Sustaining cooperation on networks: an analytical study based on evolutionary game theory

We analytically study the role played by the network topology in sustaining cooperation in a society of myopic agents in an evolutionary setting. In our model, each agent plays the Prisoner's Dilemma (PD) game with its neighbors, as specified by a network. Cooperation is the incumbent strategy, whereas defectors are the mutants. Starting with a population of cooperators, some agents are switched to defection. The agents then play the PD game with their neighbors and compute their fitness. After this, an evolutionary rule, or imitation dynamic is used to update the agent strategy. A defector switches back to cooperation if it has a cooperator neighbor with higher fitness. The network is said to sustain cooperation if almost all defectors switch to cooperation. Earlier work on the sustenance of cooperation has largely consisted of simulation studies, and we seek to complement this body of work by providing analytical insight for the same. We find that in order to sustain cooperation, a network should satisfy some properties such as small average diameter, densification, and irregularity. Real-world networks have been empirically shown to exhibit these properties, and are thus candidates for the sustenance of cooperation. We also analyze some specific graphs to determine whether or not they sustain cooperation. In particular, we find that scale-free graphs belonging to a certain family sustain cooperation, whereas Erdos-Renyi random graphs do not. To the best of our knowledge, ours is the first analytical attempt to determine which networks sustain cooperation in a population of myopic agents in an evolutionary setting.

Better QOS with W/T SPR codes for the transmission of multimedia signals in Optical CDMA networks

In this paper optical code-division multiple-access (O-CDMA) packet network is considered, which offers inherent security in the access networks. The application of O-CDMA to multimedia transmission (voice, data, and video) is investigated. The simultaneous transmission of various services is achieved by assigning to each user unique multiple code signatures. Thus, by applying a parallel mapping technique, we achieve multi-rate services. A random access protocol is proposed, here, where all distinct codes are used, for packet transmission. The codes, Optical Orthogonal Code (OOC), or 1D codes and Wavelength/Time Single-Pulse-per-Row (W/T SPR), or 2D codes, are analyzed. These 1D and 2D codes with varied weight are used to differentiate the Quality of Service (QoS). The theoretical bit error probability corresponding to the quality of each service is established using 1D and 2D codes in the receiver noiseless case and compared. The results show that, using 2D codes QoS in multimedia transmission is better than using 1D codes.

A Multicast Authentication Protocol (MAP) for Dynamic Multicast Groups in Wireless Networks

In this paper, we have proposed a centralized multicast authentication protocol (MAP) for dynamic multicast groups in wireless networks. In our protocol, a multicast group is defined only at the time of the multicasting. The authentication server (AS) in the network generates a session key and authenticates it to each of the members of a multicast group using the computationally inexpensive least common multiple (LCM) method. In addition, a pseudo random function (PRF) is used to bind the secret keys of the network members with their identities. By doing this, the AS is relieved from storing per member secrets in its memory, making the scheme completely storage scalable. The protocol minimizes the load on the network members by shifting the computational tasks towards the AS node as far as possible. The protocol possesses a membership revocation mechanism and is protected against replay attack and brute force attack. Analytical and simulation results confirm the effectiveness of the proposed protocol.

Joint Scheduling & Jamming for Data Secrecy in Wireless Networks

The broadcast nature of the wireless medium jeopardizes secure transmissions. Cryptographic measures fail to ensure security when eavesdroppers have superior computational capability; however, it can be assured from information theoretic security approaches. We use physical layer security to guarantee non-zero secrecy rate in single source, single destination multi-hop networks with eavesdroppers for two cases: when eavesdropper locations and channel gains are known and when their positions are unknown. We propose a two-phase solution which consists of finding activation sets and then obtaining transmit powers subject to SINR constraints for the case when eavesdropper locations are known. We introduce methods to find activation sets and compare their performance. Necessary but reasonable approximations are made in power minimization formulations for tractability reasons. For scenarios with no eavesdropper location information, we suggest vulnerability region (the area having zero secrecy rate) minimization over the network. Our results show that in the absence of location information average number of eavesdroppers who have access to data is reduced.

Give-and-take based peer-to-peer content distribution networks

Content Distribution Networks (CDNs) are widely used to distribute data to large number of users. Traditionally, content is being replicated among a number of surrogate servers, leading to high operational costs. In this context, Peer-to-Peer (P2P) CDNs have emerged as a viable alternative. An issue of concern in P2P networks is that of free riders, i.e., selfish peers who download files and leave without uploading anything in return. Free riding must be discouraged. In this paper, we propose a criterion, the Give-and-Take (G&T) criterion, that disallows free riders. Incorporating the G&T criterion in our model, we study a problem that arises naturally when a new peer enters the system: viz., the problem of downloading a `universe' of segments, scattered among other peers, at low cost. We analyse this hard problem, and characterize the optimal download cost under the G&T criterion. We propose an optimal algorithm, and provide a sub-optimal algorithm that is nearly optimal, but runs much more quickly; this provides an attractive balance between running time and performance. Finally, we compare the performance of our algorithms with that of a few existing P2P downloading strategies in use. We also study the computation time for prescribing the strategy for initial segment and peer selection for the newly arrived peer for various existing and proposed algorithms, and quantify cost-computation time trade-offs.

Co-Evolution of Content Spread and Popularity in Mobile Opportunistic Networks

We consider a setting in which a single item of content is disseminated in a population of mobile nodes by opportunistic copying when pairs of nodes come in radio contact. The nodes in the population may either be interested in receiving the content (referred to as destinations) or not yet interested in receiving the content (referred to as relays). We consider a model for the evolution of popularity, the process by which relays get converted into destinations. A key contribution of our work is to model and study the joint evolution of content popularity and its spread in the population. Copying the content to relay nodes is beneficial since they can help spread the content to destinations, and could themselves be converted into destinations. We derive a fluid limit for the joint evolution model and obtain optimal policies for copying to relay nodes in order to deliver content to a desired fraction of destinations, while limiting the fraction of relay nodes that get the content but never turn into destinations. We prove that a time-threshold policy is optimal for controlling the copying to relays, i.e., there is an optimal time-threshold up to which all opportunities for copying to relays are exploited, and after which relays are not copied to. We then utilize simulations and numerical evaluations to provide insights into the effects of various system parameters on the optimally controlled co-evolution model.

Two timescale convergent Q-learning for sleep-scheduling in wireless sensor networks

In this paper, we consider an intrusion detection application for Wireless Sensor Networks. We study the problem of scheduling the sleep times of the individual sensors, where the objective is to maximize the network lifetime while keeping the tracking error to a minimum. We formulate this problem as a partially-observable Markov decision process (POMDP) with continuous stateaction spaces, in a manner similar to Fuemmeler and Veeravalli (IEEE Trans Signal Process 56(5), 2091-2101, 2008). However, unlike their formulation, we consider infinite horizon discounted and average cost objectives as performance criteria. For each criterion, we propose a convergent on-policy Q-learning algorithm that operates on two timescales, while employing function approximation. Feature-based representations and function approximation is necessary to handle the curse of dimensionality associated with the underlying POMDP. Our proposed algorithm incorporates a policy gradient update using a one-simulation simultaneous perturbation stochastic approximation estimate on the faster timescale, while the Q-value parameter (arising from a linear function approximation architecture for the Q-values) is updated in an on-policy temporal difference algorithm-like fashion on the slower timescale. The feature selection scheme employed in each of our algorithms manages the energy and tracking components in a manner that assists the search for the optimal sleep-scheduling policy. For the sake of comparison, in both discounted and average settings, we also develop a function approximation analogue of the Q-learning algorithm. This algorithm, unlike the two-timescale variant, does not possess theoretical convergence guarantees. Finally, we also adapt our algorithms to include a stochastic iterative estimation scheme for the intruder's mobility model and this is useful in settings where the latter is not known. Our simulation results on a synthetic 2-dimensional network setting suggest that our algorithms result in better tracking accuracy at the cost of only a few additional sensors, in comparison to a recent prior work.

Adaptive Sleep-Wake Control using Reinforcement Learning in Sensor Networks

The aim in this paper is to allocate the `sleep time' of the individual sensors in an intrusion detection application so that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We propose two novel reinforcement learning (RL) based algorithms that attempt to minimize a certain long-run average cost objective. Both our algorithms incorporate feature-based representations to handle the curse of dimensionality associated with the underlying partially-observable Markov decision process (POMDP). Further, the feature selection scheme used in our algorithms intelligently manages the energy cost and tracking cost factors, which in turn assists the search for the optimal sleeping policy. We also extend these algorithms to a setting where the intruder's mobility model is not known by incorporating a stochastic iterative scheme for estimating the mobility model. The simulation results on a synthetic 2-d network setting are encouraging.

Optimal power allocation for protective jamming in wireless networks: A flow based model

We address the problem of passive eavesdroppers in multi-hop wireless networks using the technique of friendly jamming. The network is assumed to employ Decode and Forward (DF) relaying. Assuming the availability of perfect channel state information (CSI) of legitimate nodes and eavesdroppers, we consider a scheduling and power allocation (PA) problem for a multiple-source multiple-sink scenario so that eavesdroppers are jammed, and source-destination throughput targets are met while minimizing the overall transmitted power. We propose activation sets (AS-es) for scheduling, and formulate an optimization problem for PA. Several methods for finding AS-es are discussed and compared. We present an approximate linear program for the original nonlinear, non-convex PA optimization problem, and argue that under certain conditions, both the formulations produce identical results. In the absence of eavesdroppers' CSI, we utilize the notion of Vulnerability Region (VR), and formulate an optimization problem with the objective of minimizing the VR. Our results show that the proposed solution can achieve power-efficient operation while defeating eavesdroppers and achieving desired source-destination throughputs simultaneously. (C) 2015 Elsevier B.V. All rights reserved.

A Multi-phase Approach for Improving Information Diffusion in Social Networks

For maximizing influence spread in a social network, given a certain budget on the number of seed nodes, we investigate the effects of selecting and activating the seed nodes in multiple phases. In particular, we formulate an appropriate objective function for two-phase influence maximization under the independent cascade model, investigate its properties, and propose algorithms for determining the seed nodes in the two phases. We also study the problem of determining an optimal budget-split and delay between the two phases.

A Perspective to Adopt Continuous Dynamic Cognition for Malicious Node Detection in Heterogeneous Networks

The current day networks use Proactive networks for adaption to the dynamic scenarios. The use of cognition technique based on the Observe, Orient, Decide and Act loop (OODA) is proposed to construct proactive networks. The network performance degradation in knowledge acquisition and malicious node presence is a problem that exists. The use of continuous time dynamic neural network is considered to achieve cognition. The variance in service rates of user nodes is used to detect malicious activity in heterogeneous networks. The improved malicious node detection rates are proved through the experimental results presented in this paper. (C) 2015 The Authors. Published by Elsevier B.V.

Zinc oxide nanowire networks for macroelectronic devices

Highly transparent zinc oxide (ZnO) nanowire networks have been used as the active material in thin film transistors (TFTs) and complementary inverter devices. A systematic study on a range of networks of variable density and TFT channel length was performed. ZnO nanowire networks provide a less lithographically intense alternative to individual nanowire devices, are always semiconducting, and yield significantly higher mobilites than those achieved from currently used amorphous Si and organic TFTs. These results suggest that ZnO nanowire networks could be ideal for inexpensive large area electronics. © 2009 American Institute of Physics.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.