Wireless Sensor Network Consists Computer Science Essay

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Wireless Sensor Network contains a number of sensor nodes that are deployed over a geographical area to sense the desired physical phenomena. Typically, a sensor node includes the following basic components: Sensing subsystem, Processing subsystem, Communication subsystem and a power unit. Sensing subsystem acquires data from the geographical area. A processing subsystem encapsulates data processing and storage, and a wireless communication subsystem to transfer the data. Sensor nodes typically exhibit limited capabilities in terms of processing and communication, and they especially run on battery power. Energy consumption in WSN can be broadly classified into duty-cycling, data-driven and mobility based.


Fig 1 . Wireless Sensor Network

A wireless sensor network consists of much lower cost, less power and battery powered sensor nodes. As sensor nodes have limited and non-rechargeable energy resources, energy is a very scarce resource and has to be managed carefully in order to increase the lifetime of the sensor networks. Clustering is an effective scheme in increasing the scalability and lifetime of wireless sensor clustering is an effective scheme in increasing the scalability and life time of wireless sensor networks. In clustering schemes, there are two types of nodes in one cluster, one cluster head and several cluster members. Cluster members collect the data from the geographical area periodically and send the data to the cluster heads. Cluster head collects the data from the members and aggregate it. The aggregated data forward to the based station by the cluster head.

Cluster head maintains two kinds of communications for sending the data to BS, single - hop communication and multi-hop communication. In multi-hop communication clustering algorithm, the energy consumption of cluster heads consists of energy for receiving, aggregating and sending the data from their cluster members and the energy for forwarding data for their neighbour cluster heads.

Wireless Sensor networking is a challenging technology that includes atmosphere monitoring, health systems and robotic exploration. Each sensor node has more than one sensor, a processor and less power radio. A sensor nodes have some degree of non-rechargeable battery power, energy has been managed cautiously to increase life time of the sensor.

Sensor nodes are rigorously controlled by available battery power. Since wireless communications requires considerable amounts of battery power, sensor network must be designed to use as less energy as feasible for accepting and transmitting data. Sensor node's life should be maximized by communication protocol and reduce the bandwidth consumption by using local cooperation among the nodes.

Coverage exhibits how much a sensor area is monitored. Coverage is one of the most important performance factors to assess sensor networks. Coverage becomes serious problem when large numbers of nodes are deployed randomly. Sensor coverage model reflects the characteristics of sensors such as shift function, sensibility, active range and correctness etc. These measurements used to measure sensing ability and value of a sensor. Collected quality of services in various locations used to measure sensing coverage of the wireless network.

Wireless Sensor Networks have specific constraints and rigorous requirements. From these specific requirements, increasing the lifetime of sensor is the major issue. Therefore, the primary constraint on WSN to making batteries runs longer. In recent years, researchers have involved to prove that clustering is a qualified method for improving the lifetime of WSN. Clustering has two forms of nodes, first one of the form is Cluster Head (CH) and second is several Cluster Members (CMs). CM collects data from the surroundings location regularly and transmits the data to CH. CH combines the data from their CM and sends the aggregate data to the BS.

A wireless sensor network is a collection of nodes organized into a cooperative network. Each node consists of processing capability (one or more microcontrollers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single omni-directional antenna), have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. Systems of 1000s or even 10,000 nodes are anticipated. Such systems can revolutionize the way we live and work.

Wireless sensor networks are beginning to be deployed at an accelerated pace. It is not unreasonable to expect that in 10-15 years that the world will be covered with wireless sensor networks with access to them via the Internet. This can be considered as the Internet becoming a physical network. This new technology is exciting with unlimited potential for numerous application areas including environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces. The main reason is that the set of assumptions underlying previous work has changed dramatically. Most past distributed systems research has assumed that the systems are wired, have unlimited power, are not real-time, have user interfaces such as screens and mice, have a fixed set of resources, treat each node in the system as very important and are location independent. In contrast, for wireless sensor networks, the systems are wireless, have scarce power, are real-time, utilize sensors and actuators as interfaces, have dynamically changing sets of resources, aggregate behavior is important and location is critical.

Many wireless sensor networks also utilize minimal capacity devices which places a further strain on the ability to use past solutions. This Chapter presents an overview of some of the key areas and research in wireless sensor networks. The challenges in the hierarchy of: detecting the relevant quantities, monitoring and collecting the data, assessing and evaluating the information, formulating meaningful user displays, and performing decision-making and alarm functions are enormous. The information needed by smart environments is provided by Distributed Wireless Sensor Networks, which are responsible for sensing as well as for the first stages of the processing hierarchy. The importance of sensor networks is highlighted by the number of recent funding initiatives, including the DARPA SENSIT program, military programs, and NSF Program Announcements. The wireless sensor networks, which generally consist of a data acquisition network and a data distribution network, monitored and controlled by a management center. components difficult, let alone the design of a consistent, reliable, robust overall system.

The study of wireless sensor networks is challenging in that it requires an enormous breadth of knowledge from an enormous variety of disciplines. In this chapter we outline communication networks, wireless sensor networks and smart sensors, physical transduction principles, commercially available wireless sensor systems, self-organization, signal processing and decision-making, and finally some concepts for home automation.

Communication Networks

The study of communication networks can encompass several years at the college or university level. To understand and be able to implement sensor networks, however, several basic primary concepts are sufficient.

Network Topology

The basic issue in communication networks is the transmission of messages to achieve a prescribed message throughput (Quantity of Service) and Quality of Service (QoS). QoS can be specified in terms of message delay, message due dates, bit error rates, packet loss, economic cost of transmission, transmission power, etc. Depending on QoS, the installation environment, economic considerations, and the application, one of several basic network topologies may be used.

A communication network is composed of nodes, each of which has computing power and can transmit and receive messages over communication links, wireless or cabled. The basic network topologies are shown in the figure and include fully connected, mesh, star, ring, tree, bus. A single network may consist of several interconnected subnets of different topologies. Networks are further classified as Local Area Networks (LAN), e.g. inside one building, or Wide Area Networks (WAN), e.g. between buildings. Fully connected networks suffer from problems of NP-complexity as additional nodes are added, the number of links increases exponentially. Therefore, for large networks, the routing problem is computationally intractable even with the availability of large amounts of computing power.

Mesh networks are regularly distributed networks that generally allow transmission only to a node's nearest neighbors. The nodes in these networks are generally identical, so that mesh nets are also referred to as peer-to-peer (see below) nets. Mesh nets can be good models for large-scale networks of wireless sensors that are distributed over a geographic region, e.g. personnel or vehicle security surveillance systems. Note that the regular structure reflects the communications topology; the actual geographic distribution of the nodes need not be a regular mesh. Since there are generally multiple routing paths between nodes, these nets are robust to failure of individual nodes or links. An advantage of mesh nets is that, although all nodes may be identical and have the same computing and transmission capabilities, certain nodes can be designated as 'group leaders' that take on additional functions. If a group leader is disabled, another node can then take over these duties. All nodes of the star topology are connected to a single hub node. The hub requires greater message handling, routing, and decision-making capabilities than the other nodes. If a communication link is cut, it only affects one node. However, if the hub is incapacitated the network is destroyed. In the ring topology all nodes perform the same function and there is no leader node.

Messages generally travel around the ring in a single direction. However, if the ring is cut, all communication is lost. The self-healing ring network (SHR) shown has two rings and is more fault tolerant. In the bus topology, messages are broadcast on the bus to all nodes. Each node checks the destination address in the message header, and processes the messages addressed to it. The bus topology is passive in that each node simply listens for messages and is not responsible for retransmitting any messages.

Network Security:

Network security consists of the provisions and policies adopted by a network administrator to prevent and monitor unauthorized access, misuse, modification, or denial of a computer network and network-accessible resources. Network security involves the authorization of access to data in a network, which is controlled by the network administrator. Users choose or are assigned an ID and password or other authenticating information that allows them access to information and programs within their authority.

Network security covers a variety of computer networks, both public and private, that are used in everyday jobs conducting transactions and communications among businesses, government agencies and individuals. Networks can be private, such as within a company, and others which might be open to public access. Network security is involved in organizations, enterprises, and other types of institutions. It does as its title explains: It secures the network, as well as protecting and overseeing operations being done. The most common and simple way of protecting a network resource is by assigning it a unique name and a corresponding password.

Cluster Based Routing

The Cluster based Routing protocol contains associate degree energy-aware clump formula EADC and a cluster-based routing formula. so as to elect cluster heads with higher energy, the parameter of cluster head competition in EADC is predicated on the magnitude relation between the typical residual energy of neighbor nodes and therefore the residual energy of the node itself. Moreover, cluster heads broadcast head messages victimization an equivalent competition vary to construct clusters of even sizes.


Fig 2. Clusters in Wireless Sensor Network

The energy consumption among cluster members may be balanced well. However, the even cluster size conjointly makes the energy consumption among cluster heads unbalanced, because of inhomogeneous distribution of nodes. Cluster heads in dense areas have a lot of member's nodes, so they have high intra-cluster energy consumption. For this, we propose associate degree inter-cluster energy-efficient multi-hop routing protocol, in which cluster heads choose the neighbor cluster head with higher residual energy and a smaller range of cluster members as the next hop to balance the energy consumption among cluster heads.

Maximal Independent Set

Maximal Independent Set (MIS) is an self-governing set which is having at least one endpoint and not a subset of any other self-governing set. Maximum independent set is related with minimum connected dominating set by comparison of these sets. This will show how many vertices are required to connect a maximal independent set. In this paper, we step forward a Set-based routing protocol for WSN whose core is based on Maximal Independent Set (MIS).


Fig 3. Maximum Independent Set

A maximal independent set or maximal stable set is an independent set that is not a subset of any other independent set. That is, it is a set S such that every edge of the graph has at least one endpoint not in S and every vertex not in S has at least one neighbor in S. A maximal independent set is also a dominating set in the graph, and every dominating set that is independent must be maximal independent, so maximal independent sets are also called independent dominating sets. A graph may have many maximal independent sets of widely varying sizes; a largest maximal independent set is called a maximum independent set.

Project Description

Sensor nodes are scattered in the field and which deployed in non uniform manner. The Set Head nodes get elected based on the link connectivity and its remaining energy levels of specific rounds. The Set Members nodes have to send its data to the SH which aggregate it into a single data packet.

The following assumptions have been made,

All the nodes in the network know its position by using GPS receivers.

After the deployment of the nodes, it becomes stationary nodes.

All the nodes are Homogenous, having equal sensing range, battery power and uses same type of sensor.

Nodes can communicate with each other and with the Base Station

All the nodes exhibit the same Deterministic coverage model.

Energy efficient data collection in wireless sensor networks project work based on energy efficient Maximum Independent Set (MIS) concept. It is a set-based routing protocol for WSN is based on Maximal Independent Set (MIS). It has the following advantages over clustering as: MIS avoids overlapping, Maximum area coverage, Network life time is prolonged by set based routing.

Set creation contains two kinds of nodes, one as Set Head (SH) and specific number of set Members (SMs). MIS forms the sets based on non-adjacent nodes. To equalize the energy conservation amongst set members, SH activity is assigned to next higher residual SM node.



LEACH is a cluster- based protocol that forms the clusters by the received indication power and without any centralized control. LEACH is the cluster based algorithm. LEACH forms the clusters based on the received signal strength and without any centralized control. The CH is determined with probability that can reach using least communication energy. CH localized the data processing of all the fusion and aggregation. The role of CH is assigned to some other node in order to balance the load. The rotation of CH role is assigned through choosing random number between 0 and 1. A node becomes a CH for the current rotation, if the random number is less than the threshold. Data from cluster nodes to BS achieves through CH.

In EDUC, cluster heads use uneven competition ranges to construct clusters of uneven sizes. Clusters farther far from the bachelor's degree have smaller sizes so as to preserve some energy for long-distance knowledge transmission. Therefore, the energy consumption among cluster heads is balanced effectively. Supported this agglomeration structure, an energy-driven cluster head rotation theme is planned to minimize the extra energy waste. Each node acts as cluster head no over once throughout the total network lifetime. Thus, EDUC minimizes the extra value and achieves high energy potency. In this paper, we tend to accurately calculate the energy threshold in cluster head rotation supported the idea that the cluster head may be a single-hop far from the bachelor's degree. However, the single-hop assumption might not continuously be applicable for some real state of affairs. In agglomeration multi hop networks mistreatment a random competition theme for electing cluster head, the number of packages forwarded by a cluster head isn't straightforward to estimate whereas computing the energy threshold. Therefore, the energy-driven cluster head rotation theme planned here isn't applicable for multi hop networks, because the energy threshold is asked to be terribly precise. For further work, we'll specialize in the appliance of EDUC to multi hop wireless sensing element networks.

PEGASIS is optimal chain-based protocol that communicates only with close neighbor nodes for transmitting the data to BS which reduces the energy spent per round. S-MAC auto-synchronize on sleep schedules for their neighboring nodes. ETSP identifies the condition to toggle between receiver-receiver as RBS and sender-receiver TPSN based on the threshold value.

GESC selects the CH based on the residual energy and forward through multi-hop paths to sink. GESC is a Distributed clustering algorithm and designed for multi-hop networks. Clustering creates the hierarchical base structure. Network management technique uses the residual energy for selecting cluster head. The clustering protocol is divided into two major procedures: the clustering formation procedure (CFP) and the network operation procedure (NOP). The duration of the clustering formation procedure is the time interval needed to cluster the network, while the duration of the network operation procedure is the time interval between two subsequent intervals. The clustering protocol is divided into rounds where at the beginning of each round CFP is triggered. The NOP follows the CFP when data transferred from the nodes to cluster heads and it will forward through multi-hop paths to the information sink.

OWFA evaluate optimal wake up frequency by totalling the operating cost values and data broadcast energy values of all child nodes. This frequency assigns by the root node such as head node to all child node. OWFA exhibits both centralized and distributed mechanism. OWFA assigns optimal wake up frequency to all nodes in the data gathering tree. OWFA has three procedures as: Alpha procedure, Combine-Node procedure and Assign frequency procedure. Alpha calculates optimal wake-up frequency by summing the overhead energy values and data transmission energy values of all child nodes. Combine-Node procedure recursively calculates the combined energy consumption coefficient of the root node. The root node assigns the wake-up frequency of each node.

ML-MAC reduces the wake up time of each sensor node by listening the periods of nodes in various layers. ML-MAC is a distributed contention-based and self organizing MAC protocol, where nodes discover their neighbours based on their radio signal level. ML-MAC divides sensor nodes into layers. Layers are randomly chosen by nodes and its time period of each layer is divided into number of frames. Each frame decides its listen and sleep periods. The listen periods of nodes in different layers are non-overlapping. A node in the ML-MAC protocol wakes up only at its assigned layer's listen period. ML-MAC reduces the energy consumption than other protocols by reducing the wake up time of each node through layered architecture.

R-MAC allots time slot to every node that will be traced up by nearby sender node to transmit its message to the receiver node using time stealing mechanism. TDMA maintains path wake-up aggregation techniques for low end-to-end delay from the sensors to the gateway. RMAC is a type of TDMA based scheduling scheme. Unlike other MAC protocol, each node in its time slot will receive message from other nodes. To avoid simultaneous message transmission from multiple nodes to the receiver node, each receiver node will have a sender node on its scheduled time period. When compared with other Receiver MAC protocol, it is having time stealing mechanism. If the sender node is not transmitting any message on the time slot of receiver node, then the allotted time slot will be traced up by nearby sender node to transmit its message to the receiver node.

EADC forward to sink based on the calculated threshold through next CH node having higher residual energy. EADC is a cluster-based routing protocol. EADC calculates average residual energy and waiting time of each node. If a node has not receive any header message from remaining nodes within the waiting time, then it elects as a CH. The other nodes are joined as members to CH. CH broadcasts the schedule to its cluster members. During its schedule, nodes transmit the data to CH. If the distance between the CH to BS is less than the calculated threshold, then it will have BS as next hop. Otherwise, it will forward to next CH node having higher residual energy.

The above algorithms like LEACH explain layered based CH selection as single-level clustering as well as multi-level clustering strategy. EADEEG selects the cluster head based on the proportion among the middling residual energy of neighbor nodes and the residual energy of the node itself. EADEEG achieves a prolong network life time by good cluster head distribution.

EECL is distributed over the sensors which forms optimal clusters. The cluster has been created and the data transmission is fixed with TDMA scheduling. Sensors nodes have to send the data during its allocation transmission time to the CH. After allocating transmission time for each node, the radio of CH node is turn to off. When wake-up time CH receives data from remaining nodes and the data has been aggregated. The data has sent through energy efficient CH to BS.

NCCARQ MAC is a network coding technique. NCCARQ correlates the transmissions between a set of relay nodes that supports a bidirectional communication among pair of nodes. NCCARQ-MAC enables wireless workstations to request cooperation of neigboring nodes for correct reception of a data packet. It allows the helper nodes to perform network coding techniques to the packets to be transmitted before relaying the packets. The relay store, a copy of any captured data packet until it is acknowledged by the intended destination. The error mechanism, such as Cyclic Redundancy Code is applied to perform error control for receiving messages.

EEGTP avoids direct transmission from CH to BS which uses multi-hop data communication. ECCA minimizes the energy conservation by reducing the activated nodes using sleep/wake up schedules. The less number of sensor nodes selected in closely atmosphere while keeping complete coverage. PEAS maintains coverage by adjusting the probing range of nodes and energy reduced by activate only those set of coverage maintaining nodes.




The energy consumption among nodes are more imbalanced in cluster-based wireless sensor networks. In a existing System, a cluster-based routing protocol for wireless sensor networks with non uniform node distribution includes an energy-aware clustering algorithm EADC and a cluster-based routing algorithm. EADC uses competition range to construct clusters of even sizes. At the same time, the routing algorithm increases forwarding tasks of the nodes in scarcely covered areas by forcing cluster heads to choose nodes with higher energy and fewer member nodes as their next hops, and finally, achieves load balance among cluster heads.

The clustering algorithm balances the energy consumption among cluster members by constructing equal clusters. Inevitably, the energy consumption among cluster heads is imbalance due to the non uniform node distribution. Therefore, a cluster-based inter-cluster routing algorithm is used to balance the energy consumption among cluster heads by adjusting intra-cluster energy consumption and inter-cluster energy consumption. Each cluster head chooses a cluster head with higher residual energy and fewer cluster members as its next hop.

The cluster heads elected in EADC is uniform manner, which means that all the clusters have even sizes. The number of nodes within a cluster is non uniform, due to the non uniform distribution of nodes. Clusters in dense area have more cluster members, while clusters in sparsely covered areas have fewer cluster members. In multi-hop communication clustering algorithm, the energy consumption of cluster heads is divided into intra-cluster energy consumption of cluster heads is divided into intra-cluster energy consumption and inter-cluster energy consumption. In order to balance the energy consumption between cluster heads is the ratio of the two part of the energy consumption among cluster heads. Cluster heads in sparse areas take more forwarding tasks to alleviate the imbalance of inter-cluster energy consumption. Cluster heads with higher residual energy taking more forwarding tasks is better for prolonging the network lifetime.

Among all the cluster heads, several nodes need to be chosen to be child nodes of the BS, and communicate with the BS directly. Therefore, each cluster head determines whether to communicate with the BS directly according to the distance to the BS. Here, we introduce a threshold distance. If the distance from cluster head to BS is less than the threshold then it communicates with BS directly, and follows the multi-hop routing if the distance is greater than the threshold level.


Energy consumption rates are more imbalance

Less network lifetime


In the set based networks, the imbalanced energy consumption among nodes is the key factor affecting the network life. To balance the energy consumption between the nodes, creating the sets for networks with irregular node distribution tend to construct sets based on non-adjacency and the sets have the particular number of members and coverage areas. Thus, the intra-cluster energy consumption of cluster heads can be balanced. For cluster members, the maximum communicate distance of cluster members are approximate, because of the uniform cluster sizes. Thus, the energy consumption among nodes and finally prolong the network life time.

Energy-efficient Cluster head selection is based on the residual energy and link connectivity of the node. Energy efficient head node selection is the major issue in wireless sensor networks. The energy conservation has been reduced by selecting the proper Set Head (SH) nodes. SH selection is based on the residual energy as well as the link connectivity of the node without interference of the server i.e., Non adjacency of the nodes. Multi hop routing is maintained the inter set energy consumption phase to reduce the energy conservation.

Due to non regular node distribution energy consumption is not a balanced one in cluster-based wireless sensor networks. To rectify this problem, Maximal Independent Set (MIS) selection technique is used for energy efficient data collection in non regular node distribution. By using the MIS avoids overlap, increase the network lifetime and maximum area coverage


MIS avoids overlapping

Maximum area coverage

Network life time is prolonged by set based routing

High message delivery ratio

The methodology concentrates on reducing intra-set communication cost and inter-set communication cost. Non adjacency nodes have been detected for creating MIS. Intra-set energy consumption will be reduced by the data collection and data aggregation. Inter-set consumption follows the routing algorithm. The methodology will be used for this work is more efficient and stable set formation which increases the lifetime of sensor networks i.e., Number of data collection rounds. This project also reduces the energy consumption of SH by multi-hop communication to BS.





NET BEAN 6.9.1





Software Description


Java is a programming language originally developed by James Gosling at Sun Microsystems (now a subsidiary of Oracle Corporation) and released in 1995 as a core component of Sun Microsystems' Java platform. The language derives much of its syntax from C and C++ but has a simpler object model and fewer low-level facilities. Java applications are typically compiled to byte code (class file) that can run on any Java Virtual Machine (JVM) regardless of computer architecture. Java is a general-purpose, concurrent, class-based, object-oriented language that is specifically designed to have as few implementation dependencies as possible. It is intended to let application developers "write once, run anywhere." Java is currently one of the most popular programming languages in use, particularly for client-server web applications.

The original and reference implementation Java compilers, virtual machines, and class libraries were developed by Sun from 1995. As of May 2007, in compliance with the specifications of the Java Community Process, Sun relicensed most of its Java technologies under the GNU General Public License. Others have also developed alternative implementations of these Sun technologies, such as the GNU Compiler for Java and GNU Class path.

Java Platform

One characteristic of Java is portability, which means that computer programs written in the Java language must run similarly on any hardware/operating-system platform. This is achieved by compiling the Java language code to an intermediate representation called Java byte code, instead of directly to platform-specific machine code. Java byte code instructions are analogous to machine code, but are intended to be interpreted by a virtual machine (VM) written specifically for the host hardware. End-users commonly use a Java Runtime Environment (JRE) installed on their own machine for standalone Java applications, or in a Web browser for Java applets. Standardized libraries provide a generic way to access host-specific features such as graphics, threading, and networking.

A major benefit of using byte code is porting. However, the overhead of interpretation means that interpreted programs almost always run more slowly than programs compiled to native executables would. Just-in-Time compilers were introduced from an early stage that compiles byte codes to machine code during runtime.

Just as application servers such as Glass Fish provide lifecycle services to web applications, the Net Beans runtime container provides them to Swing applications. Application servers understand how to compose web modules, EJB modules, and so on, into a single web application, just as the Net Beans runtime container understands how to compose Net Beans modules into a single Swing application.

Modularity offers a solution to "JAR hell" by letting developers organize their code into strictly separated and versioned modules. Only those that have explicitly declared dependencies on each other are able to use code from each other's exposed packages. This strict organization is of particular relevance to large applications developed by engineers in distributed environments, during the development as well as the maintenance of their shared codebase.

Java a High-level Language

A high-level programming language developed by Sun Microsystems. Java was originally called OAK, and was designed for handheld devices and set-top boxes. Oak was unsuccessful so in 1995 Sun changed the name to Java and modified the language to take advantage of the burgeoning World Wide Web.

Java is an object-oriented language similar to C++, but simplified to eliminate language features that cause common programming errors. Java source code files (files with a .java extension) are compiled into a format called byte code (files with a .class extension), which can then be executed by a Java interpreter. Compiled Java code can run on most computers because Java interpreters and runtime environments, known as Java Virtual Machines (VMs), exist for most operating systems, including UNIX, the Macintosh OS, and Windows. Byte code can also be converted directly into machine language instructions by a just-in-time compiler (JIT).

Java is a general purpose programming language with a number of features that make the language well suited for use on the World Wide Web. Small Java applications are called Java applets and can be downloaded from a Web server and run on your computer by a Java-compatible Web browser, such as Netscape Navigator or Microsoft Internet Explorer.

Object-oriented software development matured significantly during the past several years. The convergence of object-oriented modeling techniques and notations, the development of object-oriented frameworks and design patterns, and the evolution of object-oriented programming languages have been essential in the progression of this technology.

Object-Oriented Software Development using Java: Principles, Patterns, and Frameworks contain a much applied focus that develops skills in designing software-particularly in writing well-designed, medium-sized object-oriented programs. It provides a broad and coherent coverage of object-oriented technology, including object-oriented modeling using the Unified Modeling Language (UML) object-oriented design using Design Patterns, and object-oriented programming using Java.

Net Beans

The Net Beans Platform is a reusable framework for simplifying the development of Java Swing desktop applications. The Net Beans IDE bundle for Java SE contains what is needed to start developing Net Beans plug-in and Net Beans Platform based applications; no additional SDK is required.

Applications can install modules dynamically. Any application can include the Update Center module to allow users of the application to download digitally-signed upgrades and new features directly into the running application. Reinstalling an upgrade or a new release does not force users to download the entire application again.

The platform offers reusable services common to desktop applications, allowing developers to focus on the logic specific to their application. Among the features of the platform are:

User interface management (e.g. menus and toolbars)

User settings management

Storage management (saving and loading any kind of data)

Window management

Wizard framework (supports step-by-step dialogs)

Net Beans Visual Library

Integrated Development Tools

Wamp Server

WAMPs are packages of independently-created programs installed on computers that use a Microsoft Windows operating system. WAMP is an acronym formed from the initials of the operating system Microsoft Windows and the principal components of the package: Apache, MySQL and one of PHP, Perl or Python.

Apache is a web server. MySQL is an open-source database. PHP is a scripting language that can manipulate information held in a database and generate web pages dynamically each time content is requested by a browser. Other programs may also be included in a package, such as phpMyAdmin which provides a graphical user interface for the MySQL database manager, or the alternative scripting languages Python or Perl. Equivalent packages are MAMP (for the Apple Mac) and LAMP (for the Linux operating system).


The MySQL development project has made its source code available under the terms of the GNU General Public License, as well as under a variety of proprietary agreements. MySQL was owned and sponsored by a single for-profit firm, the Swedish company MySQL AB, now owned by Oracle Corporation.

Free-software-open source projects that require a full-featured database management system often use MySQL. For commercial use, several paid editions are available, and offer additional functionality. Applications which use MySQL databases include: TYPO3, Joomla, WordPress, phpBB, Drupal and other software built on the LAMP software stack. MySQL is also used in many high-profile, large-scale World Wide Web products, including Wikipedia, Google (though not for searches), Facebook, and Twitter.


Energy efficient Coverage aware Data Collection protocol works based on energy efficient Maximum Independent Set (MIS) concept. This project has three modules follows,

Maximal Independent Set Formation phase

Set Head election phase

Communication phase

MIS Formation Phase:

The nodes participating in the network knows its own location and other nodes location with the help of GPS receivers or by some other cost effective techniques. The distance between one node to all other nodes are calculated using Euclidean distance formula,

Distance d = (1)

The Euclidean distance'd' used to find the non adjacency nodes with the particular threshold value X. The distance 'X' is computed such that the nodes which are 'X' distance far away from each other is eligible to form a set.

D = / 100 (2)

Where 'n' can be any random node from the network and dn,i denotes the distance between the node 'n' to 'i'.

X = D δ (3)

where δ is negligible when compared with D. MIS formation phase has been worked with the following control messages: i) Idt_msg, ii) Set_msg.

Idt_msg: All the nodes will send their location details to the BS.

Set_msg: The BS will form the MIS with 'X' as the minimum distance between the set members. It sends the Set_msg to all the nodes indicating its membership to particular set.

Set Head Election Phase:

After the MIS sets have been formed, the distance between each node to all other nodes in each set is calculated as 'Y',

Y = (4)

The proper eligible SH is chosen in such a way that SH=min(Y). Set Head Election phase has the following control messages as i) Head_msg, ii) Slot_msg.

Head_msg: Within each set, BS identifies a node which is easily reachable by all other set member & it is elected as Set Head. BS sends Head_msg to all the newly elected Set Head nodes.

Slot_msg: After receiving the Head_msg, each Set Head in turn will generate a separate data collection time slot for each member nodes and sent it to the set members.

Communication phase:

The data generated by sensor nodes by sensing the field will be communicated to the base station at equal interval of time. It has been divided into two sub-phases as intra-cluster communication and inter-cluster communication phases.

Intra Set phase:

The Set Member nodes send its data to the Set Head nodes in its allotted time slots regularly. After collecting all the data from its set members SH node will aggregate the data.

Inter Set phase:

Each SH node has to calculate its distance with the Base station. If the distance is less than the threshold level 'T', then it directly transmits its packet to the BS, otherwise it forwards its aggregated data packet to the next nearest neighbour SH node and it reaches the BS.

When the above formed Set is working for long time, the Set Head nodes will loses its energy quickly and may not be in a position to survive in the network. In order to avoid this, EECDC reorganises the existing Set for every 'r' data collection rounds and elects new Set Heads to balance energy consumption among nodes. The new Set Heads elected after 'r' rounds are based on link connectivity and residual energy.




Unit testing is usually conducted as part of a combined code and unit test phase of the software lifecycle, although it is not uncommon for coding and unit testing to be conducted as two distinct phases.

Test strategy and approach

Field testing will be performed manually and functional tests will be written in detail.

Test objectives

All field entries must work properly.

Pages must be activated from the identified link.

The entry screen, messages and responses must not be delayed.

Test Case ID

Test Case


Expected Output

Actual Output



Collecting the information from the sensors

By answering the query asked

The opinion will be collected for each node separately

The opinion will be collected for each node separately


Table: Test case for Unit Testing

Features to be tested

Verify that the entries are of the correct format

No duplicate entries should be allowed

All links should take the user to the correct page.

Test Results:

All the test cases mentioned above passed successfully. No defects encountered.


Software integration testing is the incremental integration testing of two or more integrated software components on a single platform to produce failures caused by interface defects.

The task of the integration test is to check that components or software applications, e.g. components in a software system or - one step up - software applications at the company level - interact without error.

Test Case ID

Test Case


Expected Output

Actual Output



Analyze the Correct information to the nodes

By answering the query asked

The opinion will be collected for each node separately

The opinion will be collected for each node separately


Table: Test case for Integration Testing

Test Results:

All the test cases mentioned above passed successfully. No defects encountered.



EECDC works by exploring the features of Maximal Independent Set concept. Instead of activating every node in the network, it divides this into equal numbered sets. Each set is activated in sequence in such a way that when one set is activated all other set nodes will be sleeping state. Since the node which is having minimum distance with all other nodes in the network is elected as Set Head, Intra Set Communication cost is reduced. Also Inter Set communication cost is reduced by using multi-hop communication between Set Head to BS node if necessary. But EECDC does not monitor the surrounding environment all the time. When a particular Set is activated it covers certain area in the target region and through activating the Set sequentially the uncovered area will be monitored by other Sets at different timing interval. In total EECDC achieves 10 fold times better energy efficient coverage of the region when compared with other existing protocols.



Energy conservation of the sensor network further improved by introducing multi hop communication in the intra set energy consumption phase. In the future, work will focus on heterogeneous sensor networks composed of different types of sensors and discuss the

approaches to guarantee the expected coverage rate.