12,839 research outputs found
Power-Aware Hybrid Intrusion Detection System (PHIDS) using Cellular Automata in Wireless AdHoc Networks
Adhoc wireless network with their changing topology and distributed nature
are more prone to intruders. The network monitoring functionality should be in
operation as long as the network exists with nil constraints. The efficiency of
an Intrusion detection system in the case of an adhoc network is not only
determined by its dynamicity in monitoring but also in its flexibility in
utilizing the available power in each of its nodes. In this paper we propose a
hybrid intrusion detection system, based on a power level metric for potential
adhoc hosts, which is used to determine the duration for which a particular
node can support a network monitoring node. Power aware hybrid intrusion
detection system focuses on the available power level in each of the nodes and
determines the network monitors. Power awareness in the network results in
maintaining power for network monitoring, with monitors changing often, since
it is an iterative power optimal solution to identify nodes for distributed
agent based intrusion detection. The advantage that this approach entails is
the inherent flexibility it provides, by means of considering only fewer nodes
for reestablishing network monitors. The detection of intrusions in the network
is done with the help of Cellular Automat CA. The CAs classify a packet routed
through the network either as normal or an intrusion. The use of CAs enable in
the identification of already occurred intrusions as well as new intrusions
Context Aware Multisensor Image Fusion for Military Sensor Networks using Multi Agent System
This paper proposes a Context Aware Agent based Military Sensor Network
(CAMSN) to form an improved infrastructure for multi-sensor image fusion. It
considers contexts driven by a node and sink. The contexts such as general and
critical object detection are node driven where as sensing time (such as day or
night) is sink driven. The agencies used in the scheme are categorized as node
and sink agency. Each agency employs a set of static and mobile agents to
perform dedicated tasks. Node agency performs context sensing and context
interpretation based on the sensed image and sensing time. Node agency
comprises of node manager agent, context agent and node blackboard (NBB).
Context agent gathers the context from the target and updates the NBB, Node
manager agent interprets the context and passes the context information to sink
node by using flooding mechanism. Sink agency mainly comprises of sink manager
agent, fusing agent, and sink black board. A context at the sensor node
triggers the fusion process at the sink. Based on the context, sink manager
agent triggers the fusing agent. Fusing agent roams around the network, visits
active sensor node, fuses the relevant images and sends the fused image to
sink. The fusing agent uses wavelet transform for fusion. The scheme is
simulated for testing its operation effectiveness in terms of fusion time, mean
square error, throughput, dropping rate, bandwidth requirement, node battery
usage and agent overhead
Artificial Intelligence-Based Techniques for Emerging Robotics Communication: A Survey and Future Perspectives
This paper reviews the current development of artificial intelligence (AI)
techniques for the application area of robot communication. The study of the
control and operation of multiple robots collaboratively toward a common goal
is fast growing. Communication among members of a robot team and even including
humans is becoming essential in many real-world applications. The survey
focuses on the AI techniques for robot communication to enhance the
communication capability of the multi-robot team, making more complex
activities, taking an appreciated decision, taking coordinated action, and
performing their tasks efficiently.Comment: 11 pages, 6 figure
Data aggregation routing protocols in wireless sensor networks: a taxonomy
Routing in Wireless Sensor Network (WSN) aims to interconnect sensor nodes
via single or multi-hop paths. The routes are established to forward data
packets from sensor nodes to the sink. Establishing a single path to report
each data packet results in increasing energy consumption in WSN, hence, data
aggregation routing is used to combine data packets and consequently reduce the
number of transmissions. This reduces the routing overhead by eliminating
redundant and meaningless data. There are two models for data aggregation
routing in WSN: mobile agent and client/server. This paper describes data
aggregation routing and classifies then the routing protocols according to the
network architecture and routing models. The key issues of the data aggregation
routing models (client/server and mobile agent) are highlighted and discussed
Cluster Based Cost Efficient Intrusion Detection System For Manet
Mobile ad-hoc networks are temporary wireless networks. Network resources are
abnormally consumed by intruders. Anomaly and signature based techniques are
used for intrusion detection. Classification techniques are used in anomaly
based techniques. Intrusion detection techniques are used for the network
attack detection process. Two types of intrusion detection systems are
available. They are anomaly detection and signature based detection model. The
anomaly detection model uses the historical transactions with attack labels.
The signature database is used in the signature based IDS schemes.
The mobile ad-hoc networks are infrastructure less environment. The intrusion
detection applications are placed in a set of nodes under the mobile ad-hoc
network environment. The nodes are grouped into clusters. The leader nodes are
assigned for the clusters. The leader node is assigned for the intrusion
detection process. Leader nodes are used to initiate the intrusion detection
process. Resource sharing and lifetime management factors are considered in the
leader election process. The system optimizes the leader election and intrusion
detection process.
The system is designed to handle leader election and intrusion detection
process. The clustering scheme is optimized with coverage and traffic level.
Cost and resource utilization is controlled under the clusters. Node mobility
is managed by the system
Mobile Edge Cloud: Opportunities and Challenges
Mobile edge cloud is emerging as a promising technology to the internet of
things and cyber-physical system applications such as smart home and
intelligent video surveillance. In a smart home, various sensors are deployed
to monitor the home environment and physiological health of individuals. The
data collected by sensors are sent to an application, where numerous algorithms
for emotion and sentiment detection, activity recognition and situation
management are applied to provide healthcare- and emergency-related services
and to manage resources at the home. The executions of these algorithms require
a vast amount of computing and storage resources. To address the issue, the
conventional approach is to send the collected data to an application on an
internet cloud. This approach has several problems such as high communication
latency, communication energy consumption and unnecessary data traffic to the
core network. To overcome the drawbacks of the conventional cloud-based
approach, a new system called mobile edge cloud is proposed. In mobile edge
cloud, multiple mobiles and stationary devices interconnected through wireless
local area networks are combined to create a small cloud infrastructure at a
local physical area such as a home. Compared to traditional mobile distributed
computing systems, mobile edge cloud introduces several complex challenges due
to the heterogeneous computing environment, heterogeneous and dynamic network
environment, node mobility, and limited battery power. The real-time
requirements associated with the internet of things and cyber-physical system
applications make the problem even more challenging. In this paper, we describe
the applications and challenges associated with the design and development of
mobile edge cloud system and propose an architecture based on a cross layer
design approach for effective decision making.Comment: 4th Annual Conference on Computational Science and Computational
Intelligence, December 14-16, 2017, Las Vegas, Nevada, USA. arXiv admin note:
text overlap with arXiv:1810.0704
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
Applications of Data Mining Techniques for Vehicular Ad hoc Networks
Due to the recent advances in vehicular ad hoc networks (VANETs), smart
applications have been incorporating the data generated from these networks to
provide quality of life services. In this paper, we have proposed taxonomy of
data mining techniques that have been applied in this domain in addition to a
classification of these techniques. Our contribution is to highlight the
research methodologies in the literature and allow for comparing among them
using different characteristics. The proposed taxonomy covers elementary data
mining techniques such as: preprocessing, outlier detection, clustering, and
classification of data. In addition, it covers centralized, distributed,
offline, and online techniques from the literature
Review of MANETS Using Distributed Public-key Cryptography
Ensuring security is something that is not easily done as many of the demands
of network security conflict with the demands of mobile networks, majorly
because of the nature of the mobile devices (e.g. low power consumption, low
processing load). The study of secure distributed key agreement has great
theoretical and practical significance. Securing Mobile Ad-hoc Networks using
Distributed Public-key Cryptography in pairing with Mobile Ad hoc Networks and
various protocols are essential for secure communications in open and
distributed environment.Comment: no of pages - 5 and published with IJCT
Managing Congestion Control in Mobile AD-HOC Network Using Mobile Agents
In mobile adhoc networks, congestion occurs with limited resources. The
standard TCP congestion control mechanism is not able to handle the special
properties of a shared wireless channel. TCP congestion control works very well
on the Internet. But mobile adhoc networks exhibit some unique properties that
greatly affect the design of appropriate protocols and protocol stacks in
general, and of congestion control mechanism in particular. As it turned out,
the vastly differing environment in a mobile adhoc network is highly
problematic for standard TCP. Many approaches have been proposed to overcome
these difficulties. Mobile agent based congestion control Technique is proposed
to avoid congestion in adhoc network. When mobile agent travels through the
network, it can select a less-loaded neighbor node as its next hop and update
the routing table according to the node congestion status. With the aid of
mobile agents, the nodes can get the dynamic network topology in time. In this
paper, a mobile agent based congestion control mechanism is presented.Comment: 9 Pages. IJCEA, 2014. arXiv admin note: substantial text overlap with
arXiv:0907.5441 by other authors without attributio
- …