What is Mobile Ad-hoc Network (MANET)

Mobile Ad-hoc Network (MANET) is wireless communication technology. that communicate by using the cooperative communication methodology. It is a kind of decentralized network. Due to their dynamic nature, there is not a pre-existing infrastructure available. in this network, every node can perform routing by forwarding data to others, which nodes forward data is discovered dynamically by using network connectivity. An ad hoc network basically defined as a set of network devices with similar functions. These devices are free to associate with any other device in a scope. Connectivity between nodes is affected by the node’s resources, behavioral properties, and connection properties. The network offers communication between any two nodes via relaying the information through intermediate nodes. A network path is a list of connected nodes that link two nodes source and destination. Different routing techniques use more than one path between source and destination [2]. In MANET, nodes making efforts to get access to sharing wireless medium can result in collision issues. Using a cooperative communication network improves their immunity to collision by including the destination node associate self and other nodes [3].

Figure 1 An example of a mobile ad-hoc network

An ad hoc network is a collection of nodes that do not need to rely on a predefined infrastructure. Ad hoc networks can be formed, merged together, or partitioned, without a fixed infrastructure to manage the operation. Nodes of ad hoc networks are often mobile, thus the networks are called mobile ad hoc networks (MANET). In ad hoc networks there may exist static and wired nodes, which may make use of services offered by fixed infrastructure [3, 4].

Applications of Mobile Ad-hoc Network (MANET)

Due to the available features of the mobile ad-hoc networks, it is much beneficial in different domains of applications. some essential applications of MANET are discussed in the below table.

Applications Possible Scenarios/Services
1. Tactical networks
  • Military communication and operations
  • Automated battlefields
2. Emergency services
  • Search and rescue operations
  • Disaster recovery
  • Replacement of fixed infrastructure in case of environmental disasters
  • Policing and fire fighting
  • Supporting doctors and nurses in hospitals
3. Commercial and civilian environments
  • E-commerce: electronic payments anytime and anywhere
  • Business: dynamic database access, mobile offices
  • Vehicular services: road or accident guidance, the transmission of road and weather conditions, taxi cab network, inter-vehicle networks
  • Sports stadiums, trade fairs, shopping malls
  • Networks of visitors at airports
4. Home and enterprise networking
  • Home/office wireless networking
  • Conferences, meeting rooms
  • Personal area networks (PAN), Personal networks (PN)
  • Networks at construction sites
5. Education
  • Universities and campus settings
  • Virtual classrooms
  • Ad hoc communications during meetings or lectures
6. Entertainment
  • Multi-user games
  • Wireless P2P networking
  • Outdoor Internet access
  • Robotic pets
  • Theme parks
7. Sensor networks
  • Home applications: smart sensors and actuators embedded in consumer electronics
  • Body area networks (BAN)
  • Data tracking of environmental conditions, animal movements, chemical/biological detection


[1] Hassan Al-Mahdi and Mohamed A. Kalil, “A Dynamic Hop-Aware Buffer Management Scheme for Multi-hop Ad Hoc Networks”, IEEE Wireless Communications Letters 6, no. 1 (2017): pp. 22-25.

[2] Indrani Das and D. K Lobiyal, “Effect of Mobility Models on the Performance of Multipath Routing Protocol in MANET”, Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP), PP. 149–155, 2014

[3] M. Frodigh, P. Johansson, and P. Larsson.―Wireless ad hoc networking: the art of networking without a network, Ericsson Review, No.4, 2000, pp. 248-263.

[4] Mäki, Silja, “Security Fundamentals in Ad Hoc Networking”, Proceedings of the Helsinki University of Technology, Seminar on Internetworking-Ad Hoc Networks. 2000.

[5] M. Frodigh, P. Johansson, and P. Larsson.―Wireless ad hoc networking: the art of networking without a network, Ericsson Review, No.4, 2000, pp. 248-263.

[6] Zhisheng Niu, Lecture Notes: “Broadband Computer Networks”, Tsinghua University, 2003.

[7] Luis Bernardo, Rodolfo Oliveira, Sérgio Gaspar, David Paulino and Paulo Pinto a Telephony Application for MANETs: Voice over a MANET-Extended JXTA Virtual Overlay Network.

what is collaborative filtering/ recommendation system how it works

In recommendation systems, there are two main kinds of recommendation systems collaborative and content-based. The collaborative recommendation system is also called a collaborative filter because that reduces the information and identifies the relevant information. The aim of this filter is to focus on user-profiles and try to find similar users. Based on these similar users the recommendation system performs recommendations for products and services. In such recommendation models, we need not consider the features of the profile of the item. The basic block diagram of collaborative filtering is demonstrated in figure 1.

Figure 1 Collaborative recommendation system

In this kind of recommendation model, we need to profile users and make groups of similar behavior users. Based on the common and union group of preferences of most of the users the system recommends the products. We can understand the working of the collaborative recommendation system by using an example:

Let, there are some users say U1, U2, U3, and U4. Additionally, for assumption, we have considered there are three movies say M1, M2, and M3. Now, let users U1 and U3 like to watch movies M1 and M3. and user U2 like to watch movie M2. Then if a movie appears which is liked by user U3 it means the U1 may also like the movie.

Implementation of a Collaborative recommendation system

The recommendation can be implemented in two kinds of techniques:

  1. Method 1: in this method, a clustering algorithm is used to reduce the search space. additionally, a machine learning algorithm can be used for learning the user profile attributes. based on the learning algorithm recommend the products.
  2. Method 2: in this method, a linear search technique is implemented using distance measuring techniques or similarity matching techniques. On the basis of similarity between two user profile attributes, we have calculated the most likely product to recommend.

Content based Recommendation System

The recommendation system can be developed based on Content-Based filtering, and collaborative filtering models. in this article, we are discussing a content-based Recommendation system. The content-based Recommendation system utilizes the data taken from the user which may be captured directly or indirectly from the user. For example by using a rating, review, or clicking on a link. The collected data will be used to prepare a user profile, which will be used to suggest the product or service to users. This type of recommendation system is adaptive in nature and improves its performance with user inputs,  and becomes more accurate. You can also watch the video in Hindi for the same content using this link

Figure 1 Content-based recommendation system

User Profile: The content-based recommendation systems prepare a User profile. this process of making a user profile is termed Profiling. using this process the algorithms create vectors that describe the user’s preference. The User profile utilizes a utility matrix to describe the relationship between user and item.

Item Profile: we also need to build a profile for each item. this item or product profile represents the characteristics of that item. For example, if we consider a movie as an item then its actors, director, and the release year are the features or characteristics of the movie. We can also add its rating as a profile attribute of the product.

Utility Matrix: The Utility Matrix describes the user’s preference for certain items. During data collection from users, we have to consider the items which are liked and disliked by users. the preferences using this matrix are represented in terms of a score that is assigned to each user-item pair, sometimes this score is called the degree of preference.


Implementation techniques

there are two kinds of concepts by which we can implement the content-based recommendation system.

Method 1: in this technique, we can use a distance or similarity-based function to measure the likelihood between the item and the user’s preferences. the most similar products with similar characteristics based on the item are predicted as recommendations. For example: Let a user will have like a number of actors that tend to appear or not appear based on dislike. Consider a movie with actors that the user likes and only a few actors that the user doesn’t like, then the distance between the user and the movie will be measured and the number of actors liked by the user then be recommended to view. The distance indicates the user tends to like the movie.

Method 2: In this method of implementation, we can use a machine learning classifier in the recommendation systems, like Decision Tree or other. The classifier tries to find out whether a user wants to watch a movie or not based on the characteristics. At each level of prediction, we can apply a certain condition to refine our predictions.

What is web recommendation/recommender system in web mining

A web recommendation system is an extensive class of Web applications. That involves predicting user responses to options. Such a facility is called a recommendation system. We can take a basic idea of a recommendation system by using two good examples [13]:

  1. Online news publishing websites are offering news articles to their readers, based on readers’ interests.
  2. Offering customers of e-commerce to suggest what they might like to buy. This prediction is performed based on their past history.

Types of Recommendation system

Recommendation systems can be designed by using a number of different techniques. these techniques are related to web mining and data mining algorithms. based on their design concept we can classify these systems into three broad groups:

Figure 1 web recommendation system overview

  1. Content-based systems examine the properties of the items recommended. For instance, if a Netflix user has watched many cowboy movies, then recommend a movie classified in the database as having the “cowboy” genre.
  2. Collaborative filtering systems recommend items based on similarity measures between users and/or items. The items recommended to a user are those preferred by similar users. However, these technologies by themselves are not sufficient, and there are some new algorithms that have proven effective for recommendation systems.
  3.  hybrid filtering systems are a combination of both content-based and collaborative filtering systems. That can be developed in different techniques such as:
    1.  first, implement the content-based technique and then implement properties of collaborative filters, and vice versa
    2.  second implement both the filters separately and combine the consequences of both filters

Applications of Recommendation Systems

There is the various application of recommendation systems available. Several important applications of recommendation systems are given below:

  1. Product Recommendations: Perhaps the most important use of recommendation systems is at online retailers. Noted how Amazon or similar online vendors strive to present each returning user with some suggestions of products that they might like to buy. These suggestions are not random but are based on the purchasing decisions made by similar customers or on other techniques.
  2. Movie Recommendations: Netflix offers its customer’s recommendations of movies they might like. These recommendations are based on ratings provided by users, much like the ratings suggested. The importance of predicting ratings accurately is so high, that Netflix offered a prize of one million dollars for the first algorithm that could beat its own recommendation system by 10%. The prize was finally won in 2009, by a team of researchers called “Bellkor’s Pragmatic Chaos,” after over three years of competition.
  3. News Articles: News services have attempted to identify articles of interest to readers, based on the articles that they have read in the past. The similarity might be based on the similarity of important words in the documents, or on the articles that are read by people with similar reading tastes. The same principles apply to recommending blogs from among the millions of blogs available, videos on YouTube, or other sites where content is provided regularly.
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