Cyber Crime and Cyber Terrorism

TETRA OVER MOBILE AD-HOC NETWORK
Mobile Ad-Hoc Networks
are multi hop networks where nodes can be stationary 
or mobile; and they are formed on a dynamic basis. They allow people to perform 
tasks efficiently by offering unprecedented levels of access to information. In mo-
bile ad-hoc networks, topology is highly dynamic and random; and in addition, the 
distribution of nodes and their capability of self-organizing play an important role. 
Their main characteristics can be summarized as follows: The topology is highly 
dynamic and frequent changes in the topology may be hard to predict; Mobile ad-
hoc networks are based on wireless links, which will continue to have a significantly 
lower capacity than their wired counterparts; Physical security is limited due to the 
wireless transmission; Mobile ad-hoc networks are affected by higher loss rates, and 
can present higher delays and jitter than fixed networks due to the wireless transmis-
sion; and Mobile ad-hoc network nodes rely on batteries or other exhaustible means 
for their energy. As a result, energy savings are an important system design criterion. 
Furthermore, nodes have to be power-aware: the set of functions offered by a node 
depends on its available power (CPU, memory, etc.).
A well-designed architecture for mobile ad-hoc networks involves all networking 
layers, ranging from the physical to the application layer. Power management is of 

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Proposed PPDR communication network architectural solutions
paramount importance; and general strategies for saving power need to be addressed, 
as well as adaptation to the specifics of nodes of general channel and source coding 
methods, of radio resource management and multiple accesses. In mobile ad-hoc net-
works, with the unique characteristic of being totally independent from any authority 
and infrastructure, there is a great potential for the users. In fact, roughly speak-
ing, two or more users can become a mobile ad-hoc network simply by being close 
enough to meet the radio constraints, without any external intervention.
Routing
problems have been addressed through research; where routing protocols 
between any pair of nodes within an ad-hoc network can be difficult because the 
nodes can move randomly and can also join or leave the network. This means that an 
optimal route at a certain time may not work seconds later.
Two of the best multicast protocols to be adopted are MAODV (Multicast Ad-
hoc on-demand Distance Vector Routing Protocol) and ODMRP (On Demand 
Multicast Routing Protocol). The performance measures that were evaluated are the 
PDR (Packet Delivery Ratio) and the Latency. Previous studies have evaluated these 
algorithms with respect to the network traffic, the node speed, the area and the an-
tenna range for different simulation scenarios. In general, MAODV performs bet-
ter for high traffic. ODMRP performs better for large areas and high node speeds 
but poorer for small antenna ranges. Therefore, MAODV and its derivative AODV 
ALMA will be adopted in this project. A number of technical challenges are faced 
today due to the heterogeneous, dynamic nature of this hybrid MANET. The hybrid 
routing scheme AODV ALMA can act simultaneously combining mobile agents to 
find path to the gateway and on-demand distance vector approach to find path in local 
MANET is one of the unique solution. An adaptive gateway discovery mechanism 
based on mobile agents making use of pheromone value, pheromone decay time and 
balance index is used to estimate the path and next hop to the gateway. The mobile 
nodes automatically configure the address using mobile agents first selecting the 
gateway and then using the gateway prefix address. The mobile agents are also used 
to track changes in topology enabling high network connectivity with reduced delay 
in packet transmission to Internet.
Clustering
is an effective technique for node management in a MANET. Cluster 
formation involves election of a mobile node as Cluster head to control the other 
nodes in the newly formed cluster. The connections between nodes and the cluster 
head changes rapidly in a mobile ad-hoc network. Thus cluster maintenance is also 
essential. Prediction of mobility-based cluster maintenance involves the process of 
finding out the next position that a mobile node might take based on the previous 
locations it visited. The overhead can be reduced in communication by predicting 
mobility of node using linear autoregression and cluster formation.