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Above and beyond a mere modulation of neuronal activity on a focal level, subthalamic-deep brain stimulation (STN-DBS) unfolds its substantial potential in the therapy of Parkinson’s disease (PD) symptomatology by recalibrating widespread dysfunctional brain networks. Crucially, given the non-monolithic, multisymptomatic disease structure of PD, the optimal therapy of each different symptom domain may require modulating distinct “symptomatotopic” networks. Apart from evidence regarding motor deficits paradigmatic for PD, prior attempts to identify such symptom-specific circuit targets have, however, often neglected the effects of remote connectivity seeding from precisely placed electrodes on the neuropsychiatric symptom spectrum. Within our project, we thus aim at leveraging DBS network mapping by combining patient-specific electrode locations derived from magnetic resonance imaging (MRI) and computer tomography with a normative PD connectome (diffusion-weighted tractography and resting state functional MRI). Based on these training data, we strive to model and cross-validate segregated symptom-specific, structural and functional connectivity profiles passing by STN-DBS electrode stimulation sites, which are predictive of patients’ improvement or worsening in neuropsychiatric assessments. We will perform parameter optimization and model selection on a training and cross-validation set and validate final results in a left-out independent test dataset. Secondly, by integrating these domain-specific beneficial and detrimental connectivity profiles into a convergent circuit target model which can be weighted by individual intensities of symptom constellations, we hope to pave the way towards personalized precision neuromodulation targeting. Keywords: STN-DBS, PD Non-Motor Symptoms, DBS Network Mapping, Discriminative Fibertracts Analysis, Connectomic DBS, Optimal DBS Stimulation Site, DTI, rs-fMRI

This paper considers a multicloud radio access network (M-CRAN), wherein each cloud serves a cluster of base-stations (BS's) which are connected to the clouds through high capacity digital links. The network comprises several remote users, where each user can be connected to one (and only one) cloud. This paper studies the user-to-cloud-assignment problem by maximizing a network-wide utility subject to practical cloud connectivity constraints. The paper solves the problem by using an auction-based iterative algorithm, which can be implemented in a distributed fashion through a reasonable exchange of information between the clouds. The paper further proposes a centralized heuristic algorithm, with low computational complexity. Simulations results show that the proposed algorithms provide appreciable performance improvements as compared to the conventional cloud-less assignment solutions.

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that only depends on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules (long-term potentiation and long-term depression) can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both via simulation and analytically, how different forms of edge-weight update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.

We construct team-optimal estimation algorithms over distributed networks for state estimation in the finite-horizon mean-square error (MSE) sense. Here, we have a distributed collection of agents with processing and cooperation capabilities. These agents observe noisy samples of a desired state through a linear model and seek to learn this state by interacting with each other. Although this problem has attracted significant attention and been studied extensively in fields including machine learning and signal processing, all the well-known strategies do not achieve team-optimal learning performance in the finite-horizon MSE sense. To this end, we formulate the finite-horizon distributed minimum MSE (MMSE) when there is no restriction on the size of the disclosed information, i.e., oracle performance, over an arbitrary network topology. Subsequently, we show that exchange of local estimates is sufficient to achieve the oracle performance only over certain network topologies. By inspecting these network structures, we propose recursive algorithms achieving the oracle performance through the disclosure of local estimates. For practical implementations we also provide approaches to reduce the complexity of the algorithms through the time-windowing of the observations. Finally, in the numerical examples, we demonstrate the superior performance of the introduced algorithms in the finite-horizon MSE sense due to optimal estimation.

We present a distributed algorithm for joint power control, routing and scheduling in multihop wireless networks. The algorithm also provides for Quality of Service (QoS) guarantees, namely, end-to-end mean delay guarantees and hard deadline guarantees, for different users. It is easily implementable and works by giving local dynamic priority to flows requiring QoS, the priority being a function of the queue length at the nodes. We provide theoretical bounds for the stability properties of the algorithm. We also compare the performance of the algorithm with other existing algorithms by means of extensive simulations, and demonstrate its efficacy in providing QoS on demand.

This paper considers the optimization-based traffic allocation problem among multiple end points in connectionless networks. The network utility function is modeled as a non-concave function, since it is the best description of the quality of service perceived by users with inelastic applications, such as video and audio streaming. However, the resulting non-convex optimization problem, is challenging and requires new analysis and solution techniques. To overcome these challenges, we first propose a hierarchy of problems whose optimal value converges to the optimal value of the non-convex optimization problem as the number of moments tends to infinity. From this hierarchy of problems, we obtain a convex relaxation of the original non-convex optimization problem by considering truncated moment sequences. For solving the convex relaxation, we propose a fully distributed iterative algorithm, which enables each node to adjust its date allocation/ rate adaption among any given set of next hops solely based on information from the neighboring nodes. Moreover, the proposed traffic allocation algorithm converges to the optimal value of the convex relaxation at a $O(1/K)$ rate, where $K$ is the iteration counter, with a bounded optimality. At the end of this paper, we perform numerical simulations to demonstrate the soundness of the developed algorithm.

The Wirelessly Networked Aperstructure Digital Phased Array Radar (WNADPAR) applies three relatively new concepts: Opportunistic, Aperstructure, and Wirelessly Networked Digital Architecture concepts. Using this approach almost the full length of the ship becomes the aperture of the phased array radar by placing the antenna elements at available open areas and having the power supply as the only wired connection. This thesis research addressed the wireless networking of the full-scale radar system. An analysis of the various existing and newly developed wireless technologies and guided transmission media was conducted to determine a baseline approach for the full system implementation. A two-element array demonstrator was wirelessly networked and tested to allow wireless communication between the central beamformer and controller and the T/R modules. Control and monitoring software was developed in LabVIEW that allows simultaneous transmission and reception in both T/R modules. Finally, a number of tests and measurements were conducted to validate the operation of the two-element array demonstrator while transmitting the control data wirelessly.

This paper develops a distributed algorithm for rate allocation in wireless networks that achieves the same throughput region as optimal centralized algorithms. This cross-layer algorithm jointly performs medium access control (MAC) and physical-layer rate adaptation. The paper establishes that this algorithm is throughput-optimal for general rate regions. In contrast to on-off scheduling, rate allocation enables optimal utilization of physical-layer schemes by scheduling multiple rate levels. The algorithm is based on local queue-length information, and thus the algorithm is of significant practical value. The algorithm requires that each link can determine the global feasibility of increasing its current data-rate. In many classes of networks, any one link's data-rate primarily impacts its neighbors and this impact decays with distance. Hence, local exchanges can provide the information needed to determine feasibility. Along these lines, the paper discusses the potential use of existing physical-layer control messages to determine feasibility. This can be considered as a technique analogous to carrier sensing in CSMA (Carrier Sense Multiple Access) networks. An important application of this algorithm is in multiple-band multiple-radio throughput-optimal distributed scheduling for white-space networks.

This paper considers a heterogeneous ad hoc network with multiple transmitter-receiver pairs, in which all transmitters are capable of harvesting renewable energy from the environment and compete for one shared channel by random access. In particular, we focus on two different scenarios: the constant energy harvesting (EH) rate model where the EH rate remains constant within the time of interest and the i.i.d. EH rate model where the EH rates are independent and identically distributed across different contention slots. To quantify the roles of both the energy state information (ESI) and the channel state information (CSI), a distributed opportunistic scheduling (DOS) framework with two-stage probing and save-then-transmit energy utilization is proposed. Then, the optimal throughput and the optimal scheduling strategy are obtained via one-dimension search, i.e., an iterative algorithm consisting of the following two steps in each iteration: First, assuming that the stored energy level at each transmitter is stationary with a given distribution, the expected throughput maximization problem is formulated as an optimal stopping problem, whose solution is proved to exist and then derived for both models; second, for a fixed stopping rule, the energy level at each transmitter is shown to be stationary and an efficient iterative algorithm is proposed to compute its steady-state distribution. Finally, we validate our analysis by numerical results and quantify the throughput gain compared with the best-effort delivery scheme.

We consider a two-hop wireless network where a transmitter communicates with a receiver via $M$ relays with an amplify-and-forward (AF) protocol. Recent works have shown that sophisticated linear processing such as beamforming and distributed space-time coding (DSTC) at relays enables to improve the AF performance. However, the relative utility of these strategies depend on the available channel state information at transmitter (CSIT), which in turn depends on system parameters such as the speed of the underlying fading channel and that of training and feedback procedures. Moreover, it is of practical interest to have a single transmit scheme that handles different CSIT scenarios. This motivates us to consider a unified approach based on DSTC that potentially provides diversity gain with statistical CSIT and exploits some additional side information if available. Under individual power constraints at the relays, we optimize the amplifier power allocation such that pairwise error probability conditioned on the available CSIT is minimized. Under perfect CSIT we propose an on-off gradient algorithm that efficiently finds a set of relays to switch on. Under partial and statistical CSIT, we propose a simple waterfilling algorithm that yields a non-trivial solution between maximum power allocation and a generalized STC that equalizes the averaged amplified noise for all relays. Moreover, we derive closed-form solutions for M=2 and in certain asymptotic regimes that enable an easy interpretation of the proposed algorithms. It is found that an appropriate amplifier power allocation is mandatory for DSTC to offer sufficient diversity and power gain in a general network topology.

We present an algorithm for boundary approximation in locally-linked sensor networks that communicate with a remote monitoring station. Delaunay triangulations and Voronoi diagrams are used to generate a sensor communication network and define boundary segments between sensors, respectively. The proposed algorithm reduces remote station communication by approximating boundaries via a decentralized computation executed within the sensor network. Moreover, the algorithm identifies boundaries based on differences between neighboring sensor readings, and not absolute sensor values. An analysis of the bandwidth consumption of the algorithm is presented and compared to two naive approaches. The proposed algorithm reduces the amount of remote communication (compared to the naive approaches) and becomes increasingly useful in networks with more nodes.

We consider the min-cost multicast problem (under network coding) with multiple correlated sources where each terminal wants to losslessly reconstruct all the sources. We study the inefficiency brought forth by the selfish behavior of the terminals in this scenario by modeling it as a noncooperative game among the terminals. The degradation in performance due to the lack of regulation is measured by the {\it Price of Anarchy} (POA), which is defined as the ratio between the cost of the worst possible \textit{Wardrop equilibrium} and the socially optimum cost. Our main result is that in contrast with the case of independent sources, the presence of source correlations can significantly increase the price of anarchy. Towards establishing this result, we first characterize the socially optimal flow and rate allocation in terms of four intuitive conditions. Next, we show that the Wardrop equilibrium is a socially optimal solution for a different set of (related) cost functions. Using this, we construct explicit examples that demonstrate that the POA $> 1$ and determine near-tight upper bounds on the POA as well. The main techniques in our analysis are Lagrangian duality theory and the usage of the supermodularity of conditional entropy.

We consider a source-destination pair assisted by parallel regenerative decode-and-forward relays operating in orthogonal channels. We investigate distributed power allocation strategies for this system with limited channel state information at the source and the relay nodes. We first propose a distributed decision mechanism for each relay to individually make its decision on whether to forward the source data. The decision mechanism calls for each relay that is able to decode the information from the source to compare its relay-to-destination channel gain with a given threshold. We identify the optimum distributed power allocation strategy that minimizes the total transmit power while providing a target signal-to-noise ratio at the destination with a target outage probability. The strategy dictates the optimum choices for the source power as well as the threshold value at the relays. Next, we consider two simpler distributed power allocation strategies, namely the passive source model where the source power and the relay threshold are fixed, and the single relay model where only one relay is allowed to forward the source data. These models are motivated by limitations on the available channel state information as well as ease of implementation as compared to the optimum distributed strategy. Simulation results are presented to demonstrate the performance of the proposed distributed power allocation schemes. Specifically, we observe significant power savings with proposed methods as compared to random relay selection.

In wireless ad hoc networks, distributed nodes can collaboratively form an antenna array for long-distance communications to achieve high energy efficiency. In recent work, Ochiai, et al., have shown that such collaborative beamforming can achieve a statistically nice beampattern with a narrow main lobe and low sidelobes. However, the process of collaboration introduces significant delay, since all collaborating nodes need access to the same information. In this paper, a technique that significantly reduces the collaboration overhead is proposed. It consists of two phases. In the first phase, nodes transmit locally in a random access fashion. Collisions, when they occur, are viewed as linear mixtures of the collided packets. In the second phase, a set of cooperating nodes acts as a distributed antenna system and beamform the received analog waveform to one or more faraway destinations. This step requires multiplication of the received analog waveform by a complex number, which is independently computed by each cooperating node, and which enables separation of the collided packets based on their final destination. The scheme requires that each node has global knowledge of the network coordinates. The proposed scheme can achieve high throughput, which in certain cases exceeds one.

In this paper, we consider the problem of remote vector Gaussian source coding for a wireless acoustic sensor network. Each node receives messages from multiple nodes in the network and decodes these messages using its own measurement of the sound field as side information. The node's measurement and the estimates of the source resulting from decoding the received messages are then jointly encoded and transmitted to a neighboring node in the network. We show that for this distributed source coding scenario, one can encode a so-called conditional sufficient statistic of the sources instead of jointly encoding multiple sources. We focus on the case where node measurements are in form of noisy linearly mixed combinations of the sources and the acoustic channel mixing matrices are invertible. For this problem, we derive the rate-distortion function for vector Gaussian sources and under covariance distortion constraints.

We propose a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects. Our method provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT). Multiple mobile agents and objects are localized and tracked using measurements between agents and objects and between agents. For a distributed operation and low complexity, we combine particle-based belief propagation with a consensus or gossip scheme. High localization accuracy is achieved through a probabilistic information transfer between the CS and DT parts of the underlying factor graph. Simulation results demonstrate significant improvements in both agent self-localization and object localization performance compared to separate CS and DT, and very good scaling properties with respect to the numbers of agents and objects.

Development of many futuristic technologies, such as MANET, VANET, iThings, nano-devices, depend on efficient distributed communication protocols in multi-hop ad hoc networks. A vast majority of research in this area focus on design heuristic protocols, and analyze their performance by simulations on networks generated randomly or obtained in practical measurements of some (usually small-size) wireless networks. %some library. Moreover, they often assume access to truly random sources, which is often not reasonable in case of wireless devices. In this work we use a formal framework to study the problem of broadcasting and its time complexity in any two dimensional Euclidean wireless network with uniform transmission powers. For the analysis, we consider two popular models of ad hoc networks based on the Signal-to-Interference-and-Noise Ratio (SINR): one with opportunistic links, and the other with randomly disturbed SINR. In the former model, we show that one of our algorithms accomplishes broadcasting in $O(D\log^2 n)$ rounds, where $n$ is the number of nodes and $D$ is the diameter of the network. If nodes know a priori the granularity $g$ of the network, i.e., the inverse of the maximum transmission range over the minimum distance between any two stations, a modification of this algorithm accomplishes broadcasting in $O(D\log g)$ rounds. Finally, we modify both algorithms to make them efficient in the latter model with randomly disturbed SINR, with only logarithmic growth of performance. Ours are the first provably efficient and well-scalable, under the two models, distributed deterministic solutions for the broadcast task.

We consider a multi-object detection problem over a sensor network (SNET) with limited range multi-modal sensors. Limited range sensing environment arises in a sensing field prone to signal attenuation and path losses. The general problem complements the widely considered decentralized detection problem where all sensors observe the same object. In this paper we develop a distributed detection approach based on recent development of the false discovery rate (FDR) and the associated BH test procedure. The BH procedure is based on rank ordering of scalar test statistics. We first develop scalar test statistics for multidimensional data to handle multi-modal sensor observations and establish its optimality in terms of the BH procedure. We then propose a distributed algorithm in the ideal case of infinite attenuation for identification of sensors that are in the immediate vicinity of an object. We demonstrate communication message scalability to large SNETs by showing that the upper bound on the communication message complexity scales linearly with the number of sensors that are in the vicinity of objects and is independent of the total number of sensors in the SNET. This brings forth an important principle for evaluating the performance of an SNET, namely, the need for scalability of communications and performance with respect to the number of objects or events in an SNET irrespective of the network size. We then account for finite attenuation by modeling sensor observations as corrupted by uncertain interference arising from distant objects and developing robust extensions to our idealized distributed scheme. The robustness properties ensure that both the error performance and communication message complexity degrade gracefully with interference.

In this paper we propose a new cooperative packet transmission scheme that allows independent sources to superimpose over-the-air their packet transmissions. Relay nodes are used and cooperative diversity is combined with distributed space-time block coding (STBC). With the proposed scheme the participating relays create a ST code for the over-the-air superimposed symbols that are received locally and without proceeding to any decoding step beforehand. The advantage of the proposed scheme is that communication is completed in fewer transmission slots because of the concurrent packet transmissions, while the diversity benefit from the use of the STBC results in higher decoding performance. The proposed scheme does not depend on the STBC that is applied at the relays. Simulation results reveal significant throughput benefits even in the low SNR regime.

Wireless sensor networks are often modeled in terms of a dense deployment of smart sensor nodes in a two-dimensional region. Give a node deployment, the \emph{critical geometric graph (CGG)} over these locations (i.e., the connected \emph{geometric graph (GG)} with the smallest radius) is a useful structure since it provides the most accurate proportionality between hop-count and Euclidean distance. Hence, it can be used for GPS-free node localisation as well as minimum distance packet forwarding. It is also known to be asymptotically optimal for network transport capacity and power efficiency. In this context, we propose DISCRIT, a distributed and asynchronous algorithm for obtaining an approximation of the CGG on the node locations. The algorithm does not require the knowledge of node locations or internode distances, nor does it require pair-wise RSSI (Received Signal Strength Indication) measurements to be made. Instead, the algorithm makes use of successful Hello receipt counts (obtained during a Hello-protocol-based neighbour discovery process) as edge weights, along with a simple distributed min-max computation algorithm. In this paper, we first provide the theory for justifying the use of the above edge weights. Then we provide extensive simulation results to demonstrate the efficacy of DISCRIT in obtaining an approximation of the CGG. Finally, we show how the CGG obtained from DISCRIT performs when used in certain network self-organisation algorithms.

This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unit rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems.

This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unit rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems.

We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is a stochastic approximation type algorithm, recently proposed. At each time step k, the state at each sensor is updated by a local averaging of the sensor's own state and the states of its neighbors (consensus) and by accounting for the new observations (innovation). We assume Gaussian, spatially correlated observations. We allow the underlying network be time varying, provided that the graph that collects the union of links that are online at least once over a finite time window is connected. This paper shows through large deviations that, under stated assumptions on the network connectivity and sensors' observations, the running consensus detection asymptotically approaches in performance the optimal centralized detection. That is, the Bayes probability of detection error (with the running consensus detector) decays exponentially to zero as k goes to infinity at the Chernoff information rate-the best achievable rate of the asymptotically optimal centralized detector.

Spectrum management has been identified as a crucial step towards enabling the technology of a cognitive radio network (CRN). Most of the current works dealing with spectrum management in the CRN focus on a single task of the problem, e.g., spectrum sensing, spectrum decision, spectrum sharing or spectrum mobility. In this two-part paper, we argue that for certain network configurations, jointly performing several tasks of the spectrum management improves the spectrum efficiency. Specifically, our aim is to study the uplink resource management problem in a CRN where there exist multiple cognitive users (CUs) and access points (APs). The CUs, in order to maximize their uplink transmission rates, have to associate to a suitable AP (spectrum decision), and to share the channels used by this AP with other CUs (spectrum sharing). These tasks are clearly interdependent, and the problem of how they should be carried out efficiently and in a distributed manner is still open in the literature.

The spectral efficiency of a representative uplink of a given length, in interference-limited, spatially-distributed wireless networks with hexagonal cells, simple power control, and multiantenna linear Minimum-Mean-Square-Error receivers is found to approach an asymptote as the numbers of base-station antennas N and wireless nodes go to infinity. An approximation for the area-averaged spectral efficiency of a representative link (averaged over the spatial base-station and mobile distributions), for Poisson distributed base stations, is also provided. For large N, in the interference-limited regime, the area-averaged spectral efficiency is primarily a function of the ratio of the product of N and the ratio of base-station to wireless-node densities, indicating that it is possible to scale such networks by linearly increasing the product of the number of base-station antennas and the relative density of base stations to wireless nodes, with wireless-node density. The results are useful for designers of wireless systems with high inter-cell interference because it provides simple expressions for spectral efficiency as a function of tangible system parameters like base-station and wireless-node densities, and number of antennas. These results were derived combining infinite random matrix theory and stochastic geometry.

Distributed RC active two-port network and voltage amplifier provides advantage over lumped elements in that second-order bandpass function is obtained with single distributed passive element. Incorporating positive and negative feedback loops provides improvement in Q, sensitivity, and gain-Q sensitivity product compared to single-loop networks.

Distributed RC active two-port network and voltage amplifier provides advantage over lumped elements in that second-order bandpass function is obtained with single distributed passive element. Incorporating positive and negative feedback loops provides improvement in Q, sensitivity, and gain-Q sensitivity product compared to single-loop networks.

Includes bibliographical references (p. 24-26)

Includes bibliographical references (p. 24-26)

Includes bibliographical references (p. 24-26)

This article is from Sensors (Basel, Switzerland) , volume 10 . Abstract In many wireless sensor network applications, the possibility of exceptions occurring is relatively small, so in a normal situation, data obtained at sequential time points by the same node are time correlated, while, spatial correlation may exist in data obtained at the same time by adjacent nodes. A great deal of node energy will be wasted if data which include time and space correlation is transmitted. Therefore, this paper proposes a data compression algorithm for wireless sensor networks based on optimal order estimation and distributed coding. Sinks can obtain correlation parameters based on optimal order estimation by exploring time and space redundancy included in data which is obtained by sensors. Then the sink restores all data based on time and space correlation parameters and only a little necessary data needs to be transmitted by nodes. Because of the decrease of redundancy, the average energy cost per node will be reduced and the life of the wireless sensor network will obviously be extended as a result.

Motivated by the need for fast synchronized operation of power microgrids, we analyze the problem of single and multiple pinning in networked systems. We derive lower and upper bounds on the algebraic connectivity of the network with respect to the reference signal. These bounds are utilized to devise a suboptimal algorithm with polynomial complexity to find a suitable set of nodes to pin the network effectively and efficiently. The results are applied to secondary voltage pinning control design for a microgrid in islanded operation mode. Comparisons with existing single and multiple pinning strategies clearly demonstrates the efficacy of the obtained results.

This paper aims at answering two fundamental questions: how area spectral efficiency (ASE) behaves with different system parameters; how to design an energy-efficient network. Based on stochastic geometry, we obtain the expression and a tight lower-bound for ASE of Poisson distributed networks considering multi-user MIMO (MU-MIMO) transmission. With the help of the lower-bound, some interesting results are observed. These results are validated via numerical results for the original expression. We find that ASE can be viewed as a concave function with respect to the number of antennas and active users. For the purpose of maximizing ASE, we demonstrate that the optimal number of active users is a fixed portion of the number of antennas. With optimal number of active users, we observe that ASE increases linearly with the number of antennas. Another work of this paper is joint optimization of the base station (BS) density, the number of antennas and active users to minimize the network energy consumption. It is discovered that the optimal combination of the number of antennas and active users is the solution that maximizes the energy-efficiency. Besides the optimal algorithm, we propose a suboptimal algorithm to reduce the computational complexity, which can achieve near optimal performance.

This paper aims at answering two fundamental questions: how area spectral efficiency (ASE) behaves with different system parameters; how to design an energy-efficient network. Based on stochastic geometry, we obtain the expression and a tight lower-bound for ASE of Poisson distributed networks considering multi-user MIMO (MU-MIMO) transmission. With the help of the lower-bound, some interesting results are observed. These results are validated via numerical results for the original expression. We find that ASE can be viewed as a concave function with respect to the number of antennas and active users. For the purpose of maximizing ASE, we demonstrate that the optimal number of active users is a fixed portion of the number of antennas. With optimal number of active users, we observe that ASE increases linearly with the number of antennas. Another work of this paper is joint optimization of the base station (BS) density, the number of antennas and active users to minimize the network energy consumption. It is discovered that the optimal combination of the number of antennas and active users is the solution that maximizes the energy-efficiency. Besides the optimal algorithm, we propose a suboptimal algorithm to reduce the computational complexity, which can achieve near optimal performance.

Thesis advisor, Jon T. Butler

This article is from Sensors (Basel, Switzerland) , volume 13 . Abstract An essential application of wireless sensor networks is to successfully respond to user queries. Query packet losses occur in the query dissemination due to wireless communication problems such as interference, multipath fading, packet collisions, etc. The losses of query messages at sensor nodes result in the failure of sensor nodes reporting the requested data. Hence, the reliable and successful dissemination of query messages to sensor nodes is a non-trivial problem. The target of this paper is to enable highly successful query delivery to sensor nodes by localized and energy-efficient discovery, and recovery of query losses. We adopt local and collective cooperation among sensor nodes to increase the success rate of distributed discoveries and recoveries. To enable the scalability in the operations of discoveries and recoveries, we employ a distributed name resolution mechanism at each sensor node to allow sensor nodes to self-detect the correlated queries and query losses, and then efficiently locally respond to the query losses. We prove that the collective discovery of query losses has a high impact on the success of query dissemination and reveal that scalability can be achieved by using the proposed approach. We further study the novel features of the cooperation and competition in the collective recovery at PHY and MAC layers, and show that the appropriate number of detectors can achieve optimal successful recovery rate. We evaluate the proposed approach with both mathematical analyses and computer simulations. The proposed approach enables a high rate of successful delivery of query messages and it results in short route lengths to recover from query losses. The proposed approach is scalable and operates in a fully distributed manner.

Recently Li and Xia have proposed a transmission scheme for wireless relay networks based on the Alamouti space time code and orthogonal frequency division multiplexing to combat the effect of timing errors at the relay nodes. This transmission scheme is amazingly simple and achieves a diversity order of two for any number of relays. Motivated by its simplicity, this scheme is extended to a more general transmission scheme that can achieve full cooperative diversity for any number of relays. The conditions on the distributed space time block code (DSTBC) structure that admit its application in the proposed transmission scheme are identified and it is pointed out that the recently proposed full diversity four group decodable DSTBCs from precoded co-ordinate interleaved orthogonal designs and extended Clifford algebras satisfy these conditions. It is then shown how differential encoding at the source can be combined with the proposed transmission scheme to arrive at a new transmission scheme that can achieve full cooperative diversity in asynchronous wireless relay networks with no channel information and also no timing error knowledge at the destination node. Finally, four group decodable distributed differential space time block codes applicable in this new transmission scheme for power of two number of relays are also provided.

We present an algorithm which computes a planar 2-spanner from an Unit Disk Graph when the node density is sufficient. The communication complexity in terms of number of node's identifier sent by the algorithm is $6n$, while the computational complexity is $O(n\Delta)$, with $\Delta$ the maximum degree of the communication graph. Furthermore, we present a simple and efficient routing algorithm dedicated to the computed graph. Last but not least, using traditional Euclidean coordinates, our algorithm needs the broadcast of as few as $3n$ node's identifiers. Under the hypothesis of sufficient node density, no broadcast at all is needed, reducing the previous best known complexity of an algorithm to compute a planar spanner of an Unit Disk Graph which was of $5n$ broadcasts.

In this paper, the problem of distributed detection in tree networks in the presence of Byzantines is considered. Closed form expressions for optimal attacking strategies that minimize the miss detection error exponent at the fusion center (FC) are obtained. We also look at the problem from the network designer's (FC's) perspective. We study the problem of designing optimal distributed detection parameters in a tree network in the presence of Byzantines. Next, we model the strategic interaction between the FC and the attacker as a Leader-Follower (Stackelberg) game. This formulation provides a methodology for predicting attacker and defender (FC) equilibrium strategies, which can be used to implement the optimal detector. Finally, a reputation based scheme to identify Byzantines is proposed and its performance is analytically evaluated. We also provide some numerical examples to gain insights into the solution.

Wireless Mesh Networks (WMNs) technology has been used in recent years for broadband access in both cities and rural areas. A key development is to equip routers with multiple directional antennas so that these routers can transmit to, or receive from multiple neighbors simultaneously. The Multi-Transmit-Receive (MTR) feature can boost network capacity significantly if suitable scheduling policy is applied. In this paper, we propose a distributed link scheduler called PCP-TDMA that fully utilizes the MTR capability. In particular, it activates every link at least once within the shortest period of time. We evaluated the performance of PCP-TDMA in various network topologies, and compared it against a centralized algorithm called ALGO-2, and two distributed approaches: JazzyMAC and ROMA. The results show that PCP-TDMA achieves similar performance with the centralized algorithm in all scenarios, and outperforms the distributed approaches significantly. Specifically, in a fully connected network, the resulting superframe length of PCP-TDMA is less than 1/3 and 1/2 of JazzyMAC and ROMA, respectively.

The vision of Organic Computing addresses challenges that arise in the design of future information systems that are comprised of numerous, heterogeneous, resource-constrained and error-prone components or devices. Here, the notion organic particularly highlights the idea that, in order to be manageable, such systems should exhibit self-organization, self-adaptation and self-healing characteristics similar to those of biological systems. In recent years, the principles underlying many of the interesting characteristics of natural systems have been investigated from the perspective of complex systems science, particularly using the conceptual framework of statistical physics and statistical mechanics. In this article, we review some of the interesting relations between statistical physics and networked systems and discuss applications in the engineering of organic networked computing systems with predictable, quantifiable and controllable self-* properties.

This paper is concerned with distributed computation of several commonly used centrality measures in complex networks. In particular, we propose deterministic algorithms, which converge in finite time, for the distributed computation of the degree, closeness and betweenness centrality measures in directed graphs. Regarding eigenvector centrality, we consider the PageRank problem as its typical variant, and design distributed randomized algorithms to compute PageRank for both fixed and time-varying graphs. A key feature of the proposed algorithms is that they do not require to know the network size, which can be simultaneously estimated at every node, and that they are clock-free. To address the PageRank problem of time-varying graphs, we introduce the novel concept of persistent graph, which eliminates the effect of spamming nodes. Moreover, we prove that these algorithms converge almost surely and in the sense of $L^p$. Finally, the effectiveness of the proposed algorithms is illustrated via extensive simulations using a classical benchmark.

We study the problem of interference management in large-scale small cell networks, where each user equipment (UE) needs to determine in a distributed manner when and at what power level it should transmit to its serving small cell base station (SBS) such that a given network performance criterion is maximized subject to minimum quality of service (QoS) requirements by the UEs. We first propose a distributed algorithm for the UE-SBS pairs to find a subset of weakly interfering UE-SBS pairs, namely the maximal independent sets (MISs) of the interference graph in logarithmic time (with respect to the number of UEs). Then we propose a novel problem formulation which enables UE-SBS pairs to determine the optimal fractions of time occupied by each MIS in a distributed manner. We analytically bound the performance of our distributed policy in terms of the competitive ratio with respect to the optimal network performance, which is obtained in a centralized manner with NP (non-deterministic polynomial time) complexity. Remarkably, the competitive ratio is independent of the network size, which guarantees scalability in terms of performance for arbitrarily large networks. Through simulations, we show that our proposed policies achieve significant performance improvements (from 150% to 700%) over the existing policies.

In contrast with existing works which rely on the same type of energy-efficiency measure to design distributed power control policies, the present work takes into account the presence of a finite packet buffer at the transmitter side and the impact of transport protocols. This approach is relevant when the transmitters have a non-zero energy cost even when the radiated power is zero. A generalized energy-efficiency performance metric integrating these features is constructed under two different scenarios in terms of transport layer protocols characterized by a constant or an adaptive packet arrival rate. The derived performance metric is shown to have several attractive properties in both scenarios, which ensures convergence of the used distributed power control algorithm to a unique point. This point is the Nash equilibrium of a game for which the equilibrium analysis is conducted. Although the equilibrium analysis methodology is not new in itself, conducting it requires several non-trivial proofs, including the proof of quasi-concavity of the payoff functions. A thorough numerical analysis is provided to illustrate the effects of the proposed approach, and provides several valuable insights in terms of designing interference management policies.

Motivated by current challenges in data-intensive sensor networks, we formulate a coverage optimization problem for mobile visual sensors as a (constrained) repeated multi-player game. Each visual sensor tries to optimize its own coverage while minimizing the processing cost. We present two distributed learning algorithms where each sensor only remembers its own utility values and actions played during the last plays. These algorithms are proven to be convergent in probability to the set of (constrained) Nash equilibria and global optima of certain coverage performance metric, respectively.

Small distributed systems are limited by their main memory to generate massively large graphs. Trivial extension to current graph generators to utilize external memory leads to large amount of random I/O hence do not scale with size. In this work we offer a technique to generate massive scale graphs on small cluster of compute nodes with limited main memory. We develop several distributed and external memory algorithms, primarily, shuffle, relabel, redistribute, and, compressed-sparse-row (csr) convert. The algorithms are implemented in MPI/pthread model to help parallelize the operations across multicores within each core. Using our scheme it is feasible to generate a graph of size $2^{38}$ nodes (scale 38) using only 64 compute nodes. This can be compared with the current scheme would require at least 8192 compute node, assuming 64GB of main memory. Our work has broader implications for external memory graph libraries such as STXXL and graph processing on SSD-based supercomputers such as Dash and Gordon [1][2].

Cellular network performance can significantly benefit from direct device-to-device (D2D) communication, but interference from cochannel D2D communication limits the performance gain. In hybrid networks consisting of D2D and cellular links, finding the optimal interference management is challenging. In particular, we show that the problem of maximizing network throughput while guaranteeing predefined service levels to cellular users is non- convex and hence intractable. Instead, we adopt a distributed approach that is computationally extremely efficient, and requires minimal coordination, communication and cooperation among the nodes. The key algorithmic idea is a signaling mechanism that can be seen as a fictional pricing mechanism, that the base stations optimize and transmit to the D2D users, who then play a best response (i.e., selfishly) to this signal. Numerical results show that our algorithms converge quickly, have low overhead, and achieve a significant throughput gain, while maintaining the quality of cellular links at a predefined service level.

We introduce conferencing-based distributed channel quantizers for two-user interference networks where interference signals are treated as noise. Compared with the conventional distributed quantizers where each receiver quantizes its own channel independently, the proposed quantizers allow multiple rounds of feedback communication in the form of conferencing between receivers. We take the network outage probabilities of sum rate and minimum rate as performance measures and consider quantizer design in the transmission strategies of time sharing and interference transmission. First, we propose distributed quantizers that achieve the optimal network outage probability of sum rate for both time sharing and interference transmission strategies with an average feedback rate of only two bits per channel state. Then, for the time sharing strategy, we propose a distributed quantizer that achieves the optimal network outage probability of minimum rate with finite average feedback rate; conventional quantizers require infinite rate to achieve the same performance. For the interference transmission strategy, a distributed quantizer that can approach the optimal network outage probability of minimum rate closely is also proposed. Numerical simulations confirm that our distributed quantizers based on conferencing outperform the conventional ones.

A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming energy efficient clusters in a self-organized fashion while restricting interference to Primary User (PU) systems. The spectrum-aware clustered structure is presented where the communications consist of intra-cluster aggregation and inter-cluster relaying. In order to save communication power, the optimal number of clusters is derived and the idea of groupwise constrained clustering is introduced to minimize intra-cluster distance under spectrum-aware constraint. In terms of practical implementation, DSAC demonstrates preferable scalability and stability because of its low complexity and quick convergence under dynamic PU activity. Finally, simulation results are given to validate the proposed scheme.

We consider the problem of characterizing and assessing the voltage stability in power distribution networks. Different from previous formulations, we consider the branch-flow parametrization of the power system state, which is particularly effective for radial networks. Our approach to the voltage stability problem is based on a local, approximate, yet highly accurate characterization of the determinant of the power flow Jacobian. Our determinant approximation allows us to construct a voltage stability index that can be computed in a fully scalable and distributed fashion. We provide an upper bound on the approximation error, and we show how the proposed index outperforms other voltage indices that have been recently proposed in the literature.