Mobile IP is an Internet Engineering Task Force (IETF) standard communications protocol. In many applications (e.g., VPN, VoIP) sudden changes in network connectivity and IP address can cause problems. Mobile IP protocols were designed to support seamless and continuous internet connectivity. Mobile IP was developed as a means for transparently dealing with problems of mobile users. It enables hosts to stay connected to the Internet regardless of their location and to be tracked without needing to change their IP address. It requires no modifications to IP addresses or IP address format. It has no geographical limitations.
Mobile IP Entities
• Mobile Node (MN)It is the entity that may change its point of attachment from network to network in the Internet. MN assigned a permanent IP called its home address to which other hosts send packets regardless of MN’s location. Since this IP doesn’t change it can be used by long-lived applications as MN’s location changes.

• Home Agent (HA): This is a router with additional functionality. It is Located on home network of MN. HA does mobility binding of MN’s IP with its COA (care of address). It forwards packets to appropriate network when MN is away.

• Foreign Agent (FA): it is a router with enhanced functionality. If MN is away from HA, it uses an FA to send/receive data to/from HA. FA advertises itself periodically. It forwards MN’s registration request. FA de-encapsulates messages for delivery to MN.

• Care-of-address (COA): It is an address which identifies MN’s current location. This address is sent by FA to HA when MN attaches. It is usually the IP address of the FA.

• Correspondent Node (CN): CN is the end host to which MN is corresponding (e.g. a web server).

Home agent and foreign agent

Mobile IP Operations
o A MN listens for agent advertisement and then initiates registration. If responding agent is the HA, then mobile IP is not necessary.
o After receiving the registration request from a MN, the HA acknowledges and registration is complete. Registration happens as often as MN changes networks.
o HA intercepts all packets destined for MN. This is simple unless sending application is on or near the same network as the MN. HA masquerades as MN. There is also a de-registration process with HA if an MN returns home.
o HA then encapsulates all packets addressed to MN and forwards them to FA
o FA de-encapsulates all packets addressed to MN and forwards them via hardware address (learned as part of registration process)

Enhancements to the Mobile IP standard, such as Mobile IPv6 and Hierarchical Mobile IPv6 (HMIPv6), were developed to advance mobile communications by making the processes involved less cumbersome.

Big data is a buzzword, or catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Despite these problems, big data has the potential to help companies improve operations and make faster, more intelligent decisions.
An example of big data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on). The data is typically loosely structured data that is often incomplete and inaccessible.

History and Consideration
While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.
Complexity. Today’s data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.
Advantages of Big data:
1) Cost reductions
2) Time reductions
3) New product development and optimized offerings
4) Smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

Forensic means to use in court. Computer forensics is the collection, preservation, analysis, extraction, documentation and in some cases, the court presentation of computer-related evidence which has either been generated by a computer or has been stored on computer media.
Traditional Forensic v/s Computer Forensic
The difference between traditional forensics workers and the computer forensics folks is that the traditionalists tend to work in two locations only. The initial crime scene but the majority of the investigation goes on back at the lab. The computer forensics investigators may have to take on their analyses wherever the equipment is located or found rather than just in the pristine environment and the products that are used by investigators come from a market-driven private sector. Traditional forensics investigators are expected to interpret their findings and draw conclusions accordingly whereas computer forensics investigators must only produce direct information and data that will have significance in the case they are investigating.
Component steps for Computer Forensics:
Make a Forensic Image
• Requires Extensive Knowledge of Computer Hardware and Software, Especially File Systems and Operating Systems.
• Requires Special “Forensics” Hardware and Software
• Requires Knowledge of Proper Evidence Handling.

Create Indexes and setup “case”
Access Data Forensic Toolkit (FTK)
• Implements “Hashing” concept.
• Hashing is done using hash functions which is a well-defined procedure or mathematical function for turning some kind of data into a comparatively small integer.
• The values returned by hash function are called hash values

Look for evidence within the image
• View Emails, Documents, Graphics etc.
• Searching for Keywords
• Bookmark relevant material for inclusion into report
• Good investigation skills needed to interview the client to get background material needed to focus the CF investigation.

Generate CF Report
• Usually in HTML format
• Can be printed or on CD-ROM
• Basis for Investigation Report, Deposition, Affidavits, and Testimony.
• CF Report often supplemented with other investigation methods (Online Databases, Email / Phone Interviews)

Forensic Software:
Byte Back, Drive Spy, EnCase , Forensic Tool Kit (FTK), Hash Keeper, Ilook, Maresware, Password Recovery Toolkit (PRTK), Safeback, Thumbs Plus

Pros and cons of Computer Forensics
Pros:
• Searching and analyzing of a mountain of data quickly and efficiently.
• Valuable data which has been deleted and lost by offenders can be retrieved. It may become substantial evidence in court.
• Preservation of evidence.
• Ethical process
Cons:
• Retrieval of data is costly.
• Evidence on the other hand can only be captured once.
• Privacy concerns.
• Data corruption

With computers becoming more and more involved in our everyday lives, both socially and professionally, there is a need for computer forensics. This field will enable crucial electronic evidence to be found, whether it was deleted, lost, hidden, or damaged and used to prosecute individuals that believe they have successfully beaten the system.

Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. One starts at the root and explores as far as possible along each branch before backtracking.
A version of depth-first search was investigated in the 19th century by French mathematician Charles Pierre Trémaux as a strategy for solving mazes.

The order of Excution of nodes are as :1, 2, 3, 4, 5, 6, 7, 8, 9, 10.
Pseudo code:
Input: A graph G and a vertex v of G
Output: All vertices reachable from v labeled as discovered
A recursive implementation of DFS:
1 procedure DFS(G,v):
2 label v as discovered
3 for all edges from v to w in G.adjacentEdges(v) do
4 if vertex w is not labeled as discovered then
5 recursively call DFS(G,w)
A non-recursive implementation of DFS:
1 procedure DFS-iterative(G,v):
2 let S be a stack
3 S.push(v)
4 while S is not empty
5 v = S.pop()
6 if v is not labeled as discovered:
7 label v as discovered
8 for all edges from v to w in G.adjacentEdges(v) do
9 S.push(w)

Applications: There are various applications of DFS.
• Finding connected components.
• Detecting cycle in a graph.
• Finding connected components.
• Finding the bridges of a graph.
• Generating words in order to plot the Limit Set of a Group.
• Finding strongly connected components.
• Planarity testing.
• Solving puzzles with only one solution, such as mazes. (DFS can be adapted to find all solutions to a maze by only including nodes on the current path in the visited set.)
• Maze generation may use a randomized depth-first search.
• Finding biconnectivity in graphs.

Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some subjective node of a graph, sometimes referred to as a ‘search key’) and explores the neighbor nodes first, before moving to the next level neighbors.
BFS was invented in the late 1950s by E. F. Moore, who used it to find the shortest path out of a maze, and discovered independently by C. Y. Lee as a wire routing algorithm.

So traversal is: a, b, c, d, e, f, g, h, i , j, k.
Pseudocode:
Input: A graph Graph and a starting vertex root of G
Output: All vertices reachable from root labeled as explored.
A non-recursive implementation of breadth-first search:
1 Breadth-First-Search (Graph, root):
2
3 for each node n in Graph:
4 n.distance = INFINITY
5 n.parent = NIL
6
7 create empty queue Q
8
9 root.distance = 0
10 Q.enqueue(root)
11
12 while Q is not empty:
13
14 current = Q.dequeue()
15
16 for each node n that is adjacent to current:
17 if n.distance == INFINITY:
18 n.distance = current.distance + 1
19 n.parent = current
20 Q.enqueue(n)
Applications:
Breadth-first search can be used to solve many problems in graph theory, for example:
• Shortest Path and Minimum Spanning Tree for unweighted graph.
• Peer to Peer Networks.
• Social Networking Websites.
• In Garbage Collection.
• Path Finding.
• Finding all nodes within one connected component.

Li-Fi refers to visible light communications (VLC) technology, which delivers high-speed, bidirectional, networked mobile communications in a manner similar to Wi-Fi. It promises huge speed advantages, as well as more-secure communications and reduced device interference.
The term was coined by German physicist Harald Haas during a TED Talk when he outlined the idea of using lightbulbs as wireless routers. That address was delivered four years ago, and many people speculated that, like a lot of apparent revolutionary breakthroughs, Li-Fi would go the way of other “next big things” and not come to fruition. A year after his TED Talk, though, Haas, a professor of mobile communications at the University of Edinburgh, created pureLiFi with a group of people who had been researching the technology since 2008. The company has claimed to be the “recognized leaders in Li-Fi technology” and has already produced two products.
Working of LIFI
When a constant current is applied to an LED [light-emitting-diode] lightbulb, a constant stream of photons is emitted from the bulb which is observed as visible light. If the current is varied slowly, the output intensity of the light dims up and down. Because LED bulbs are semiconductor devices, the current, and hence the optical output, can be modulated at extremely high speeds which can be detected by a photodetector device and converted back to electrical current. The intensity modulation is imperceptible to the human eye, and thus communication is just as seamless as RF [radio frequency technology]. Using this technique, high-speed information can be transmitted from an LED lightbulb.

Disadvantages:
Because Li-Fi technology uses visible light as its means of communication, it won’t work through walls. This means that to have a Li-Fi network throughout your house, you will need these lightbulbs in every room (and maybe even the fridge) to have seamless connectivity.
Another major issue is that Li-Fi does not work outdoors, meaning that public Li-Fi will not be able to replace public Wi-Fi networks any time soon. While Li-Fi’s employment in direct sunlight won’t be possible, pureLiFi said that through the use of filters the technology can be used indoors even when sunlight is present.
Of course, one of the biggest drawbacks is the fact the light needs to be on all the time to deliver connectivity. While that’s not going to be an issue in industrial and retail environments, it’s will be both environmentally and practically problematic in domestic settings.
When Will Li-Fi Light bulbs Be Available?
Speaking to International Business Times UK this week, Deepak Solanki, CEO of Velmenni, said the technology could reach consumers in three to four years. “We are doing a few pilot projects within different industries where we can utilize the VLC technology,” Solanki said. “Currently, we have designed a smart-lighting solution for an industrial environment where the data communication is done through light. We are also doing a pilot project with a private client where we are setting up a Li-Fi network to access the Internet in their office space.”

WHAT IS BLUE EYES TECHNOLOGY?
 Aims at creating computational machines that have perceptual and sensory ability.
 Use camera and microphone to identify user actions and emotions
The term BLUE EYES:-
 BLUE in the term stands for Bluetooth, which enables reliable wireless communication
 EYES, because the eye movement enables us to obtain a lot of interesting and important information.
NEED OF BLUE EYES TECHNOLOGY:-
 To built a machine that can understand your emotions
 A pc that can listen, talk or scream
 Verify your identity, feels your presence and interact with you.
TECHNOLGIES USED:-
 Emotion Mouse
 Manual And Gaze Input Cascaded (MAGIC)
 Artificial Intelligent Speech Recognition
 The eye movement Sensor
EMOTION MOUSE:-
 Simplest way
 People spend approximately 1/3 of their total computer time touching input device
 Physiological data is obtained and emotional state is determined

MAGIC POINTING:-
 Reduce the cursor movement needed for target selection
 Click on the target with a regular manual input device
 Two magic pointing techniques
 Liberal
 Conservative
APPROACHES:-
 LIBERAL APPROACH:- To warp the cursor to every new object user looks.
 CONSERVATIVE APPROACH:- Does not warp the cursor target until the manual input device has been actuated.
SPEECH RECOGNITION:-
 Input words are scanned and matched against internally stored words
 Identification causes some action to be taken User speaks to the computer through microphone
 Filtered and fed to ADC and then stored in RAM
THE EYE MOVEMENT SENSOR DESIGNING:-
 A personal area network for linking all the operators and the supervising system
 Two major units
– DAU (data acquisition unit )
– CSU (central system unit )
DAU:-
 The DAU consists of the following components
 ATMEL 8952 microcontroller
 BLUE TOOTH MODULE – supports synchronous voice
 data transmission
 central system sound feedback
 ALPHAUNUMERIC LCD display
 LED indicators
DAU – features:-
 Lightweight
 Runs on batteries – low power consumption
 Easy to use – does not disturb the operator working
 ID cards for operator authorization
 Voice transmission using hardware PCM codec
CSU – components:-
 CONNECTION MANAGER – main task to perform low-level blue tooth communication
 DATA ANALYSIS MODULE – performs the analysis of the raw sensor data
 DATA LOGGER MODULE – provides support for storing the monitored data.
 VISULAIZATION MODULE – provides user interface for the supervisors
Data security:-
 Only registered mobile devices can connect to the system
 Bluetooth connection authentication
 Bluetooth connection encryption
 Access rights restrictions Personal and physiological data encryption
Applications:-
 In retailing record and interpret customer movements
 In automobile industry
 In video games
 To create “Face Responsive Display” and “Perceptive Environment” Generic control rooms

Ellipse is an elongated form of circle or in other words, circle is an special case of ellipse where the two radii of the circle are equal. Cvc An ellipse explained as a set of points such that the sum of distances from two fixed points (foci) is same for all points given a point P = (x, y), distances are d1 & d2, equation is :
d1 + d2 = constant _ _ _ (1)
In terms of local coordinates
F1 = (x1, y1) & F2 (x2, y2)
Equation is :
_ _ _ (2)
This can also be written in the form :
Ax2 + By2 + Cxy + Dx + Ey + F = 0 _ _ _ (3)
(More A, B, C, D, E, & F are evaluated in terms of focal coordinates & major minor axis).
• Major axis – which extends form 1 point to other through foci.
• Minor axis – which spans the shouter dimension bisecting major axis at ellipse center.
An interactive method for specifying an ellipse in an arbitrary orientation is to input two foci & a point on ellipse boundary & evaluate constant in equation (1) & so on.
Equation can be simplified if ellipse is in “standard position” with major & minor axis oriented parallel to x and y axis.
y

yc

xc x
= 1 _ _ _(4)
Now using polar coordinates r & θ
Parameter equations are :
x = xc + rx cos θ
y = yc + ry sin θ

TalkTalk, a British phone and broadband provider with more than four million customers, disclosed Friday that intruders had hacked its Web site and may have stolen personal and financial data. Sources close to the investigation say the company has received a ransom demand of approximately £80,000 (~USD $122,000), with the attackers threatening to publish the TalkTalk’s customer data unless they are paid the amount in Bitcoin.
In a statement on its Web site, TalkTalk said a criminal investigation was launched by the Metropolitan Police Cyber Crime Unit following “a significant and sustained cyberattack on our website.”
“That investigation is ongoing, but unfortunately there is a chance that some of the following data has been compromised: names, addresses, date of birth, phone numbers, email addresses, TalkTalk account information, credit card details and/or bank details,” the statement continues. “We are continuing to work with leading cyber crime specialists and the Metropolitan Police to establish exactly what happened and the extent of any information accessed.”
A source close to the investigation who spoke on condition of anonymity told KrebsOnSecurity that the hacker group who demanded the £80,000 ransom provided TalkTalk with copies of the tables from its user database as evidence of the breach. The database in question, the source said, appears related to at least 400,000 people who have recently undergone credit checks for new service with the company. However, TalkTalk’s statement says it’s too early to say exactly how many customers were impacted. “Identifying the extent of information accessed is part of the investigation that’s underway,” the company said.
Separately, promises to post the stolen data have appeared on AlphaBay, a Deep Web black market that specialized in selling stolen goods and illicit drugs. The posting was made by someone using the nickname “Courvoisier.” This member, whose signature describes him as “Level 6 Fraud and Drugs seller,” appears to be an active participant in the AlphaBay market with many vouches from happy customers who’ve turned to him for illegal drugs and stolen credit cards, among other goods and services.

Homomorphism Encryption (HE) provides a solution to the problem of outsourcing computations privately to a cloud. There are a number of Somewhat Homomorphism Encryption (SHE) schemes that support many additions but only a limited number of multiplications. Though SHE schemes can evaluate only a limited set of circuits compared to FHE schemes, it is at rest motivating to believe them for application chiefly for competence motive. Those SHE schemes that support only a single operation on cipher texts – addition or multiplication – is called linearly homomorphism. That is, (additive) linearly-homomorphism encryption scheme can estimate barely linear function on top of cipher texts.

The major problem address in the present job is to allow assessment of degree-two function using linearly-homomorphism encryption scheme. To this end, the author offer a general conversion that extend an (additively-written) linearly-homomorphism format to carry a solitary development procedure so that it will now be able to evaluate a subset of functions of degree two. The only requirement of the underlying linearly-homomorphism scheme is that it should be public space, which means that the communication room must be a openly known limited commutative circle and that it is probable to efficiently sample a random element from this ring. This property seem to hold for nearly the entire recognized linearly-homomorphism encryption scheme or can be simply modified to become so.

Density is single of the three main materials of a (fully) HE system that say so as to the difficulty of decryption must exist autonomous of the complexity of the route evaluate the given function. The other two requirements are semantic safety and route solitude for the underlying plaintext mail. The latter means that it must not be possible to learn anything regarding the post in the code texts input to a occupation It is exposed that the proposed transformation conserve semantic safety and the distorted format will be leveled circuit private if the underlying scheme is circuit classified. The distorted system will be clever to compactly evaluate a subset of degree-two functions (represented as polynomials) [Math Processing Error] consisting of the following polynomials [Math Processing Error]
where [Math Processing Error], [Math Processing Error], [Math Processing Error] are linear polynomials. It turns out that the subclass [Math Processing Error] of quadratic polynomials is applicable, for example, SPDZ protocol for multi-party computation requires a HE scheme for [Math Processing Error] with [Math Processing. A transformation of the underlying HE scheme is given in this model that is similar to the previous alteration, using which it is now possible to evaluate any quadratic polynomial compactly. Interestingly, the former (single-server) change preserves other property such as zero-knowledge proof of plaintext information, threshold decryption, and multi-key homomorphicity.