The prevalent applications of WSN have fascinated a plethora of research efforts. Sensor nodes have serious limitations such as battery lifetime, memory constraints, and computational capabilities. Clustering is an important method for maximizing the network lifetime. In clustering, a network is divided into virtual groups, and CHs send their data to the BS either directly or using multi-hop routing. CHs are some special nodes having more energy than normal nodes. In fact, these special nodes are also battery operated and consequently power constrained; thus, they play a vital role in network lifetime. Cluster formation is very important and improper design may cause overload. This paper presents a modified GA-based load balanced clustering (MGALBC) algorithm for WSN. It is better than GA-based load balanced clustering (GALBC) algorithm because it balances the load by considering the residual energy. The result shows that the proposed method is better than GALBC in terms of energy consumption, number of active sensor nodes and network life.

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A MANET is a cooperative network in which each node has dual responsibilities of forwarding and routing thus node strength is a major factor because a lesser number of nodes reduces network performance. The existing reputation based methods have limitation due to their stricter punishment strategy because they isolate nodes from network participation having lesser reputation value and thus reduce the total strength of nodes in a network. In this paper we have proposed a mathematical model for the classification of nodes in MANETs using adaptive decision boundary. This model classifies nodes in two classes: selfish and regular node as well as it assigns the grade to individual nodes. The grade is computed by counting how many passes are required to classify a node and it is used to define the punishment strategy as well as enhances the reputation definition of traditional reputation based mechanisms. Our work provides the extent of noncooperation that a network can allow depending on the current strength of nodes for the given scenario and thus includes selfish nodes in network participation with warning messages. We have taken a leader node for reputation calculation and classification which saves energy of other nodes as energy is a major challenge of MANET. The leader node finally sends the warning message to low grade nodes and broadcasts the classification list in the MANET that is considered in the routing activity.

Human society is a complex and most organized networks, in which many communities have different cultural livelihood. The creation/formation of one or more communities within a society and the way of associations can be mapped to MANET. By involving human characteristics and behavior, surely it would pave a new way, for further development. In this paper we have presented a new approach called "HAMANET" which is not only robust and secure but it certainly meets the challenges of MANET (such as name resolution, address allocation and authentication). Our object oriented design defines a service in terms of Arts, Culture, and Machine. The "Art" is the smallest unit of work (defined as an interface), the "Culture" is the integration/assembling of one or more Arts (defined as a class) and finally the "Machine" which is an instance of a Culture that defines a service. The grouping of the communicable Machines of the same Culture forms a "Community". We have used the term "Society" for MANET consisting of one or more communities and modeled using humanistic approach. We have compared our design with GloMoSim and proposed the implementation of file transfer service using the said approach. Our approach gives better results in terms of implementation of the basic services, security, reliability, throughput, extensibility, scalability etc.

Naive Bayes classifiers are a set of categorization techniques based on Bayes' theorem. It is a collection of algorithms where all these algorithms share a common principle. This chapter presents the detection of DDos attack using scoreboard dataset. The dataset is separated into two parts, that is, feature vector and the reaction vector. Feature vector contains all the rows of dataset in which each vector consists of the value of dependent features such as IP address, port, counter, flag, syncnt, no. of packets, etc. The reaction vector contains the value of class variable (prediction or output) for each row. Result shows the effectiveness of the model in preventing DDoS attack by classifying request.