Analysis of MAC protocols with correlation receiver for optical CDMA networks – part I

In this paper optical code-division multiple-access (O-CDMA) packet network is considered. Two types of random access protocols are proposed for packet transmission. In protocol 1, all distinct codes and in protocol 2, distinct codes as well as shifted versions of all these codes are used. O-CDMA network performance using optical orthogonal codes (OOCs) 1-D and twodimensional (2-D) wavelength/time single-pulse-per-row (W/TSPR) codes are analyzed. The main advantage of using 2-D codes instead of one-dimensional (1-D) codes is to reduce the errors due to multiple access interference among different users. In this paper, correlation receiver is considered in the analysis. Using analytical model, we compute and compare packet-success probability for 1-D and 2-D codes in an O-CDMA network and the analysis shows improved performance with 2-D codes as compared to 1-D codes.

Information dissemination in socially aware networks under the linear threshold model

We provide new analytical results concerning the spread of information or influence under the linear threshold social network model introduced by Kempe et al. in, in the information dissemination context. The seeder starts by providing the message to a set of initial nodes and is interested in maximizing the number of nodes that will receive the message ultimately. A node's decision to forward the message depends on the set of nodes from which it has received the message. Under the linear threshold model, the decision to forward the information depends on the comparison of the total influence of the nodes from which a node has received the packet with its own threshold of influence. We derive analytical expressions for the expected number of nodes that receive the message ultimately, as a function of the initial set of nodes, for a generic network. We show that the problem can be recast in the framework of Markov chains. We then use the analytical expression to gain insights into information dissemination in some simple network topologies such as the star, ring, mesh and on acyclic graphs. We also derive the optimal initial set in the above networks, and also hint at general heuristics for picking a good initial set.

A dynamic bandwidth allocation scheme for interactive multimedia applications over cellular networks

Cellular networks played key role in enabling high level of bandwidth for users by employing traditional methods such as guaranteed QoS based on application category at radio access stratum level for various classes of QoSs. Also, the newer multimode phones (e.g., phones that support LTE (Long Term Evolution standard), UMTS, GSM, WIFI all at once) are capable to use multiple access methods simulta- neously and can perform seamless handover among various supported technologies to remain connected. With various types of applications (including interactive ones) running on these devices, which in turn have different QoS requirements, this work discusses as how QoS (measured in terms of user level response time, delay, jitter and transmission rate) can be achieved for interactive applications using dynamic bandwidth allocation schemes over cellular networks. In this work, we propose a dynamic bandwidth allocation scheme for interactive multimedia applications with/without background load in the cellular networks. The system has been simulated for many application types running in parallel and it has been observed that if interactive applications are to be provided with decent response time, a periodic overhauling of policy at admission control has to be done by taking into account history, criticality of applications. The results demonstrate that interactive appli- cations can be provided with good service if policy database at admission control is reviewed dynamically.

Study of the Formation of Nano-Networks in Colloidal Particles

The authors studied the formation of a wafer-scale network of connected colloidal beads by reactive ion etching. The dimensions of the connections have been studied as a function of etching time for colloidal beads of different sizes, and could be well controlled. The authors have found that the nano-network forms and disappears for the same time of etching independent of the diameter of the polystyrene beads. With recent interest of connected colloidal networks in various optical sensing applications, such as photonic crystals, as surface-enhanced Raman scattering substrates, the studies have potential uses in the development of wafer-scale nanophotonic sensors.

Limit laws for coverage in backbone-sensor networks

We study coverage in sensor networks having two types of nodes, namely, sensor nodes and backbone nodes. Each sensor is capable of transmitting information over relatively small distances. The backbone nodes collect information from the sensors. This information is processed and communicated over an ad hoc network formed by the backbone nodes, which are capable of transmitting over much larger distances. We consider two models of deployment for the sensor and backbone nodes. One is a PoissonPoisson cluster model and the other a dependently thinned Poisson point process. We deduce limit laws for functionals of vacancy in both models using properties of association for random measures.

Vibration spectrum analyzer using stretched membranes of polymer piezoelectrics for sensor networks

The key problem tackled in this paper is the development of a stand-alone self-powered sensor to directly sense the spectrum of mechanical vibrations. Such a sensor could be deployed in wide area sensor networks to monitor structural vibrations of large machines (e. g. aircrafts) and initiate corrective action if the structure approaches resonance. In this paper, we study the feasibility of using stretched membranes of polymer piezoelectric polyvinlidene fluoride for low-frequency vibration spectrum sensing. We design and evaluate a low-frequency vibration spectrum sensor that accepts an incoming vibration and directly provides the spectrum of the vibration as the output.

Phase synchronization in brain networks derived from correlation between probabilities of recurrences in functional MRI data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric ``correlation between probabilities of recurrence'' (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

Throughput analysis of primary and secondary networks in a shared IEEE 802.11 system

In this paper, we analyze the coexistence of a primary and a secondary (cognitive) network when both networks use the IEEE 802.11 based distributed coordination function for medium access control. Specifically, we consider the problem of channel capture by a secondary network that uses spectrum sensing to determine the availability of the channel, and its impact on the primary throughput. We integrate the notion of transmission slots in Bianchi's Markov model with the physical time slots, to derive the transmission probability of the secondary network as a function of its scan duration. This is used to obtain analytical expressions for the throughput achievable by the primary and secondary networks. Our analysis considers both saturated and unsaturated networks. By performing a numerical search, the secondary network parameters are selected to maximize its throughput for a given level of protection of the primary network throughput. The theoretical expressions are validated using extensive simulations carried out in the Network Simulator 2. Our results provide critical insights into the performance and robustness of different schemes for medium access by the secondary network. In particular, we find that the channel captures by the secondary network does not significantly impact the primary throughput, and that simply increasing the secondary contention window size is only marginally inferior to silent-period based methods in terms of its throughput performance.

Effect of lipopolysaccharide on alteration of phospholipids and their fatty acid composition in spleen and thymus by in vitro metabolic labeling

Lipopolysaccharide (LPS) is an endotoxin, a potent stimulator of immune response and induction of LPS leads to acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). ARDS is a life-threatening disease worldwide with a high mortality rate. The immunological effect of LPS with spleen and thymus is well documented; however the impact on membrane phospholipid during endotoxemia has not yet been studied. Hence we aimed to investigate the influence of LPS on spleen and thymus phospholipid and fatty acid composition by 32P]orthophosphate labeling in rats. The in vitro labeling was carried out with phosphate-free medium (saline). Time course, LPS concentration-dependent, pre- and post-labeling with LPS and fatty acid analysis of phospholipid were performed. Labeling studies showed that 50 mu g LPS specifically altered the major phospholipids, phosphatidylcholine and phosphatidylglycerol in spleen and phosphatidylcholine in thymus. Fatty acid analysis showed a marked alteration of unsaturated fatty acids/saturated fatty acids in spleen and thymus leading to immune impairment via the fatty acid remodeling pathway. Our present in vitro lipid metabolic labeling study could open up new vistas for exploring LPS-induced immune impairment in spleen and thymus, as well as the underlying mechanism.

Governing the local networks in Indian agrarian societies-an MAS perspective

In a networked society, governing advocacy groups and networks through decentralized systems of policy implementation has been the interest of governance network literature. This paper addresses the topic of governing networks in the context of Indian agrarian societies by taking the case example of a welfare scheme for the Indian rural poor. We explore context-specific regulatory dynamics through the situated agent based architectural framework. The effects of various regulatory strategies that can be adopted by governing node are tested under various action arenas through experimental design. Results show the impact of regulatory strategies on the resource dependencies and asymmetries in the network relationships. This indicates that the optimal feasible regulatory strategy in networked society is institutionally rational and is context dependent. Further, we show that situated MAS architecture is a natural fit for institutional understanding of the dynamics (Ostrom et al. in Rules, games, and common-pool resources, 1994).

Method of designing an access mechanism for social networks

The rapid development of communication and networking has lessened geographical boundaries among actors in social networks. In social networks, actors often want to access databases depending upon their access rights, privacy, context, privileges, etc. Managing and handling knowledge based access of actors is complex and hard for which broad range of technologies need to be called. Access based on dynamic access rights and circumstances of actors impose major tasks on access systems. In this paper, we present an Access Mechanism for Social Networks (AMSN) to render access to actors over databases taking privacy and status of actors into consideration. The designed AMSN model is tested over an Agriculture Social Network (ASN) which utilises distinct access rights and privileges of actors related to the agriculture occupation, and provides access to actors over databases.

DMT of parallel-path and layered networks under the half-duplex constraint

In this paper, we study the diversity-multiplexing-gain tradeoff (DMT) of wireless relay networks under the half-duplex constraint. It is often unclear what penalty if any, is imposed by the half-duplex constraint on the DMT of such networks. We study two classes of networks; the first class, called KPP(I) networks, is the class of networks with the relays organized in K parallel paths between the source and the destination. While we assume that there is no direct source-destination path, the K relaying paths can interfere with each other. The second class, termed as layered networks, is comprised of relays organized in layers, where links exist only between adjacent layers. We present a communication scheme based on static schedules and amplify-and-forward relaying for these networks. We also show that for KPP(I) networks with K >= 3, the proposed schemes can achieve full-duplex DMT performance, thus demonstrating that there is no performance hit on the DMT due to the half-duplex constraint. We also show that, for layered networks, a linear DMT of d(max)(1 - r)(+) between the maximum diversity d(max) and the maximum MG, r(max) = 1 is achievable. We adapt existing DMT optimal coding schemes to these networks, thus specifying the end-to-end communication strategy explicitly.

Stochastic analysis of epidemics on adaptive time varying networks

Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an ``adaptive threshold,'' i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.

Smartconnect: a system for the design and deployment of wireless sensor networks

We have developed SmartConnect, a tool that addresses the growing need for the design and deployment of multihop wireless relay networks for connecting sensors to a control center. Given the locations of the sensors, the traffic that each sensor generates, the quality of service (QoS) requirements, and the potential locations at which relays can be placed, SmartConnect helps design and deploy a low-cost wireless multihop relay network. SmartConnect adopts a field interactive, iterative approach, with model based network design, field evaluation and relay augmentation performed iteratively until the desired QoS is met. The design process is based on approximate combinatorial optimization algorithms. In the paper, we provide the design choices made in SmartConnect and describe the experimental work that led to these choices. Finally, we provide results from some experimental deployments.

Mining large-scale response networks reveals `topmost activities' in Mycobacterium tuberculosis infection

Mycobacterium tuberculosis owes its high pathogenic potential to its ability to evade host immune responses and thrive inside the macrophage. The outcome of infection is largely determined by the cellular response comprising a multitude of molecular events. The complexity and inter-relatedness in the processes makes it essential to adopt systems approaches to study them. In this work, we construct a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions. We then compute response networks based on available gene expression profiles corresponding to states of health, disease and drug treatment. We use a novel formulation for mining response networks that has led to identifying highest activities in the cell. Highest activity paths provide mechanistic insights into pathogenesis and response to treatment. The approach used here serves as a generic framework for mining dynamic changes in genome-scale protein interaction networks.