On provision of resilient connectivity in cognitive unmanned aerial vehicles

Mobile ad-hoc network (MANET) can be established in the areas/scenarios where the infrastructure networks are either out of service or no more available. MANETs have a lot of applications in sensor networks. Generally, a MANET deploys mobile ground nodes to set up a network. However, there can be some severe scenarios such as flood, battlefield, rescue operations, etc. where these ground nodes cannot be deployed. In such cases, a network of unmanned aerial vehicles (UAVs) can be a more viable option. Normally, UAVs operates on IEEE L-Band, IEEE S-Band or ISM band. These bands are already overcrowded, therefore, UAVs will face the problem of the spectrum scarcity. To resolve this issue cognitive radio (CR) is a most promising technology. Hence, in this work, we focus on CR based UAVs. As CR is based on opportunistic spectrum access, therefore, it is quite possible that all UAVs do not have one single channel available to communicate with each other. They need to form clusters for their communication depending on the availability of the channel. However, channel availability is intermittent because of opportunistic spectrum access. This may result in reforming of the cluster again and again. To avoid this frequent re-clustering and to maintain connectivity among the UAVs, in this paper, we present a resilient clustering technique with a concept of introducing a backup channel for each cluster. Simulation results show the significance of the proposed technique

On the Impact of Practical P2P Incentive Mechanisms on User Behavior

In this paper we report on the results of a large-scale measurement study of two popular peer-topeer systems, namely BitTorrent and eMule, that use practical and lightweight incentive mechanisms to encourage cooperation between users. We focus on identifying the strategic behavior of users in response to those incentive mechanisms. Our results illustrate a gap between what system designers and researchers expect from users in reaction to an incentive mechanism, and how users react to those incentives. In particular, we observe that the majority of BitTorrent users appear to cooperate well, despite the existence of known ways to tamper with the incentive mechanism, users engaging in behavior that could be regarded as cheating comprised only around 10% of BitTorrent’s population. That is, although we know that users can easily cheat, they actually do not currently appear to cheat at a large enough scale. In the eMule system, we identify several distinct classes of users based on their behavior. A large fraction of users appears to perceive cooperation as a good strategy, and openly share all the files they obtained. Other users engage in more subtle strategic choices, by actively optimizing the number and types of files they share in order to improve their standing in eMule’s waiting queues; they tend to remove files for which downloading is complete and keep a limited total volume of files shared

On the Impact of Practical P2P Incentive Mechanisms on User Behavior

In this paper we report on the results of a large-scale measurement study of two popular peer-topeer systems, namely BitTorrent and eMule, that use practical and lightweight incentive mechanisms to encourage cooperation between users. We focus on identifying the strategic behavior of users in response to those incentive mechanisms. Our results illustrate a gap between what system designers and researchers expect from users in reaction to an incentive mechanism, and how users react to those incentives. In particular, we observe that the majority of BitTorrent users appear to cooperate well, despite the existence of known ways to tamper with the incentive mechanism, users engaging in behavior that could be regarded as cheating comprised only around 10% of BitTorrent’s population. That is, although we know that users can easily cheat, they actually do not currently appear to cheat at a large enough scale. In the eMule system, we identify several distinct classes of users based on their behavior. A large fraction of users appears to perceive cooperation as a good strategy, and openly share all the files they obtained. Other users engage in more subtle strategic choices, by actively optimizing the number and types of files they share in order to improve their standing in eMule’s waiting queues; they tend to remove files for which downloading is complete and keep a limited total volume of files shared

SHAM: Scalable Homogeneous Addressing Mechanism for structured P2P networks

Abstract In designing structured P2P networks, scalability, resilience, and load balancing are features that are needed to be handled meticulously. The P2P overlay has to handle large scale of nodes while maintaining minimized path lengths in performing lookups. It has also to be resilient to nodes’ failure and be able to distribute the load uniformly over its participant. In this paper, we introduce SHAM: a Scalable, Homogenous, Addressing Mechanism for structured P2P networks. SHAM is a multi-dimensional overlay that places nodes in the network based on geometric addressing and maps keys onto values using consistent hashing. Our simulation results show that SHAM locates keys in the network efficiently, is highly resilient to major nodes’ failure, and has an effective load balancing property. Furthermore, unlike other DHTs and due to its distinguished naming scheme, SHAM deploys homogenous addressing which drastically reduces latency in the underlying network

Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network

Multicasting is a basic networking primitive used in a wide variety of applications that is also true for cognitive radio-based networks. Although cognitive radio technology is considered to be the most promising technology to deal with spectrum scarcity, it relates to completely different aspects of networking and presents new challenges. For cognitive radio-based multicast sessions, it is important to use the spectrum efficiently by reducing the number of channels used as well as engaging fewer nodes in data relaying. This will benefit the network in three ways. First, it will decrease the number of transmissions. Second, it will help to reduce energy usage. Third, it will spare more channels and relay nodes for simultaneous multicast sessions. To achieve these advantages, efficient channel selection and relay nodes are required based on hop-to-hop communication. In this paper an algorithm has been developed that attempts to minimize energy consumption by selecting the minimum possible number of relay nodes and channels for a multicast session, taking into account the sporadic availability of the spectrum. The proposed method performs effectively compared to the flooding method in terms of energy consumption for the provided examples in multicasting

Efficient FPGA Routing using Reinforcement Learning

With every new generation, Field Pro-grammable Gate Arrays (FPGAs) are getting more complex and so are their back end flow. Routing is an important step of FPGA back end flow that takes a lot of time. Making it more efficient in terms of execution time without the loss of quality is a huge challenge. In this work, we propose to use Reinforcement Learning(RL) based routing technique to make the FPGA routing faster. We use a comprehensive set of homogeneous and heterogeneous benchmarks to compare the RL-based technique with the conventional negotiated congestion driven routing technique. Experimental results reveal that for quick turn around, when compared to negotiated congestion technique, the RL-based technique gives, on average, 35% more accurate results about the final design. Moreover, for the complete routing step, the RL-based technique gives 30% speed up while giving similar quality of results