired as well as wireless network functions and protocols (e.g., routing algorithms, TCP, UDP) can be done using NS2. In general, NS2 provides users with a way of specifying such network protocols and simulating their corresponding behaviours. Basic Architecture NS2 provides users with executable command ns which take on input argument, the name of a Tcl simulation scripting file. Users are feeding the name of a Tcl simulation script (which sets up a simulation) 431

6 as an input argument of an NS2 executable command ns. In most cases, a simulation trace file is created, and is used to plot graph and/or to create animation shown in Figure. NS2 consists of two key languages: C++ and Object-oriented Tool Command Language (OTcl). While the C++ defines the internal mechanism (i.e., a backend) of the simulation objects, the OTcl sets up simulation by assembling and configureuring the objects as well as scheduling discrete events (i.e., a frontend). The C++ and the OTcl are linked together using TclCL. Mapped to a C++ object, variables in the OTcl domains are sometimes referred to as handles. Conceptually, a handle (e.g., n as a Node handle) is just a string (e.g., _o10) in the OTcl domain, and does not contain any functionality. Instead, the functionality (e.g., receiving a packet) is defined in the mapped C++ object (e.g., of class Connector). In the OTcl domain, a handle acts as a frontend which interacts with users and other OTcl objects. It may defines its own procedures and variables to facilitate the interaction. Note that the member procedures and variables in the OTcl domain are called instance procedures (instprocs) and instance variables (instvars), respectively. After simulation, NS2 outputs either text-based or animation-based simulation results. To interpret these results graphically and interactively, tools such as NAM (Network AniMator) and XGraph are used. terms the number of hops) is chosen throughout the connection. ns can simulate noisy links or even links that become disconnected. To simulate the disconnection of link between nodes $n1 and $n4 from time 1 to 4.5 $ns rtmodel-at 1.0 down $n1 $n4 $ns rtmodel-at 4.5 up $n1 $n4 Routing Example THROUGHPUT The system throughput or aggregate throughput is the sum of the data rates that are delivered to all terminals in a network. Throughput is essentially synonymous to digital bandwidth consumption. It can be analyzed mathematically by means of queuing theory, where the load in packets per time unit is denoted arrival rate λ, and the throughput in packets per time unit is denoted departure rate μ. Basic architecture of NS2. Routing and Network Dynamics Unicast Routing There are several routing possibilities over the internet. The simplest one is the static routing in which the shortest route (in We compared both Random Duty Cycle and Adaptive Duty Cycle. We got higher throughput in Adaptive Duty Cycle. JITTER Jitter is the undesired deviation from true periodicity of an assumed periodic signal in electronics and telecommunications, often in relation to a reference clock source. Jitter may be observed in characteristics such as the 432

7 frequency of successive pulses, the signal amplitude, or phase of periodic signals. Jitter is a significant, and usually undesired, factor in the design of almost all communications links (e.g., USB, PCI-e, SATA, OC-48). In clock recovery applications it is called timing jitter. Jitter can be quantified in the same terms as all time-varying signals, e.g., RMS, or peak-to-peak displacement. Also like other time-varying signals, jitter can be expressed in terms of spectral densityque error types encountered in digital communications; the other two being bit error and spurious packets caused due to noise. The following Figure shows that Comparisons of Number of packets dropped between Random Duty Cycle and Adaptive Duty Cycle Number of Packets Dropped for ADC vs. RDC y content). Jitter for ADC vs. RDC nc Jitter period is the interval between two times of maximum effect (or minimum effect) of a signal characteristic that varies regularly with time. Jitter frequency, the more commonly quoted Figure, is its inverse. ITU-T G.810 classifies jitter frequencies below 10 Hz as wander and frequencies at or above 10 Hz as jitter. Jitter may be caused by electromagnetic interference (EMI) and crosstalk with carriers of other signals. Jitter can cause a display monitor to flicker, affect the performance of processors in personal computers, introduce clicks or other undesired effects in audio signals, and loss of transmitted data between network devices. The amount of tolerable jitter depends on the affected application. PACKET LOSS Packet loss occurs when one or more packets of data travelling across a computer network fail to reach their destination. Packet loss is distinguished as one of the three main DELAY Network delay is an important design and performance characteristic of a computer network or telecommunications network. The delay of a network specifies how long it takes for a bit of data to travel across the network from one node or endpoint to another. It is typically measured in multiples or fractions of seconds. Delay may differ slightly, depending on the location of the specific pair of communicating nodes. Although users only care about the total delay of a network, engineers need to perform precise measurements. Thus, engineers usually report both the maximum and average delay, and they divide the delay into several parts Processing delay - time routers take to process the packet header Queuing delay - time the packet spends in routing queues Transmission delay - time it takes to push the packet's bits onto the link Propagation delay - time for a signal to reach its destination There is a certain minimum level of delay that will be experienced due to the time it takes to transmit a packet serially through a link. Onto this is added a more variable level of delay due to network congestion. IP network delays 433

8 can range from just a few milliseconds to several hundred milliseconds. International Journal On Engineering Technology and Sciences IJETS Delay for ADC vs. RDC CONTROL OVERHEAD Control overhead is any combination of excess or indirect computation time, memory, bandwidth, or other resources that are required to attain a particular goal. It is a special case of engineering overhead. Normalized Routing Overhead for ADC vs. RDC TOTAL ENERGY CONSUMPTION Consumption of electric energy is measured by W h (Watt x Hour). 1 W h = 3600 joule = calorie energy in one year. Electric/Electronics devices consume electric energy to generate desired output(i.e. light, heat, kinetic etc.), while its operation some part of energy are consumed in unintended output. see Electrical efficiency. AVERAGE ENERGY Average Energy Consumption defines as average amount of energy taken by the sensor node at the end of the data transmission for the entire network. Control Overhead for ADC vs. RDC NORMALIZE DROUTING OVERHEAD Normalized Routing Overhead is define as during the data transmission, some of the sensor nodes was going to sleep sate, so we need to find the shortest path for data transmission, It required to maintain a routing table for each time. It leads to routing overhead. The following Figure shows that Comparisons of Normalized Routing Overhead between Random Duty Cycle and Adaptive Duty Cycle Total Energy Consumption for ADC vs. RDC The following Figure shows that Comparisons of Average Energy Consumption between Random Duty Cycle and Adaptive Duty Cycle 434

9 Average Energy Consumption for ADC vs. RDC OVERALL RESIDUAL ENERGY Overall Residual Energy defines as amount of remaining energy present at the end of data transmission for the entire sensor network. AVERAGE RESIDUAL ENERGY Average Residual Energy defines as amount of remaining energy present at the end of data transmission for the entire sensor network. The following Figure shows that Comparisons of Average Residual Energy between Random Duty Cycle and Adaptive Duty Cycle FUTURE ENHANCEMENT In this letter, the bottleneck zone in an energy-constrained wireless sensor network is identified. Further, the influence of the bottleneck zone on the entire network s performance is analyzed by deducing the performance upper bounds limited by the available energy resources inside the bottleneck zone. Finally, the ways by which network deployment variables (e.g. the number of nodes) may affect the performance bounds are discussed. The acquired results give insights for the proper deployment of wireless sensor networks. Further, the performance upper bounds given in this letter can be used as benchmarks in the performance evaluation of protocols or algorithms developed for energy-constrained wireless sensor networks. Our future work includes the following: 1) jointly optimizing multiple network parameters with cluster based methods for the proposed cooperative scheme and 2) jointly designing Linear Network Coding algorithms with the proposed Adaptive Duty Cycle scheme for multiple sources to multiple sinks in Wireless Sensor Networks. Authors Biography: - Kumar Charlie Paul, Principal of A.S.L Pauls College of Engineering & Technology. Had did many National and International Conferences and published many papers in journals. He also guided many students for their Ph.D project works. Having more than 23 years of experience in teaching field. Overall Residual Energy for ADC vs. RDC Immanual Gnandurai, Assistant Professor/ CSE of Dhaya college of Engineering. Had did many National and International Conferences and published many papers in journals. He also guided many students for their UG and PG project works. Average Residual Energy for ADC vs. RDC 435