Discovery and Verification of Neighbor Positions in

Mobile Ad Hoc Networks

Abstract

A growing number of ad hoc networking protocols and location-aware

services require that mobile nodes learn the position of their neighbors. However,

such a process can be easily abused or disrupted by adversarial nodes. In absence

of a priori trusted nodes, the discovery and verification of neighbor positions

presents challenges that have been scarcely investigated in the literature. In this

paper, we address this open issue by proposing a fully distributed cooperative

solution that is robust against independent and colluding adversaries, and can be

impaired only by an overwhelming presence of adversaries. Results show that our

protocol can thwart more than 99 percent of the attacks under the best possible

conditions for the adversaries, with minimal false positive rates.

An NPV Scheme to identify Neighbors in Mobile

Networks

Modified Abstract

Location awareness has become an asset in mobile systems, where a wide

range of protocols and applications require knowledge of the position of the

participating nodes. In absence of a priori trusted nodes the discovery and

verification of neighbor positions becomes particularly challenging in the presence

of adversaries aiming at harming the system. In this paper, we address this open

issue by proposing a fully distributed cooperative solution that is robust against

independent and colluding adversaries, and can be impaired only by an

overwhelming presence of adversaries.

Introduction

Wireless ad hoc network

A wireless ad hoc network is a decentralized type of wireless network.[1]

The network is ad hoc because it does not rely on a pre existing infrastructure, such

as routers in wired networks or access points in managed (infrastructure) wireless

networks. Instead, each node participates in routing by forwarding data for other

nodes, so the determination of which nodes forward data is made dynamically on

the basis of network connectivity. In addition to the classic routing, ad hoc

networks can use flooding for forwarding data. An ad hoc network typically refers

to any set of networks where all devices have equal status on a network and are

free to associate with any other ad hoc network device in link range. Ad hoc

network often refers to a mode of operation of IEEE 802.11 wireless networks. It

also refers to a network device's ability to maintain link status information for any

number of devices in a 1-link (aka "hop") range, and thus, this is most often a

Layer 2 activity. Because this is only a Layer 2 activity, ad hoc networks alone

may not support a routeable IP network environment without additional Layer 2 or

Layer 3 capabilities.

An ad hoc network is made up of multiple “nodes” connected by

“links.” Links are influenced by the node's resources (e.g., transmitter power,

computing power and memory) and behavioral properties (e.g., reliability), as well

as link properties (e.g. length-of-link and signal loss, interference and noise). Since

links can be connected or disconnected at any time, a functioning network must be

able to cope with this dynamic restructuring, preferably in a way that is timely,

efficient, reliable, robust, and scalable. The network must allow any two nodes to

communicate by relaying the information via other nodes. A “path” is a series of

links that connects two nodes. Various routing methods use one or two paths

between any two nodes; flooding methods use all or most of the available paths.

Mobile ad hoc network

A mobile ad hoc network (MANET) is a self-configuring infrastructure less

network of mobile devices connected by wireless. Ad hoc is Latin and means "for

this purpose".[1] Each device in a MANET is free to move independently in any

direction, and will therefore change its links to other devices frequently. Each must

forward traffic unrelated to its own use, and therefore be a router. The primary

challenge in building a MANET is equipping each device to continuously maintain

the information required to properly route traffic. Such networks may operate by

themselves or may be connected to the larger Internet. MANETs are a kind of

Wireless ad hoc network that usually has a routable networking environment on

top of a Link Layer ad hoc network. The growth of laptops and 802.11/Wi-Fi

wireless networking have made MANETs a popular research topic since the mid-

1990s. Many academic papers evaluate protocols and their abilities, assuming

varying degrees of mobility within a bounded space, usually with all nodes within

a few hops of each other. Different protocols are then evaluated based on measures

such as the packet drop rate, the overhead introduced by the routing protocol, end-

to-end packet delays, network throughput etc.

MANETS can be used for facilitating the collection of sensor data for data

mining for a variety of applications such as air pollution monitoring and different

types of architectures can be used for such applications.[2] It should be noted that a

key characteristic of such applications is that nearby sensor nodes monitoring an

environmental feature typically register similar values. This kind of data

redundancy due to the spatial correlation between sensor observations inspires the

techniques for in-network data aggregation and mining. By measuring the spatial

correlation between data sampled by different sensors, a wide class of specialized

algorithms can be developed to develop more efficient spatial data mining

algorithms as well as more efficient routing strategies.[3] Also researchers have

developed performance models[4][5] for MANET by applying Queueing Theory.

These attacks on MANETs challenge the mobile infrastructure in which

nodes can join and leave easily with dynamics requests without a static path of

routing. Schematics of various attacks as described by Al-Shakib Khan [1] on

individual layer are as under: Application Layer: Malicious code, Repudiation

Transport Layer: Session hijacking, Flooding Network Layer: Sybil, Flooding,

Black Hole, Grey Hole. Worm Hole, Link Spoofing, Link Withholding, Location

disclosure etc. Data Link/MAC: Malicious Behavior, Selfish Behavior, Active,

Passive, Internal External Physical: Interference, Traffic Jamming, Eavesdropping

Vehicular ad hoc network

A vehicular ad hoc network (VANET) uses cars as mobile nodes in a

MANET to create a mobile network. [1] A VANET turns every participating car

into a wireless router or node, allowing cars approximately 100 to 300 metres of

each other to connect and, in turn, create a network with a wide range. As cars fall

out of the signal range and drop out of the network, other cars can join in,

connecting vehicles to one another so that a mobile Internet is created. It is

estimated that the first systems that will integrate this technology are police and

fire vehicles to communicate with each other for safety purposes.

Most of the concerns of interest to mobile ad hoc networks (MANETs) are

of interest in VANETs, but the details differ. Rather than moving at random,

vehicles tend to move in an organized fashion. The interactions with roadside

equipment can likewise be characterized fairly accurately. And finally, most

vehicles are restricted in their range of motion, for example by being constrained to

follow a paved highway. In addition, in 2006 the term MANET mostly described

an academic area of research, and the term VANET an application. Such a network

might pose safety concerns (for example, one cannot safely type an email while

driving). GPS and navigation systems might benefit, as they could be integrated

with traffic reports to provide the fastest route to work. It was also promoted for

free, VoIP services such as GoogleTalk or Skype between employees, lowering

telecommunications costs.

MAC

A medium access control (MAC) protocol coordinates actions over a shared

channel. The most commonly used solutions are contention-based. One general

contention-based strategy is for a node which has a message to transmit to test the

channel to see if it is busy, if not busy then it transmits, else if busy it waits and

Tries again later. After colliding, nodes wait random amounts of time trying to

avoid re-colliding. If two or more nodes transmit at the same time there is a

collision and all the nodes colliding try again later. Many wireless MAC protocols

also have a doze mode where nodes not involved with sending or receiving a

packet in a given timeframe go into sleep mode to save energy. Many variations

exist on this basic scheme.

In general, most MAC protocols optimize for the general case and for

arbitrary communication patterns and workloads. However, a wireless sensor

network has more focused requirements that include a local uni-or broad-cast,

traffic is generally from nodes to one or a few sinks (most traffic is then in one

direction),have periodic or rare communication and must consider energy

consumption as a major factor. An effective MAC protocol for wireless sensor

networks must consume little power, avoid collisions, be implemented with a small

code size and memory requirements, be efficient for a single application, and be

tolerant to changing radio frequency and networking conditions.

One example of a good MAC protocol for wireless sensor networks is B-

MAC. B-MAC is highly configurable and can be implemented with a small code

and memory size. It has an interface that allows choosing various functionality and

only that functionality as needed by a particular application. B-MAC consists of

four main parts: clear channel assessment (CCA), packet back off, link layer acks,

and low power listening. For CCA, B-MAC uses a weighted moving average of

samples when the channel is idle in order to assess the background noise and better

be able to detect valid packets and collisions. The packet backoff time is

configurable and is chosen from a linear range as opposed to an exponential

backoff scheme typically used in other distributed systems. This reduces delay and

works because of the typical communication patterns found in a wireless sensor

network. B-MAC also supports a packet by packet link layer acknowledgement. In

This way only important packet need pay the extra cost. A low power listening

scheme is employed where a node cycles between awake and sleep cycles. While

awake it listens for a long enough preamble to assess if it needs to stay awake or

can return to sleep mode. This scheme saves significant amounts of energy. Many

MAC protocols use a request to send (RTS) and clear to send (CTS) style of

interaction. This works well for ad hoc mesh networks where packet sizes are large

(1000s of bytes). However, the overhead of RTS-CTS packets to set up a packet

transmission is not acceptable in wireless sensor networks where packet sizes are

On the order of 50 bytes. B-MAC, therefore, does not use a RTS-CTS scheme.

Recently, there has been new work on supporting multi-channel wireless

sensor networks. In these systems it is necessary to extend MAC protocols to

multi-channel MACs. One such protocol is MMSN. These protocols must support

all the features found in protocols such as B-MAC, but must also assign

Frequencies for each transmission. Consequently, multi-frequency MAC protocols

consist of two phases: channel assignment and access control. The details for

MMSN are quite complicated and are not described here. On the other hand, we

expect that more and more future wireless sensor networks will employ multiple

Channels (frequencies). The advantages of multi-channel MAC protocols include

providing greater packet throughput and being able to transmit even in the

presence of a crowded spectrum, perhaps arising from competing networks or

commercial devices such as phones or microwave ovens.

MD5 Hash Algorithm

The MD5 message-digest algorithm is a widely used cryptographic hash

function producing a 128-bit (16-byte) hash value, typically expressed as a 32 digit

hexadecimal number. MD5 has been utilized in a wide variety of security

applications. It is also commonly used to check data integrity.

MD5 digests have been widely used in the software world to provide some

assurance that a transferred file has arrived intact. For example, file servers often

provide a pre-computed MD5 (known as Md5sum) checksum for the files, so that a

user can compare the checksum of the downloaded file to it. Most unix-based

operating systems include MD5 sum utilities in their distribution packages;

Windows users may install a Microsoft utility, or use third-party applications.

Android ROMs also utilize this type of checksum.

However, now that it is easy to generate MD5 collisions, it is possible for

the person who created the file to create a second file with the same checksum, so

this technique cannot protect against some forms of malicious tampering. Also, in

some cases, the checksum cannot be trusted (for example, if it was obtained over

the same channel as the downloaded file), in which case MD5 can only provide

error-checking functionality: it will recognize a corrupt or incomplete download,

which becomes more likely when downloading larger files. MD5 can be used to

store a one-way hash of a password, often with key stretching. Along with other

hash functions, it is also used in the field of electronic discovery, in order to

provide a unique identifier for each document that is exchanged during the legal

discovery process. This method can be used to replace the Bates stamp numbering

system that has been used for decades during the exchange of paper documents.

MD5 processes a variable-length message into a fixed-length output of 128

bits. The input message is broken up into chunks of 512-bit blocks (sixteen 32-bit

words); the message is padded so that its length is divisible by 512. The padding

works as follows: first a single bit, 1, is appended to the end of the message. This is

followed by as many zeros as are required to bring the length of the message up to

64 bits fewer than a multiple of 512. The remaining bits are filled up with 64 bits

representing the length of the original message, modulo 264. The main MD5

algorithm operates on a 128-bit state, divided into four 32-bit words, denoted A, B,

C and D. These are initialized to certain fixed constants. The main algorithm then

uses each 512-bit message block in turn to modify the state. The processing of a

message block consists of four similar stages, termed rounds; each round is

composed of 16 similar operations based on a non-linear function F, modular

addition, and left rotation.

Public-key cryptography

Public-key cryptography, also known as asymmetric cryptography, refers to

a cryptographic algorithm which requires two separate keys, one of which is secret

(or private) and one of which is public. Although different, the two parts of this

key pair are mathematically linked. The public key is used to encrypt plaintext or

to verify a digital signature; whereas the private key is used to decrypt ciphertext

or to create a digital signature. The term "asymmetric" stems from the use of

different keys to perform these opposite functions, each the inverse of the other –

as contrasted with conventional ("symmetric") cryptography which relies on the

same key to perform both. Public-key algorithms are based on mathematical

problems which currently admit no efficient solution that are inherent in certain

integer factorization, discrete logarithm, and elliptic curve relationships. It is

computationally easy for a user to generate his or her public and private key-pair

and to use them for encryption and decryption. The strength lies in the fact that it is

"impossible" (computationally infeasible) for a properly generated private key to

be determined from its corresponding public key. Thus the public key may be

published without compromising security, whereas the private key must not be

revealed to anyone not authorized to read messages or perform digital signatures.

Public key algorithms, unlike symmetric key algorithms, do not require a secure

initial exchange of one (or more) secret keys between the parties. Message

authentication involves processing a message with a private key to produce a

digital signature. Thereafter anyone can verify this signature by processing the

signature value with the signer's corresponding public key and comparing that

result with the message. Success confirms the message is unmodified since it was

signed, and – presuming the signer's private key has remained secret to the signer –

that the signer, and no one else, intentionally performed the signature operation. In

practice, typically only a hash or digest of the message, and not the message itself,

is encrypted as the signature.

Public-key encryption, in which a message is encrypted with a recipient's

public key. The message cannot be decrypted by anyone who does not possess the

matching private key, who is thus presumed to be the owner of that key and the

person associated with the public key. This is used in an attempt to ensure

confidentiality. Digital signatures, in which a message is signed with the sender's

private key and can be verified by anyone who has access to the sender's public

key. This verification proves that the sender had access to the private key, and

therefore is likely to be the person associated with the public key. This also ensures

that the message has not been tampered, as any manipulation of the message will

result in changes to the encoded message digest, which otherwise remains

unchanged between the sender and receiver. An analogy to public-key encryption

is that of a locked mail box with a mail slot. The mail slot is exposed and

accessible to the public – its location (the street address) is, in essence, the public

key. Anyone knowing the street address can go to the door and drop a written

message through the slot. However, only the person who possesses the key can

open the mailbox and read the message. An analogy for digital signatures is the

sealing of an envelope with a personal wax seal. The message can be opened by

anyone, but the presence of the unique seal authenticates the sender. A central

problem with the use of public-key cryptography is confidence/proof that a

particular public key is authentic, in that it is correct and belongs to the person or

entity claimed, and has not been tampered with or replaced by a malicious third

party. The usual approach to this problem is to use a public-key infrastructure

(PKI), in which one or more third parties – known as certificate authorities –

certify ownership of key pairs. PGP, in addition to being a certificate authority

structure, has used a scheme generally called the "web of trust", which

decentralizes such authentication of public keys by a central mechanism, and

substitutes individual endorsements of the link between user and public key. To

date, no fully satisfactory solution to this "public key authentication problem" has

been found.

Some encryption schemes can be proven secure on the basis of the presumed

difficulty of a mathematical problem, such as factoring the product of two large

primes or computing discrete logarithms. Note that "secure" here has a precise

mathematical meaning, and there are multiple different (meaningful) definitions of

what it means for an encryption scheme to be "secure". The "right" definition

depends on the context in which the scheme will be deployed. The most obvious

application of a public key encryption system is confidentiality – a message that a

sender encrypts using the recipient's public key can be decrypted only by the

recipient's paired private key. This assumes, of course, that no flaw is discovered in

the basic algorithm used. Another type of application in public-key cryptography is

that of digital signature schemes. Digital signature schemes can be used for sender

authentication and non-repudiation. In such a scheme, a user who wants to send a

message computes a digital signature for this message, and then sends this digital

signature (together with the message) to the intended receiver. Digital signature

schemes have the property that signatures can be computed only with the

knowledge of the correct private key. To verify that a message has been signed by

a user and has not been modified, the receiver needs to know only the

corresponding public key. In some cases (e.g. RSA), a single algorithm can be used

to both encrypt and create digital signatures. In other cases (e.g. DSA), each

algorithm can only be used for one specific purpose.

Objective

The scope of this project is to identify the neighbor positions nodes to

enhance lightweight NPV procedure that enables each node to acquire the locations

advertised by its neighbors, and assess their truthfulness and allows a node to

perform all verification procedures autonomously. This approach has no need for

lengthy interactions,, making our scheme suitable for both low- and high mobility

environments.

Problem Statement

The correctness of node locations is therefore an important issue in mobile

networks, and it becomes particularly challenging in the presence of adversaries

aiming at harming the system. In these cases, we need solutions that let nodes 1)

correctly establish their location in spite of attacks feeding false location

information, and 2) verify the positions of their neighbors, so as to detect

adversarial nodes announcing false locations.

Architecture Diagram

Abbreviations

UML Diagrams

Use Case Diagram

Activity Diagram

Literature Survey

GNSS-based Positioning: Attacks and Countermeasures

Concept

The contribution of this paper consists of three mechanisms that allow

receivers to detect forged GNSS messages and fake GNSS signals. The

countermeasures rely on in- formation the receiver obtained before the onset of an

attack, or more precisely, before the suspected onset of an attack. The mechanisms

rely on own (i) location information, calculated by GNSS navigation messages, (ii)

clock readings, without any re-synchronization with the help of the GNSS or any

other system, and (iii) received GNSS signal Doppler shift measurements. Based

on those different types of information, our mechanisms can detect if the received

GNSS signals and messages originate from adversarial devices. If so, location

information induced by the attack can be rejected and manipulation of the location-

aware functionality be avoided.

Algorithm

Location Inertial Test At the transition to alert mode, the node utilizes own

location information obtained from the PVT solution, to predict positions while in

attack mode. If those positions match the suspected as fraudulent PVT ones, the

receiver returns to normal mode. We consider two approaches for the location

prediction: (i) inertial sensors and (ii) Kalman filtering. Inertial sensors, i.e.,

altimeters, speedometers, odometers, can calculate the node (receiver) location

independently of the GNSS functionality.4 However, the accuracy of such (electro-

mechanical) sensors degrades with time. One example is the low-cost inertial

MEMS Crista IMU-15 sensor (Inertial Measurement Unit).

Doppler Shift Test (DST)

Based on the received GNSS signal Doppler shift, with respect to the nominal

transmitter frequency (ft = 1.575GHz), the receiver can predict future Doppler

Shift values. Once lock to GNSS signals is obtained again, predicted Doppler shift

values are compared to the ones calculated due to the received GNSS signal. If the

latter are different than the predicted ones beyond a threshold, the GNSS signal is

deemed adversarial and rejected. What makes this approach attractive is the

smooth changes of Doppler shift and the ability to predict it with low, essentially

constant errors over long periods of time. This in dire in contrast to the inertial test

based on location, whose error grows exponentially with time. The Doppler shift is

produced due to the relative motion of the satellite with respect to the receiver. The

satellite velocity is computed using ephemeris information and an orbital model

available at the receiver. The received frequency, fr, increases as the satellite

approaches and de- creases as it recedes from the receiver; it can be approximated

by the classical Doppler equation

Advantages

Secure mobile systems from location information manipulation via attack.

Secures GNSS Signals

Disadvantage

The effect of counter-measures that only partially limit the attack impact.

Increases Cost.

Secure Vehicular Communication Systems: Implementation, Performance, and

Research Challenges

Concept

In this paper component-based security architecture for VC systems, which

allows adding, replace, and reconfigure components (for example, substitute

cryptographic algorithms) throughout the life cycle of the vehicle is proposed. The

large number and the variety of vehicles have to be taken into account. Even for a

single car type, different production and equipment lines lead to many distinct

versions and variants. Nonetheless, it should be possible to integrate a security

system into all those platforms. In addition, the communication stack and security

measures might be designed by different teams or vendors; a situation that clearly

requires well-defined but still flexible interfaces. These reasons led to the

development of the so called “hooking architecture”, which introduces special

hooks at the interface between every layer of the vehicular communication system.

The hooking architecture introduces an event-callback mechanism into the

communication stack which allows adding security measures without the need to

change the entire communication system. The security system in a vehicle has to

fulfill real-time or near real-time requirements. For the underlying cryptographic

primitives, this implies optimized cryptographic hardware, in order to guarantee

the near real-time performance.

Algorithm

Baseline Architecture Deployment View

The SeVeCom baseline architecture addresses different aspects, such as

secure communication protocols, privacy protection, and in-vehicle security. As

the design and development of VC protocols, system architectures, and security

mechanisms is an ongoing process, only few parts of the overall system are yet

finished or standardized. As a result, a VC security system cannot be based on a

fixed platform but instead has to be flexible, with the possibility to adapt to future

VC applications or new VC technologies. To achieve the required flexibility, the

SeVeCom baseline architecture consists of modules, which are responsible for a

certain system aspect, such as identity management. The modules, in turn, are

composed of multiple components each handling a specific task. For instance, the

Secure Communication Module is responsible for implementing protocols for

secure communication and consists of several components, each of them

implementing a single protocol. Components are instantiated only when their use is

required by certain applications, and they use well-defined interfaces to

communicate with other components. Thus, they can be exchanged by more recent

versions, without other modules being affected. It instantiates and configures the

components of all other security modules and establishes the connection to the

Cryptographic Support Module. To cope with different situations, the Security

Manager maintains different policy sets.

Advantages

Identifies pertinent threats and models for adversaries

Enhances security and privacy for communication protocols.

Robust

Disadvantages

lets a node assess whether another node is an actual neighbor

does not verify the location

A Graph Theoretic Framework for Preventing the Wormhole Attack in Wireless

Ad Hoc Networks

Concept

These papers presents a graph theoretic framework for modeling of the

wormhole attack and state the necessary and sufficient conditions for any candidate

solution to prevent such an attack. We show that any previously proposed methods

or future solutions have to satisfy our conditions in order to prevent wormholes. In

addition, we also propose a cryptographic mechanism based on keys only known

within each neighborhood, which we call local broadcast keys (LBKs), in order to

secure the network from wormhole attacks and show that our solution satisfies the

conditions of the graph theoretic framework. We present a centralized method for

establishing LBKs, when the location of all the nodes is known to a central

authority (base station). Furthermore, we propose a decentralized mechanism for

LBK establishment that defends against wormholes with a probability very close to

unity. This solution does not require any time synchronization or highly accurate

clocks. In addition, our method requires only a small fraction of the network nodes

to know their location. Finally, our approach is based on symmetric cryptography

rather than expensive asymmetric cryptography and hence is computationally

efficient, while it requires each node to broadcast only a small number of messages

thus having a small communication overhead. Due to its efficiency, our method is

applicable to ad hoc networks with very stringent resource constraints, such as

wireless sensor networks.

Algorithms

Guard ID authentication: The use of a global symmetric key K0 does not provide

any authentication on the source of the message. Hence, any guard or node holding

the global key can broadcast fractional keys encrypted with K0. Though we have

assumed that the global symmetric key K0 is not compromised and that network

entities do not operate maliciously, in order to allow nodes to authenticate the

guards within one-hop, we provide a lightweight authentication mechanism4. Our

scheme is based on efficient one-way hash chains that have also been used

extensively in broadcast authentication protocols. In addition, due to the one-way

property of the hash chain, it is computationally infeasible for an adversary to

derive values of the hash chain that have not been already published by the guard

[26]. Each node is preloaded with a table containing the Id of each guard and the

corresponding hash value Hn(PWi). For a network with 200 guards, we need 8 bits

to represent node Ids. In addition, hash functions such as SHA-1 [45] have a 128-

bit output.

Advantages

Locating the origin point of the attack

Detection of the wormhole attack

Disadvantages

Does not securely determine the neighbor positions and time reference.

HiRLoc: High-resolution Robust Localization for Wireless Sensor Networks

Concept

A novel localization scheme for WSN called High resolution Range-

independent Localization (HiRLoc) that allows sensors to passively determine their

location with high accuracy. The increased localization accuracy is the result of

combination of multiple localization information over a short time period, and does

not come at the expense of increased hardware complexity or deployment of

reference points with higher density. Since our method does not perform any range

measurements to estimate the sensors location, it is not susceptible to any range

measurement alteration attacks. Furthermore, sensors do not rely on other sensors

to infer their location and hence, the robustness of our localization method does not

rely on the easily tampered sensor devices. Finally, we show that our method is

robust against well-known security threats in WSN, such as the wormhole attack,

the Sybil attack and compromise of network entities.

Algorithm

There are two different ways of computing the region of intersection. We can

collect all beacons over several transmission rounds and compute the intersection

of the all sector areas or estimate ROI after every round of transmissions and

intersect it with the previous estimate of the ROI: We will refer to the HiRLoc-I

and HiRLoc-II algorithm.

HIRLOC-I ALGORITHM

HiRLoc-I algorithm is Computing the intersection of all sector areas.

HiRLoc the estimation of the ROI is computed by collecting all beacons

transmitted by each locator over time, intersecting all sectors of each locator and

then intersecting the outcome.

Step 1: Initial estimate of the ROI.

Step 2: Beacon collection.

Option A: Antenna orientation variation.

Option B: Communication range variation.

Option C: Combination of options A, B.

Step 3: Determination of the ROI.

HIRLOC-II ALGORITHM

HiRLoc-II is computing the sector intersection at each transmission round.

The sensor computes the ROI by intersecting all collected information at each

transmission round. HiRLoc-II can be seen as an iterative application of SeRLoc ,

with sensors using SeRLoc at each transmission round to estimate ROI and

intersecting it with the previous one.

Advantages

HiRLoc localizes sensors with significantly higher accuracy.

Requiring fewer hardware resources.

HiRLoc allows the robust location computation even in the presence of

security threats in WSN.

The wormhole attack, the Sybil attack and compromise of network entities.

Disadvantages

Enabling nodes of a WSN to compute a high-resolution estimate of their

location even in the presence of malicious adversaries.

Secure localization for wireless sensor networks is not context of,

(a) Decentralized and scalable implementation,

(b) Resource efficiency in computation, communication and storage,

(c) range-independence,

(d) Robustness against security threats in WSN.

Secure Neighbor Discovery in Mobile Ad Hoc Networks

Concept

To address the aforementioned challenges, we present Mobile Secure

Neighbor Discovery (MSND), which allows neighbors to verify that they are

speaking directly with each other. A wormhole can be detected due to the fact that

the path traveled by a ranging signal varies from expected values when a wormhole

is present. Instead of traveling directly to the remote node, the ranging signal must

travel to one end of the wormhole, transit the wormhole, and then exit to arrive at

the destination node. In the case of a static network, this variation is difficult to

detect because, for a single node, it is constant.

• A protocol (MSND) for detecting the presence of wormholes when mobile nodes

participate.

• Security analysis and correctness of MSND.

• Performance evaluation through simulations, demonstrating accurate wormhole

detection with low false negatives.

• A real system evaluation employing Epic motes and iCreate robot hardware,

demonstrating the performance of our proposed solution.

Algorithm

MSND Protocol.

Verification.

MSND Protocol

MSND executes in two phases. First, NR ranging operations are conducted

and the resulting ranges and travel distances are sent to Verification. The execution

of MSND between two mobile nodes in a network with no wormhole. Node A

initiates ranging operations. Node B will become a neighbor of A if it can be

verified. A key requirement of the ranging phase is that each ranging node must

travel along a describable path. For the purposes of this paper, nodes move in

straight lines until either enough ranges are collected or it is no longer possible to

range. Ranging operations stop before completion of the protocol when nodes are

no longer in contact or when a node is forced to turn.

Verification

Verification uses preliminary checks, metric multidimensional scaling

(MDS) and knowledge of node movement to detect distortions caused by a

wormhole. Verification is used to analyze ranges and traveled distances to

determine if a wormhole has affected the results. Successful verification confirms

that the two nodes are neighbors.

Verification begins with preliminary checks that include a check for ranges

that are too long, adjoining ranges whose length differs by more than the combined

distances traveled by the participating nodes, and degenerate configurations.

Successful preliminary checks are followed by a loop that performs distance

analysis using MDS and a test of the fit of the resulting coordinates .The output is

analyzed and the best two outcomes are used to make a decision about the

presence/absence of a wormhole.

Advantages

The ability to securely determine valid neighbors is an important part of

many network functions.

Wormholes failure to protect neighbor discovery could lead to information

disclosure, incorrect localization, routing problems, and adversary control of

the network at any time.

MSND leverages graph rigidity to aid in the verification of network

neighbors.

Disadvantages

Neighbor discovery fails, communications and protocols performance

deteriorate.

In networks affected by relay attacks, also known as wormholes, the failure

may be more subtle.

The wormhole may selectively deny or degrade communications.

Existing System

An autonomous sensor is proposed in the existing system that allows nodes

to validate the position of their neighbors through local observations only. This is

performed by checking whether subsequent positions announced by one neighbor

draw a movement over time that is physically possible. The approach forces a node

to collect several data on its neighbor movements before a decision can be taken,

making the solution unfit to situations where the location information is to be

obtained and verified in a short time span.

Demerits:

Adversary can fool the protocol by simply announcing false positions.

These sensor can be attacked by using fake id nodes.

Another drawback of the presented solution is that each node has only a

local view that might not be enough to reliably identify all position faking

node

Proposed System

In this paper fully distributed cooperative scheme for NPV, which enables a

node, hereinafter called the verifier, to discover and verify the position of its

communication neighbors is proposed. This paper deals with a mobile ad hoc

network, where a pervasive infrastructure is not present, and the location data must

be obtained through node-to-node communication. Such a scenario is of particular

interest since it leaves the door open for adversarial nodes to misuse or disrupt the

location-based services. The proposed approach is designed for spontaneous ad hoc

environments, and, as such, it does not rely on the presence of a trusted

infrastructure or of a priori trustworthy nodes and it also leverages cooperation but

allows a node to perform all verification procedures autonomously.

A fully distributed cooperative scheme for NPV, which enables a node,

hereinafter called the verifier, to discover and verify the position of its

communication neighbors. For clarity, here we summarize the principles of the

protocol as well as the gist of its resilience analysis. Detailed discussions of

message format, verification tests, and protocol resilience are provided in Sections

5 and 6. A verifier, S, can initiate the protocol at any time instant, by triggering the

4-step message exchange depicted in Fig. 1, within its 1-hop neighborhood. The

aim of the message exchange is to let S collect information it can use to compute

distances between any pair of its communication neighbors. To that end, POLL

and REPLY messages are first broadcasted by S and its neighbors, respectively.

These messages are anonymous and take advantage of the broadcast nature of the

wireless medium, allowing nodes to record reciprocal timing information without

disclosing their identities. Then, after a REVEAL broadcast by the verifier, nodes

disclose to S, through secure and authenticated REPORT messages, their identities

as well as the anonymous timing information they collected. The verifier S uses

such data to match timings and identities; then, it uses the timings to perform ToF-

based ranging and compute distances between all pairs of communicating nodes in

its neighborhood. Once S has derived such distances, it runs several position

verification tests in order to classify each candidate neighbor as either: 1. Verified,

i.e., a node the verifier deems to be at the claimed position; 2. Faulty, i.e., a node

the verifier deems to have announced an incorrect position; 3. Unverifiable, i.e., a

node the verifier cannot prove to be either correct or faulty, due to insufficient

information. Clearly, the verification tests aim at avoiding false negatives (i.e.,

adversaries announcing fake positions that are deemed verified) and false positives

(i.e., correct nodes whose positions are deemed faulty), as well as at minimizing

the number of unverifiable nodes. We remark that our NPV scheme does not target

the creation of a consistent “map” of neighborhood relations throughout an

ephemeral network: rather, it allows the verifier to independently classify its

neighbors.There, M is a malicious node announcing a false location M0, so as to

fraudulently gain some advantage over other nodes. The figure portrays the actual

network topology with black edges, while the modified topology, induced by the

fake position announced by M, is shown with gray edges. It is evident that the

displacement of M to M0 causes its edges with the other nodes to rotate, which, in

turn, forces edge lengths to change as well. The tests thus look for discrepancies in

the node distance information to identify incorrect node positions.

Merits:

robust against independent and colluding adversaries

lightweight, as it generates low overhead traffic

does not require any infrastructure or a priori trusted neighbors

suitable for both low and high mobile environments

Data Flow Diagram

System Requirements

Hardware

System : Dual core Processor

Hard Disk : 80 GB

Monitor : 15 VGA color

Mouse : Logitech.

RAM : 1 GB.

Software

Operating System : Windows XP.

Language : Java.

IDE : Net Beans 6.9.1.

Database : MySQL.

Modules

Network Construction

Anonymous communication

Position Verification

Cross verification and Multilateration verification

Modules Description

Network Construction

The entire set of interdependent relationships is examined. Network with

fixed number of nodes or agents. Every agent has a utility function and their

possible actions are to form links between each other. Usually forming or

maintaining links has a cost and having connected to other nodes has benefits. The

question of this method is that given some initial setting and parameter values what

network structure will emerge. One way of network formation modeling,

something usually pursued by physicists, or biologists, is to start from a small

network - or even a single node; and then design a usually randomized rule on how

newly-arrived nodes form their links. The aim is to see what properties the network

may have when it grows in size. In this way, researchers try to reproduce

properties common in most real networks, such as the small world network

property or the scale-free network property.

Anonymous communication

The verifier starts the protocol by broadcasting a POLL whose transmission time

tS it stores locally (Algorithm 1, lines 2-3). The POLL is anonymous, since 1) it

does not carry the identity of the verifier, 2) it is transmitted employing a fresh,

software-generated MAC address, and 3) it contains a public key K0S taken from

S’s pool of anonymous one-time use keys that do not allow neighbors to map the

key onto a specific node. We stress that keeping the identity of the verifier hidden

is important in order to make our NPV robust to attacks (see the protocol analysis

in Section 6). Since a source address has to be included in the MAC-layer header

of the message, a fresh, software-generated MAC address is needed; note that this

is considered a part of emerging cooperative systems [2], [25]. Including a one-

time key in the POLL also ensures that the message is fresh (i.e., the key acts as a

nonce).

A communication neighbor X 2 INS that receives the POLL stores its

reception time tSX, and extracts a random wait interval TX 2 ½0; (Algorithm 2,

lines 2-4). After TX has elapsed, X broadcasts an anonymous REPLY message

using a fresh MAC address, and locally records its transmission time tX

(Algorithm 2, lines 5-9). For implementation feasibility, the physical layer

transmission time cannot be stamped on the REPLY, but it is stored by X for later

use. The REPLY contains some information encrypted with S s public key (K0S ),

specifically the POLL reception time and a nonce X used

to tie the REPLY to the next message sent by X: we refer to these data as X’s

commitment, Cj X (Algorithm 2, line 7). The hash hK0 S , derived from the public

key of the verifier, K0S , is also included to bind POLL and REPLY belonging to

the same message exchange. Upon reception of a REPLY from a neighbor X, the

verifier S stores the reception time tXS and the commitment Cj X (Algorithm 1,

lines 4-5). When a different neighbor of S, e.g., Y , Y 2 INS \ INX, broadcasts a

REPLY too, X stores the reception time tYX and the commitment Cj Y (Algorithm

2, lines 10-11). Since REPLY messages are anonymous, a node records all

commitments it receives without knowing their originators.

Position Verification

Then the verifier broadcasts a REVEAL message using its real MAC address

accounts for the propagation and contention lag of REPLY messages scheduled at

time. Once the REPORT message is broadcast and the identity of the verifier is

known to the neighbor nodes. Once the message exchange is concluded, S can

decrypt the received data and acquire the position of all neighbors that participated

in the protocol. The verifier S also knows the transmission time of its POLL and

learns that of all subsequent REPLY messages, as well as the corresponding

reception times recorded by the recipients of such broadcasts. Applying a ToF-

based technique, S thus computes its distance from each communication neighbor,

as well as the distances between all neighbor pairs sharing a link. More precisely,

by denoting with c the speed of light, the verifier computes, for any

communicating pair from the timing information related to the broadcast message

sent by X from the information related to the broadcast message by Y .Once such

distances have been computed, S can run the following three verification tests The

Direct Symmetry Test (DST), The Cross-Symmetry Test and The Multilateration

Test. The DST, S verifies the direct links with its communication neighbors. To

this end, it checks whether reciprocal ToF-derived distances are consistent 1) with

each other, 2) with the position advertised by the neighbor, and 3) with a proximity

range R. The latter corresponds to the maximum nominal transmission range, and

upper bounds the distance at which two nodes can communicate. More

specifically, the first check verifies that the distances, obtained from ranging, do

not differ by more than twice the ranging error plus a tolerance value, accounting

for node spatial movements during the protocol execution. The second check

verifies that the position advertised by the neighbor is consistent with such

distances, within an error margin.

Cross verification and Mulilateration verification

In Algorithm 4, implements cross verifications, i.e., it checks on the

information mutually gathered by each pair of communication neighbors. The CST

ignores nodes already declared as faulty by the DST (Algorithm 4, line 5) and only

considers nodes that proved to be communication neighbors between each other,

i.e., for which ToF-derived mutual distances are available (Algorithm 4, line 6).

However, pairs of neighbors declaring collinear positions with respect to S are not

taken into account (Algorithm 4, line 7, where lineðpX; pY Þ is the line passing by

points pX and pY ). As shown in the next section, this choice makes our NPV

robust to attacks in particular situations. For all other pairs ðX; Y Þ, the CST

verifies the symmetry of the reciprocal distances (Algorithm 4, line 9), their

consistency with the positions declared by the nodes (Algorithm 4, line 10), and

with the proximity range (Algorithm 4, line 11). For each neighbor X, S maintains

a link counter lX and a mismatch counter mX. The former is incremented at every

new crosscheck on X, and records the number of links between X and other

neighbors of S (Algorithm 4, line 8). The latter is incremented every time at least

one of the cross-checks on distance and position fails and identifies the potential

for X being faulty.

Once all neighbor pairs have been processed, a node X is added to the

unverifiable set UUS if it shares less than two non-collinear neighbors with S

(Algorithm 4, line 14). Indeed, in this case, the information available on the node is

considered to be insufficient to tag the node as verified or faulty (see Section 6 for

details). Otherwise, if S and X have two or more noncollinear common neighbors,

X is declared as faulty, unverifiable, or conditionally verified, depending on the

percentage of mismatches in the crosschecks it was involved in (Algorithm 4, lines

15-18). Specifically, X is added to IFS or UUS, depending on whether the ratio of

the number of mismatches to the number of checks is greater or equal to a

threshold

. If such a ratio is less than

, X is added to a temporary set WWS for conditionally verified nodes.

MLT, in Algorithm 5, ignores nodes already tagged as faulty or unverifiable and

looks for suspect neighbors in WWS. For each neighbor X that did not notify about

a link reported by another node Y , with X; Y 2 WWS, a curve LXðS; Y Þ is

computed and added to the set ILX (Algorithm 5, lines 5-7). Such a curve is the

locus of points that can generate a transmission whose Time Difference of Arrival

(TDoA) at S and Y matches that measured by the two nodes, i.e., jtXS

tXY j. It is easy to verify that such a curve is a

hyperbola, with foci in pS and pY , and passing through the actual position of X.

Once all couples of nodes in WWS have been checked, each node X for which two

or more unnotified links, hence two or more hyperbolas in ILX, exist is considered

as suspect (Algorithm 5, line 9). In such a case, S exploits the hyperbolae in ILX to

multilaterate X s position, referred to as pML X , similarly to what is done in [17]

(Algorithm 5, line 10). Note that ILX must include at least two hyperbolae for S to

be able to compute pML X , and this implies the presence of at least two shared

neighbors between S and X. pML X is then compared with the position advertised

by X, pX (Algorithm 5, line 11). If the difference exceeds an error margin 2p, X is

moved to the faulty set IFS. At the end of the test, all nodes still in WWS are

tagged as verified and moved to jVS

Algorithms Used

Message Exchange Protocol Verifier

Message exchange protocol: any neighbor

Direct Symmetry Test (DST)

Cross Symmetry Test

Multilateration Test

FEASIBILITY STUDY

Feasibility studies aim to objectively and rationally uncover the strengths and

weaknesses of the existing business or proposed venture, opportunities and threats

as presented by the environment, the resources required to carry through, and

ultimately the prospects for success. In its simplest term, the two criteria to judge

feasibility are cost required and value to be attained. As such, a well-designed

feasibility study should provide a historical background of the business or project,

description of the product or service, accounting statements, details of the

operations and management, marketing research and policies, financial data, legal

requirements and tax obligations. Generally, feasibility studies precede technical

development and project implementation.

Economical Feasibility

This study is carried out to check the economic impact that the system will

have on the organization. The amount of fund that the company can pour into the

research and development of the system is limited. The expenditures must be

justified. Thus the developed system as well within the budget and this was

achieved because most of the technologies used are freely available. Only the

customized products had to be purchased.

Technical Feasibility

Technical feasibility study is carried out to check the technical feasibility,

that is, the technical requirements of the system. Any system developed must not

have a high demand on the available technical resources. This will lead to high

demands on the available technical resources. This will lead to high demands being

placed on the client. The developed system must have a modest requirement, as

only minimal or null changes are required for implementing this system.

Operational Feasibility

The aspect of study is to check the level of acceptance of the system by the

user. This includes the process of training the user to use the system efficiently.

The user must not feel threatened by the system, instead must accept it as a

necessity. The level of acceptance by the users solely depends on the methods that

are employed to educate the user about the system and to make him familiar with

it. His level of confidence must be raised so that he is also able to make some

constructive criticism, which is welcomed, as he is the final user of the system.

Software Specification

Os -Windows Xp

Windows XP is nothing but an entire line of operating systems, which has

been initiated and developed, by Microsoft and it can be used on several computer

systems that are general-purpose, including desktops at home and business, media

centers and notebook computers. Windows XP was codenamed "Whistler", after

Whistler British Columbia, and the letters "XP" stand for experience. The two

words were combined together to form "Windows XP". Windows XP was released

in the year 2001 on October 25 and by January 2006 above 350 million copies was

being used all over. People used Windows 2000 and Windows Me before

Microsoft has developed Windows XP. Windows XP is the first operating system

which is consumer based produced by Microsoft, which is to be built on the

Windows NT kernel and architecture. Microsoft Windows XP is the most

commonly used and supported operating system today. Windows Vista, which is

now a successor of Windows XP, was released worldwide on January 30, 2007.

The minimum requirements specified by Microsoft for Windows XP are 233MHz

processor, 1.5GB of available hard drive space, an SVGA-capable video card and

64MB of RAM. Although it is strongly recommended by UITS that any system

running XP should have a CPU which is faster than 400MHz and at least 256MB

of RAM.

Front-End:

Java:-

Java is a programming language expressly designed for use in the distributed

environment of the Internet. It was designed to have the "look and feel" of the C++

language, but it is simpler to use than C++ and enforces an object-oriented

programming model. Java can be used to create complete applications that may run

on a single computer or be distributed among servers and clients in a network. It

can also be used to build a small application module or applet for use as part of a

Web page. Applets make it possible for a Web page user to interact with the page.

The major characteristics of Java are:-

The programs you create are portable in a network. Your source program is

compiled into what Java calls byte code, which can be run anywhere in a network

on a server or client. That has a Java virtual machine. The Java virtual machine

interprets the byte code into code that will run on the real computer hardware. This

means that individual computer platform differences such as instruction lengths

can be recognized and accommodated locally just as the program is being

executed. Platform-specific versions of your program are no longer needed.

The code is robust, here meaning that, unlike programs written in C++ and

perhaps some other languages, the Java objects can contain no references to data

external to themselves or other known objects. This ensures that an instruction

cannot contain the address of data storage in another application or in the operating

system itself, either of which would cause the program and perhaps the operating

system itself to terminate or "crash." The Java virtual machine makes a number of

checks on each object to ensure integrity.

Java is object-oriented, which means that, among other characteristics, an

object can take advantage of being part of a class of objects and inherit code that is

common to the class. Objects are thought of as "nouns" that a user might relate to

rather than the traditional procedural "verbs." A method can be thought of as one

of the object's capabilities or behaviors.

Java was introduced by Sun Microsystems in 1995 and instantly created a

new sense of the interactive possibilities of the Web. Both of the major Web

browsers include a Java virtual machine. Almost all major operating system

developers (IBM, Microsoft, and others) have added Java compilers as part of their

product offerings.The Java virtual machine includes an optional just-in-time

compiler that dynamically compiles byte code into executable code as an

alternative to interpreting one byte code instruction at a time. In many cases, the

dynamic JIT compilation is faster than the virtual machine interpretation.

IDE:

NetBeans 6.9.1:

The NetBeans Platform is a reusable framework for simplifying the

development of Java Swing desktop applications. The NetBeans IDE bundle for

Java SE contains what is needed to start developing NetBeans plugins and

NetBeans 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)

• NetBeans Visual Library

• Integrated Development Tools

Netbeans IDE is a free, open-source, cross-platform IDE with built-in-support

for Java Programming Language.

Integrated modules

NetBeans have three Integrated Modules

• NetBeans Profiler

• GUI design tool

• NetBeans JavaScript editor

Back-End

MySQL:-

MySQL is the world's most used relational database management system

(RDBMS) [4] that runs as a server providing multi-user access to a number of

databases. It is named after developer Michael Widenius' daughter, my. [5] The

SQL phrase stands for Structured Query Language.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

MySQLAB, 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, Word Press, 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,

Facebook, and Twitter.

MySQL is a database management system.

A database is a structured collection of data. It may be anything from a simple

shopping list to a picture gallery or the vast amounts of information in a corporate

network. To add, access, and process data stored in a computer database, you need

a database management system such as MySQL Server. Since computers are very

good at handling large amounts of data, database management systems play a

central role in computing, as standalone utilities, or as parts of other applications.

MySQL is a relational database management system.

A relational database stores data in separate tables rather than putting all the data

in one big storeroom. This adds speed and flexibility. The SQL part of “MySQL”

stands for “Structured Query Language.” SQL is the most common standardized

language used to access databases and is defined by the ANSI/ISO SQL Standard.

The SQL standard has been evolving since 1986 and several versions exist. In this

manual, “SQL-92” refers to the standard released in 1992, “SQL: 1999” refers to

the standard released in 1999, and “SQL: 2003” refers to the current version of the

standard. We use the phrase “the SQL standard” to mean the current version of the

SQL Standard at any time.

TESTING

SYSTEM TESTING

System testing is the stage of implementation, which aimed at ensuring that

the system works accurately and efficiently before the live operation commences.

Testing is the process of executing a program with the intent of finding an error. A

good test case is one that has a high probability of finding a yet undiscovered error.

A successful test is one that answers a yet undiscovered error.

UNIT TESTING

Unit testing is the testing of each module and the integration of the overall

system is done. Unit testing becomes verification efforts on the smallest unit of

software design in the module. This is also known as ‘module testing’. The

modules of the system are tested separately. This testing is carried out during the

programming itself.

INTEGRATION TESTING

Data can be lost across an interface, one module can have an adverse effect on

the other sub function, when combined, may not produce the desired major

function. Integrated testing is systematic testing that can be done with sample

data. The need for the integrated test is to find the overall system performance.

There are two types of integration testing. They are:

i) Top-down integration testing.

ii) Bottom-up integration testing.

WHITE BOX TESTING

White Box testing is a test case design method that uses the control structure of

the procedural design to drive cases. Using the white box testing methods, we

derived test cases that guarantee that all independent paths within a module have

been exercised at least once.

BLACK BOX TESTING

Black box testing is done to find incorrect or missing function

Interface error

Errors in external database access

Performance errors

Initialization and termination errors

In ‘functional testing’, is performed to validate an application conforms to its

specifications of correctly performs all its required functions. So this testing is also

called ‘black box testing’. It tests the external behavior of the system. Here the

engineered product can be tested knowing the specified function that a product has

been designed to perform, tests can be conducted to demonstrate that each function

is fully operational.

Conclusion

We presented a distributed solution for NPV, which allows any node in a

mobile ad hoc network to verify the position of its communication neighbors

without relying on a priori trustworthy nodes. Our analysis showed that our

protocol is very robust to attacks by independent as well as colluding adversaries,

even when they have perfect knowledge of the neighborhood of the verifier.

Simulation results confirm that our solution is effective in identifying nodes

advertising false positions, while keeping the probability of false positives low.

Only an overwhelming presence of colluding adversaries in the neighborhood of

the verifier, or the unlikely presence of fully collinear network topologies, can

degrade the effectiveness of our NPV.

Future Enhancement

Future work will aim at integrating the NPV protocol in higher layer

protocols, as well as at extending it to a proactive paradigm, useful in presence of

applications that need each node to constantly verify the position of its neighbors.

References

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