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