The Wireless Sensor Networks Characteristics

Published Date: 02 Nov 2017

ABSTRACT

We develop a new on-demand Multipath protocol called ad hoc on-demand Multipath distance vector (AOMDV). AOMDV is based on a prominent and well-studied on-demand single path protocol known as ad hoc on-demand distance vector (AODV). AOMDV extends the AODV protocol to discover multiple paths between the source and the destination in every route discovery. Multiple paths so computed are guaranteed to be loop-free and disjoint.

Whenever a traffic source needs a route to a destination, it initiates a route discovery by flooding a route request (RREQ) for the destination in the network and then waits for a route reply (RREP). When an intermediate node receives the first copy of a RREQ packet, it sets up a reverse path to the source using the previous hop of the RREQ as the next hop on the reverse path. In addition, if there is a valid route available for the destination, it unicasts a RREPback to the source via the reverse path; otherwise, it re-broadcasts the RREQ packet.

INTRODUCTION

Wireless Sensor Networks (WSNs) have emerged as research areas with an overwhelming effect on practical application developments. They permit fine grain observation of the ambient environment at an economical cost much lower than currently possible. In hostile environments where human participation may be too dangerous sensor networks may provide a robust service. Sensor networks are designed to transmit data from an array of sensor nodes to a data repository on a server. The advances in the integration of micro-electro-mechanical system (MEMS), microprocessor and wireless communication technology have enabled the deployment of large-scale wireless sensor networks. WSN has potential to design many new applications for handling emergency, military and disaster relief operations that requires real time information for efficient coordination and planning.

Sensors are devices that produce a measurable response to a change in a physical condition like temperature, humidity, pressure etc. WSNs may consist of many different types of sensors such as seismic, magnetic, thermal, visual, infrared, acoustic and radar, capable to monitor a wide variety of ambient conditions. Though each individual sensor may have severe resource constraint in terms of energy, memory, communication and computation capabilities; large number of them may collectively monitor the physical world, disseminate information upon critical environmental events and process the information on the fly.

The issues of network lifetime and data availability are extremely important in WSN due to their deployment in hostile environment. The system should provide fault tolerant energy efficient real-time communication as well as automatic and effective action in crisis situations. A typical sensor network operates in five phases which are planning phase, deployment phase, post-deployment phase, operation phase and post-operation phase.

a. In planning phase, a site survey is conducted to evaluate deployment environment and its conditions to select a suitable deployment mechanism.

b. In deployment phase, sensors are randomly deployed over a target region.

c. In post-deployment phase, the sensor networks operators need to identify or estimate the location of sensors to access coverage.

d. The operation phase involves the normal operation of monitoring tasks where sensors observe the environment and generate data.

e. The post-operation phase involves shutting down and preserving the sensors for future operations or destroying the sensor network.

The sensor nodes consist of sensing, data processing and communicating components. They can be used for continuous sensing, event detection as well as identification, location sensing and control of actuators. The nodes are deployed either inside the phenomenon or very close to it and can operate unattended. They can use their processing abilities to locally carry out simple computations and transmit only required and partially processed data. They may be organized into clusters or collaborate together to complete a task that is issued by the users. In addition, positions of these nodes do not need to be predefined. These allow their random deployment in inaccessible terrains or disaster relief operations. The WSN provides an intelligent platform to gather and analyze data without human intervention. As a result, WSNs have a wide range of applications such as military

applications, to detect and track hostile objects in a battle field or in environmental

research applications, to monitor a disaster as seismic tremor, a tornado or a flood or for industrial applications, to guide and diagnose robots or machines in a factory or for educational applications, to monitor developmental childhood or to create a problem solving environment.

The wireless sensor nodes are generally battery driven and due to their deployment in harsh or hostile environment their battery is usually un-chargeable and un-replaceable. Moreover, since their sizes are too small to accommodate a large battery, they are constrained to operate using an extremely limited energy budget. The total stored energy in a smart dust mote, for instance is only 1J. Since this small amount of energy is the only power supply to a sensor node, it plays a vital role in determining lifetime of the sensor networks. All the research works therefore have a common concern of minimizing energy consumption and it is a significant issue at all layers of the WSN. Other key issues are scalability to large number of nodes, design of data handling techniques, localization techniques, real time communication, data availability, fault tolerance etc.

WIRELESS SENSOR NETWORKS CHARACTERISTICS

A WSN is different from other popular wireless networks like cellular network, wireless local area network (WLAN) and Bluetooth in many ways. Compared to other wireless networks, a WSN has much more nodes in a network, distance between the neighboring nodes is much shorter and application data rate is much lower also. Due to these characteristics, power consumption in a sensor network should be minimized.

To keep the cost of the entire sensor network down, cost of each sensor needs to be reduced. It is also important to use tiny sensor nodes. A smaller size makes it easier for a sensor to be embedded in the environment it is in. WSNs may also have a lot of redundant data since multiple sensors can sense similar information. The sensed data therefore need to be aggregated to decrease the number of transmissions in the network, reducing bandwidth usage and eliminating unnecessary energy consumption in both transmission and reception.

ADVANTAGES OF WIRELESS SENSOR NETWORKS

The WSNs has revolutionized the world around us. They are becoming integral part of our lives, more so than the present-day computers because of their numerous advantages as mentioned below:-

i. Ease of deployment

A sensor network contains hundreds or even thousands of nodes and can be deployed in remote or dangerous environments. Since these nodes are small and economical, throwing of hundreds or thousands of micro-sensors from a plane flying over a remote or dangerous area allows extracting information in ways that could not have been possible otherwise.

ii. Extended range of sensing

Single macro-sensor nodes can only extract data about events in a limited physical range. In contrast, a micro-sensor network uses large numbers of nodes enabling them to cover a wide area.

iii. Improved lifetime

The nodes located close to each other will have correlated data therefore they can be grouped together. Only one of the nodes in a round robin fashion from the group therefore needs to be in active state at any instance of time keeping other nodes in sleep state. It will enhance the network life time.

iv. Fault tolerance

In WSN several sensor nodes are close to each other and have correlated data, it makes these systems much more fault tolerant than single macro-sensor system. The macro-sensor system cannot function if macro-sensor node fails, whereas in case of micro-senor network even if smaller number of micro-sensor nodes fails, the system may still produce acceptable qualitative information.

v. Improved accuracy

While an individual micro-sensor’s data might be less accurate than a macro-sensor’s data. The data from nodes located close to each other can be combined since they are gathering information about the same event. It will result in better accuracy of the sensed data and reduced uncorrelated noise.

vi. Lower cost

Even though, to replace each macro-sensor node several micro-sensor nodes are required they will still be collectively much cheaper than their macro-sensor counterpart due to their reduced size, simple as well as cheap circuitry and lesser accuracy constraints. As a result protocols that enable micro-sensor networks to provide necessary support in sensing applications are becoming more popular.

vii. Actuation

Actuation can dramatically extend the capabilities of a sensor network in two ways. First, it can enhance the sensing task, by pointing cameras, aiming antennae or repositioning sensors. Secondly, it can affect the environment – by opening valves, emitting sounds or strengthening beams.

viii. Collaborative objective

Perhaps the most important aspect of sensor networks that differentiates them from other wireless networks is their objective. Typically, objective of a sensor network is monitoring a specific signal of interest and informing a central base station or a sink about activities in the region being sensed. Since a sensor network is deployed for achieving a certain system-wide goal, nodes collaborate instead of competing with each other.

CHALLENGES IN WIRELESS SENSOR NETWORKS

In order to design good applications for wireless micro-sensor networks, it is essential to understand factors important to the sensor network applications. Although WSNs share some commonalities with existing wireless ad-hoc networks they pose a number of technical challenges different from traditional wireless ad-hoc networks. The protocols and algorithms that have been proposed for traditional wireless ad-hoc networks are therefore not well suited for the application requirements of the sensor networks. To illustrate this point, differences between sensor networks and traditional networks are outlined below:

i. Energy

The sensor nodes are generally inaccessible after deployment and normally they have a finite source of energy that must be optimally used for processing and communication to extend their lifetime. It is a well known fact that communication requires significant energy. In order to make optimal use of energy, therefore communication should be minimized as much as possible.

ii. Redundancy

Due to the frequent node failures and inaccessibility of failed nodes, WSNs are required to have high redundancy of nodes so that the failure of few nodes can be negligible.

iii. System lifetime

The WSNs should function as long as possible. Their system lifetime can be measured by using generic parameters such as time until the nodes die or by using application specific parameters like time until the sensor network is no longer providing acceptable quality results.

iv. Scalability

In WSNs, each sensor node obtains a specific view of the environment. A given sensor's view of the environment is limited both in range and accuracy; it can only cover a limited physical area of the environment. The WSNs therefore, deploys sensor nodes that have a short transmission distance in large numbers to monitor the entire area.

v. Adaptability

The WSN system should be adaptable to changes such as addition of more nodes, failure of nodes, environmental conditions and thus unlike traditional networks, where the focus is on maximizing channel throughput or minimizing node deployment, the major consideration in a sensor network is to extend the network lifetime besides system robustness.

vi. Application awareness

A WSN is not a general purpose network. In order to deploy it for specific application, the WSN protocols should consider application-specific trade-offs in terms of complexity, resource usage and communication patterns to improve network efficiency.

vii. Lack of global identification

Due to large number of sensor nodes in a sensor network the global identification (GID) is generally not possible. Although in some cases, the Global Positioning System (GPS) provides positioning information to sensor nodes but it requires line of sight to several satellites, which is generally not available inside of buildings, beneath dense foliage, underwater, when jammed by an enemy or during MARS exploration etc.

viii. Storage, search and retrieval

The sensor network can produce a large volume of raw data such as continuous time series of observations over all points in space covered by the network. Since the data source is continuous traditional databases are not suitable for WSNs.

ix. Data centric processing

The naming schemes in WSNs are often data-oriented for example an environmental monitoring system may requests temperature readings through a query like "collect temperature readings in the region bounded by the rectangle (x1,y1,x2,y2)", instead of a query "collect temperature readings from a set of nodes having addresses x, y and z."

x. Production cost

The cost of a single node is very important to justify overall cost of the network; since the sensor networks consist of a large number of sensor nodes therefore cost of each sensor node has to be kept low.

xi. Node deployment

Node deployment is application dependent and affects performance of the protocol. The deployment is either deterministic or self-organizing. In deterministic situations, the sensors are manually placed and data is routed through pre-determined paths. However, in self organizing systems, the sensor nodes are scattered randomly creating an infrastructure in an ad-hoc manner.

xii. In-network processing

In general transport protocols used in wired and wireless networks have assumed end-to-end approach guaranteeing that data from the senders have not been modified by intermediate nodes until it reaches a receiver. However, in WSNs data can be modified or aggregated by intermediate nodes in order to remove redundancy of information. The previous solutions did not accommodate concept of in-network processing, called data aggregation or diffusion in WSNs.

xiii. Latency

Latency refers to delay from when a sender sends a packet until the packet is successfully received by the receiver. The sensor data has a temporal time interval in which it is valid, since the nature of the environment changes constantly, it is therefore important to receive the data in a timely manner.

xiv. Fault tolerance

Sensor nodes are fragile and they may fail due to depletion of batteries or destruction by an external event. Realizing a fault-tolerant operation is critical, for successful working of the WSN, since faulty components in a network leads to reduced throughput, thereby decreasing efficiency and performance of the network.

STORAGE MANAGEMENT

Storage management is an area of sensor network research that is attracting attention. In such class of sensor networks, the data must be stored, at least temporarily; within the network until either it is later collected by an observer or ceases to be useful. For example, consider a sensor network that is deployed in a military scenario collecting information about nearby activity. The data has to be dynamically queried by soldiers to attain the mission goals or avoiding sources of danger and help the commanders to assess progress of the mission. The queried data are real-time as well as long-term about enemy activity (for example, to answer a question: Where the supply lines are located?). The data must be stored to enable queries that span temporally long periods, such as days or even months. One can envision similar applications with sensor networks deployed in other contexts that answer questions about the environment using recent or historical data. In such networks collected data are later accessed by dynamically generated queries. With the knowledge of relevant application and system characteristics, a set of goals for the sensor network storage management can be determined:

a. Minimizing storage size to maximize coverage/data retention

b. Minimizing energy

c. Supporting efficient query execution on the stored data (note that in the reach-back method where all the data must be sent to the observer, query execution is simply transfer of the data to the observer)

d. Providing efficient data management under constrained storage.

Efficiency of query execution can be measured in terms of retrieval time, communication overhead and energy consumption required in sending requested data to the observer. The storage management can influence the efficiency of query execution by effective data placement and indexing. Several approaches to storage management have been proposed to meet these requirements, with most approaches involving a tradeoff among these different goals.

Storage management components

The storage management can be split into three major components:

• System support for storage management.

• Collaborative storage.

• Indexing and retrieval.

Specifically, each sensor has a local view of the phenomenon. Each sensor sends sensed data to the cluster head (CH) for collaborative storage. Each CH sends it further to resource rich destination for higher degree of collaboration. Further as a result of coordination, a significant reduction in the data to be stored is achieved.

Indexing and data retrieval

Indexing and retrieval are more important issues in military type applications, where data can be queried dynamically for example; a commander may be interested in enemy tank movements. Such networks are inherently data-centric; observers often name data in terms of attributes or content that may not be topologically relevant. This characteristic of such sensor networks is similar to many peer-to-peer (P2P) environments, which are also often data centric. However, existing solutions of data indexing and retrieval in P2P networks are not

suitable for sensor networks due to excessive communication required. The Geographic Hash Table (GHT) is one of the approaches which can be applied to sensor networks. The GHT is a structured approach to sensor network storage that makes it possible to index data on the basis of content without requiring query flooding. GHT also provides load balancing of storage usage (assuming fairly uniform sensor deployment). GHT implements a distributed hash table by hashing a key k into geographic coordinates.

Advantages of storage management

Accessing and processing data produced in a wireless sensor network using a database like approach has several advantages. Sensors can be deployed in physical environment and applications that manipulate their data; can be created, refined and modified afterwards without any physical intervention on the sensors themselves. The data management activity performed in the network can be remotely controlled by interactively issuing queries, expressed in a high level language, which specify the data, those are of interest for a specific task and how these should be manipulated.

APPLICATION AREAS OF WIRELESS SENSOR NETWORKS

WSNs have opened the eye of new generation scientists to observe never before phenomenon, paving the way for designing of numerous applications. These applications of WSNs can be classified into three categories

• Monitoring space

• Monitoring things

• Monitoring the interactions of things with each other and the encompassing space

Space monitoring includes environmental and habitat monitoring, precision agriculture, indoor climate control, surveillance, treaty verification and intelligent alarms. Whereas monitoring things includes structural monitoring, eco-physiology, condition-based equipment maintenance, medical diagnostics and urban terrain mapping. Further, the most dramatic applications of WSN involve monitoring complex interactions, including wildlife habitats, disaster management, emergency response, ubiquitous computing environments, asset tracking, manufacturing process flow and healthcare. The details of some of the major applications are briefly described below:-

a. Habitat monitoring

Researchers in the life sciences are becoming increasingly concerned about potential impacts of the human presence in monitoring plants and animals in field conditions for example the seabird colonies are notorious for their sensitivity to human disturbance. The WSNs therefore can be used to gather information on the habitat of a plant/animal without disturbing them. The gathered data can be analyzed later on to learn optimal environmental conditions favorable for the flora/fauna’s growth.

b. Military

The use of WSN can provide real time information of the enemy activities to commando teams thus making coordination and planning more effective. The sensing, monitoring and decision-making should be integrated seamlessly, for designing effective military applications. The accurate and timely gathering of visual surveillance and intelligence data can play a central role in attaining objectives as well as minimizing loss of human lives.

c. Home Automation

Networking various home appliances, such as vacuum cleaners, micro-wave ovens, and refrigerators, with wireless medium, has been dreamt of for many years. Embedded sensors inside such appliances can interact with each other, and with the external network via the internet or satellites. They allow users to manage home devices locally and remotely more easily.

d. Precision Agriculture

The WSNs monitors environmental conditions in which farming is done to make it more profitable and sustainable. The WSNs are proving useful for controlling in economical way climate, irrigation and nutrient supply to produce best crop condition, increase in production efficiency while decreasing cost. They are also helping in strategic planning and counter measures to increase yield of the crop.

e. Healthcare

Sensors are used in biomedical applications for healthcare. Sensors are implanted in the human body for monitoring medical problems such as cancer and help patients to maintain their health.

f. Building monitoring

Sensors can be used in buildings for detection of fire and smoke. In case of fire a network of sensors deployed in a huge building can track the source and direction in which fire is expanding. In addition, sensors can be used to monitor vibration that could damage the structure of a building.

g. Environmental observation

WSNs can be used to monitor environment such as forest fire detection, flood detection, air pollution detection, rainfall observation in agriculture etc. Sensor nodes can be used for detection of toxic waste, illegally dumped into the lake by a factory located nearby and relaying the exact origin of a pollutant to a centralized authority, which then can take appropriate measures, to limit spread of the pollution. Without the WSN, it would be difficult to get the data without the nearby factory’s knowledge, in which case the factory would prevent the data gathering process.

h. Disaster Management

The reliable early warning system based on WSN can be deployed in areas with high risk of disasters. The use of WSN promise to provide real time information of the disaster area to rescue teams making coordination and planning more effective. Location information of victims, rescuers and objects in the disaster is vital for the rescue operations.

It has been known that, for an operationally effective disaster management: sensing, monitoring and decision-making should be integrated seamlessly. Timely and updated disaster information is extremely important for efficient response and effective actions, it will help disaster managers make better decisions and take actions in time.

LITERATURE SURVEY

Title: Mobile Sensor Network Data Management

The unique characteristics of MSNs create novel data management opportunities and challenges that have not been addressed in other contexts including those of mobile databases and stationary WSNs. In order to realize the advantages of such networks, researchers have to re-examine existing data management and processing approaches in order to consider sensor and user mobility; develop new approaches that consider the impact of mobility and capture its trade-offs.

Finally, MSN data management researchers are challenged with structuring these networks as huge distributed databases whose edges consist of numerous "receptors" (e.g., RFID readers or sensor networks) and internal nodes form a pyramid scheme for (in-network) aggregation and (pipelined) data stream processing. There are numerous advantages of MSNs over their stationary counterparts. In particular, MSNs over: i) dynamic network coverage, by reaching areas that have not been adequately sampled; ii) data routing repair, by replacing failed routing nodes and by calibrating the operation of the network; iii) data muling, by collecting and disseminating data/readings from stationary nodes out of range; iv) staged data stream processing, by conducting in-network processing of continuous and ad-hoc queries; and v) user access points, by enabling connection to handheld and other mobile devices that are out of range from the communication infrastructure.

Title: Controlled Mobility for Sustainable Wireless Sensor Networks

The use of mobility has been explored recently inn wireless networks. Mobility can be classified into three categories: random, predictable and controlled. Random mobility has been studied before for improving data capacity and networking performance. However, in such cases the latency of data transfer cannot be bounded deterministically, and delivery itself is in jeopardy if the data is cleared from the buffer at the mobile agent. The use of a predictable mobile agent was considered. Morph concentrates on the third category of mobility. While some of the advantages of mobility can be realized in the above approaches, controlled mobile elements are required for a more complete exploitation of this design dimension. Examples of systems in this class are now emerging. In a network of UAV’s was proposed to create a reconfigurable network topology. However, this sort of mobility incurs high energy overheads, requires expensive hardware and has complex navigation requirements.

In this paper, we envision the use of a new design dimension to enhance sustainability in sensor networks – the use of controlled mobility. We argue that this capability can alleviate resource limitations and improve system performance by adapting to deployment demands. While opportunistic use of external mobility has been considered before, the use of controlled mobility is largely unexplored. We also outline the research issues associated with effectively utilizing this new design dimension. Two system prototypes are described to present first steps towards realizing the proposed vision.

Title: Exploiting Mobility for Efficient Coverage in Sparse Wireless Sensor Networks

In the context of area coverage, provide a taxonomy of coverage algorithms in WSNs according to several design criteria, such as: (i) the coverage objective, i.e., considering the lifetime or the number of deployed sensors, (ii) the node deployment method, which may be random or deterministic, (iii) the homogeneous or heterogeneous nature of nodes based on the sensing range, (iv) the nature of the algorithms involved, i.e., centralized or distributed, (v) additional requirements for energy efficiency and connectivity. More thorough surveys of the sensor network coverage problem can be found . In a bidding protocol is described, for mixed WSNs, with static and mobile nodes. The algorithm considers a random initial deployment, where static sensors detect their local coverage holes using Voronoi diagrams and bid mobile sensors, based on the size of their detected coverage hole.

A mobile sensor compares the bids and decides to move if the highest bid received has a coverage hole size greater than the current hole. Reliable monitoring of a large area with a Wireless Sensor Network (WSN) typically requires a very large number of stationary nodes, implying a prohibitive cost and excessive (radio) interference. Our objective is to develop an efficient system that will employ a smaller number of stationary nodes that will collaborate with a small set of mobile nodes in order to improve the area coverage. The main strength of this collaborative architecture stems from the ability of the mobile sensors to sample areas not covered (monitored) by stationary sensors. An important element of the proposed system is the ability of each mobile node to autonomously decide its path based on local information (i.e. a combination of self collected measurements and information gathered by stationary sensors in the mobile’s communication range), which is essential in the context of large, distributed WSNs. The contribution of the paper is the development of a simple distributed algorithm that allows mobile nodes to autonomously navigate through the field and improve the area coverage. We present simulation results based on a real sparse stationary WSN deployment for the coverage improvement scenario.

Title: Performance Analysis of Slotted Carrier Sense IEEE 802.15.4 Medium Access Layer

The analytical evaluation of the slotted CSMA-CA mechanism of IEEE 802.15.4 standard. We assume that there is a fixed number N of devices, and each device always has a packet available for transmission. This saturation assumption is relaxed in the performance evaluation section to model unsaturated traffic conditions. This can be done by adding a fixed number of delay slots to the model. IEEE 802.15.4 is a standard for the medium access control (MAC) and physical layer protocols of wireless networks. This paper provides one of the first analytical evaluations of its MAC protocol for the slotted channel access mechanism in a star topology network. The form of the analysis is similar to that of Bianchi for IEEE 802.11 DCF. The key difference is in the main approximation assumption: Each device’s carrier sensing probability, rather than its packet sending probability, is assumed independent. Also, unlike in 802.11, the slot duration is fixed since the channel is not constantly monitored by the stations. In the slotted CSMA-CA channel access mechanism, the backoff slot boundaries of every device in the PAN are aligned with the superframe slot boundaries of the PAN coordinator. Each time a device wishes to transmit data frames during the CAP, it must locate the boundary of the next slot period. Each device in the network has three variables: NB, CW and BE. NB is the number of times the CSMA-CA algorithm was required to delay while attempting the current transmission. It is initialized to 0 before every new transmission.

Title: ROBOMOTE: ENABLING MOBILITY IN SENSOR NETWORKS

Sensor networks hold the promise of revolutionizing our daily life by ubiquitously monitoring our environment and/or adjusting it to suit our needs. The benefits of this technology have been elabo- rated at length in the literature . The realization of such networks poses many challenges which are the subject of active research in the field. These include challenges stemming directly from the paucity of computation, storage and energy, systems chal- lenges such as unattended long term function, routing and dis- tributed computation (e.g., for localization, calibration, time syn- chronization), and, challenges associated with the dynamics and spatio-temporal irregularity of the environments within which these networks are expected to function.

Severe energy limitations, and a paucity of computation pose a set of difcult design challenges for sensor networks. Recent progress in two seemingly disparate research areas namely, distributed robotics and low power embedded systems has led to the creation of mobile (or robotic) sensor networks. Autonomous node mobility brings with it its own challenges, but also alleviates some of the traditional problems associated with static sensor networks. We illustrate this by presenting the design of the robomote, a robot plat- form that functions as a single mobile node in a mobile sensor network. We briey describe two case studies where the robomote has been used for table top experiments with a mobile sensor net- work.

Title: Mobile Sensor Network Data Management

The improvements in hardware design along with the wide availability of economically viable embedded sensor systems have enabled scientists to acquire environmental conditions at extremely high resolutions. Early approaches to monitor the physical world were primarily composed of passive sensing devices, such as those utilized in wired weather monitoring infrastructures, that could transmit their readings to more powerful processing units for storage and analysis. The evolution of passive sensing devices has been succeeded by the development of Stationary Wireless Sensor Networks (Stationary WSNs). These are composed of many tiny computers, often no bigger than a coin or a credit card, that feature a low frequency processor, some flash memory for storage, a radio for short-range wireless communication, on-chip sensors and an energy source such as AA batteries or solar panels. Applications of stationary WSNs have emerged in many domains ranging from environmental monitoring to seismic and structural monitoring as well as industry manufacturing.

In a traditional database management system, there is a single correct answer to a given query on a given database instance. When querying MSNs the situation is notably different as there are many more degrees of freedom and the underlying querying engine needs to be guided regarding which alternative execution strategy is the right one, typically on the basis of target answer quality and resource availability.

PROJECT DESCRIPTION

PROJECT INTRODUCTION

Among the on-demand protocols, multipath protocols have a relatively greater ability to reduce the route discovery frequency than single path protocols. Ondemand multipath protocols discover multiple paths between the source and the destination in a single route discovery. So, a new route discovery is needed only when all these paths fail. In contrast, a single path protocol has to invoke a new route discovery whenever the only path from the source to the destination fails. Thus, on-demand multipath protocols cause fewer interruptions to the application data traffic when routes fail. They also have the potential to lower the routing overhead because of fewer route discovery operations.

AOMDV shares several characteristics with AODV. It is based on the distance vector concept and uses hop-by-hop routing approach. Moreover, AOMDV also finds routes on demand using a route discovery procedure. The main difference lies in the number of routes found in each route discovery. In AOMDV, RREQ propagation from the source towards the destination establishes multiple reverse paths both at intermediate nodes as well as the destination. Multiple RREPs traverse these reverse paths back to form multiple forward paths to the destination at the source and intermediate nodes.

EXISTING SYSTEM

Characterizing delay in distributed systems has been investigated in different contexts. Recent work has analyzed the latency performance of WSNs in terms of its first-order statistics, i.e., the mean and the variance. However, complex and cross-layer interactions in multihop WSNs require a complete stochastic characterization of the delay. Several efforts have been made to provide probabilistic bounds on delay. Network calculus and its probabilistic extensions are based on a min-plus algebra to provide traffic curves and service curves, which are deterministic (or statistical) bounds of traffic rate and service time, respectively. In these studies, the worst-case performance bounds are analyzed. However, determining worst-case bounds has limited applicability in WSNs for three reasons: First, because of the randomness in wireless communication and the low power nature of the communication links, worst-case bounds do not exist in most practical scenarios. Second, the large variance in the end-to-end delay in WSNs results in loose bounds that cannot accurately characterize the delay distribution. Finally, most applications tolerate packet loss for a lower delay of higher priority packets since the efficiency of the system is improved. These motivate the need for probabilistic delay analysis rather than worst-case bounds.

PROPOSED SYSTEM

In the proposed system we have implemented the AOMDV protocol The core of the AOMDV protocol lies in ensuring that multiple paths discovered are loop-free and disjoint, and in efficiently finding such paths using a flood-based route discovery. AOMDV route update rules, applied locally at each node, play a key role in maintaining loop-freedom and disjointness properties.In the network creation the clusters are formed and the cluster head is chosen on the energy basis.The neighbor node is discovered.After the cluster head is chosen the route discovery is done and the packets are forwarded.

While the packets are forwarded if there is any link failure means the packet drop occurs otherwise the data is successfully transmitted.Then the performance evaluation is carried out. AOMDV shares several characteristics with AODV. It is based on the distance vector concept and uses hop-by-hop routing approach. Moreover, AOMDV also finds routes on demand using a route discovery procedure. The main difference lies in the number of routes found in each route discovery. In AOMDV, RREQ propagation from the source towards the destination establishes multiple reverse paths both at intermediate nodes as well as the destination. Multiple RREPs traverse these reverse paths back to form multiple forward paths to the destination at the source and intermediate nodes.

BLOCK DIAGRAM

MODULES

Architectural Model

Protocol Implementation

Delay cost

Cluster & Cluster Head

Data Formation

Performance Evaluation

MODULES DESCRIPTION

ARCHITECTURAL MODEL

In this model we propose network architecture with nodes.

Simulated area is about 2km*2km.

We initialize the node size, position, and color in the network.

PROTOCOL IMPLEMENTATION

AOMDV relies as much as possible on the routing information already available in the underlying AODV protocol, thereby limiting the overhead incurred in discovering multiple paths. In particular, it does not employ any special control packets. In fact, extra RREPs and RERRs for multipath discovery and maintenance along with a few extra fields in routing control packets (i.e., RREQs, RREPs, and RERRs) constitute the only additional overhead in AOMDV relative to AODV.

DELAY COST

Clearly, the route cutoff problem prevents the discovery of all disjoint reverse paths. This in turn would severely limit the number of disjoint forward paths found at the source if the destination sends RREPs only along disjoint reverse paths. Therefore, we let the destination send back a RREP along each loop-free reverse path even though it is not disjoint with previously established reverse paths. Such additionalRREPs alleviate the route cutoff problem and increase the possibility of finding more disjoint forward paths.

CLUSTER & CLUSTER HEAD

Here the nodes are arranged in the cluster form. Each cluster is assigned with the cluster head. The cluster head is chosen on the basis of the energy.

DATA FORWARDING

For data packet forwarding at a node having multiple paths to a destination, we adopt a simple approach of using a path until it fails and then switch to an alternate path; we use paths in the order of their creation. There are other alternatives for data packet forwarding which concurrently use all paths.

PERFORMANCE EVALUATION

Simulation result illustrates the efficiency of the proposed system compared to algorithm developed for static sensor network. Finally we compare the performance of the existing & the proposed system. The parameters such as throughput, packet delivery ratio, delay are also compared.

FLOW DIAGRAM

IMPLEMENTATION & METHODOLOGY

SOFTWARE SPECIFICATIONS

OS : Linux (vmware)

Simulator : NS2

Language : Tcl/Tk

Graph : GNU plot

Protocol Design : CC

HARDWARE SPECIFICATIONS

Processor Type : Pentium IV

Processor Speed : 2.7GHz

RAM : 1GB

OVERVIEW OF NS-2 SIMULATION TEST BED

NS-2 is n event driven packet level network simulator developed as a part of the VINT project (Virtual Internet Test bed).Version 1 of NS was developed in 1995 and with version 2 in 1996 Ns-2 with C++/OTCL integration feature. Version 2 included a scripting language called Object oriented Tcl (OTCl). It is an open source software package available for both Windows 32 and Linux platforms. NS-2 has many and expanding uses included.

To evaluate that performance of existing network protocols

To evaluate new network protocols before use.

To run large scale experiments not possible in real experiments

To simulate a variety of ip networks.

NS -2 is an object oriented discrete event simulator. Simulator maintains list of events and executes one event after another. Single thread of control: no locking or race conditions Back end is C++ event scheduler.

Protocols mostly

Fast to run, more control

Front end is OTCL

Creating scenarios, extensions to C++ protocols

fast to write and change

CHARACTERISTICS OF NS-2

NS-2 implementation the following features

Multicasting

Simulation of wireless networks

Terrestrial (cellular, Adhoc, GPRS, WLAN, BLUETOOTH), satellite

IEEE 802.11 can be simulated, Mobile IP and Ad hoc protocols such as DSR, TORA, DSDV and AODV Routing

SOFTWARE TOOLS USED WITH NS-2

In the simulation, there are the two tools are used.

NAM(Network Animator)

xGraph

NAM (Network Animator)

NAM provides a visual interpretation of the network topology created. The application was developed as part of the VINT project. Its feature is as follows.

Provides a visual interpretation of the network created

Can be executed directly from a Tcl script

Controls include play; stop fast forward, rewind, pause, a display speed controller button and a packet monitor facility.

Presented information such as throughput, number packets on each link

X Graph

X- Graph is an X-Window application that includes:

Interactive plotting and graphing Animated and derivatives To use Graph in NS-2 the executable can be called within a TCL script. This will then load a graph displaying the information visually displaying the information of the file produced from the simulation. The output is a graph of size 800 x 400 displaying information on the traffic flow and time.

SIMULATION TOOL

NS2 are often growing to include new protocols. LANs need to be updated for new wired/wireless support. ns are an object oriented simulator, written in C++, with an OTCl interpreter as a front-end. The simulator supports a class hierarchy in C++ and a similar class hierarchy within the OTCl interpreter (also called the interpreted hierarchy). The two hierarchies are closely related to each other; from the user’s perspective, there is a one-to-one correspondence between classes in the interpreted.

NS2 uses two languages because simulator has two different kinds of things it needs to do. On one hand, detailed simulations of protocols require a systems programming language which can efficiently manipulate bytes, packet headers, and implement algorithms that run over large data sets. For these tasks run-time speed is important and turn-around time (run simulation, find bug, fix bug, recompile, re-run) is less important.

On the other hand, a large part of network research involves slightly varying parameters or configurations, or quickly exploring a number of scenarios. In these cases, iteration time (change the model and re-run) is more important. Since configuration runs once (at the beginning of the simulation), run-time of this part of the task is less important. Ns meets both of these needs with two languages, C++ and OTCl. C++ is fast to run but slower to change, making it suitable for detailed protocol implementation. OTCl runs much slower but can be changed very quickly (and interactively), making it ideal for simulation configuration. NS (via tclcl) provides glue to make objects and variables appear on both languages.

CONCLUSION

In the proposed system we have implemented the AOMDV protocol The core of the AOMDV protocol lies in ensuring that multiple paths discovered are loop-free and disjoint, and in efficiently finding such paths using a flood-based route discovery. Simulation result illustrates the efficiency of the proposed system compared to algorithm developed for static sensor network. Finally we compare the performance of the existing & the proposed system. The parameters such as throughput, packet delivery ratio, delay are also compared.