Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications

Self-stabilizing k-clustering in mobile ad hoc networks

In this thesis, two silent self-stabilizing asynchronous distributed algorithms are given for constructing a k-clustering of a connected network of processes. These are the first self-stabilizing solutions to this problem. One algorithm, FLOOD, takes O( k) time and uses O(k log n) space per process, while the second algorithm, BFS-MIS-CLSTR, takes O(n) time and uses O(log n) space; where n is the size of the network. Processes have unique IDs, and there is no designated leader. BFS-MIS-CLSTR solves three problems; it elects a leader and constructs a BFS tree for the network, constructs a minimal independent set, and finally a k-clustering. Finding a minimal k-clustering is known to be NP -hard. If the network is a unit disk graph in a plane, BFS-MIS-CLSTR is within a factor of O(7.2552k) of choosing the minimal number of clusters; A lower bound is given, showing that any comparison-based algorithm for the k-clustering problem that takes o( diam) rounds has very bad worst case performance; Keywords: BFS tree construction, K-clustering, leader election, MIS construction, self-stabilization, unit disk graph

Simplistic vs. Complex Organization: Markets, Hierarchies, and Networks in an 'Organizational Triangle'

Transaction cost economics explains organizations in a simplistic ‘market-vs.-hierarchy’ dichotomy. In this view, complex real-world coordination forms are simply considered ‘hybrids’ of those ‘pure’ and ideal forms, thus being located on a one-dimensional ‘line’ between them. This ‘organizational dichotomy’ is mainly based on relative marginal transaction costs, relative lengths of value-added chains, and ‘rational choice’ of coordination form. The present paper, in contrast, argues that pure ‘market’ and ‘hierarchy’, even including their potential hybrids, are a theoretically untenable and empirically void set. Coordination forms, it is argued, have to be conceptualized in a fundamentally different way. A relevant ‘organizational space’ must reflect the dimensions of a complex world such as dilemma-prone direct interdependence, resulting in strong strategic uncertainty, mutual externalities, collectivities, and subsequent emergent process. This, in turn, will lead either to (1) informally institutionalized, problem-solving cooperation (the instrumental dimension of the institution) or (2) mutual blockage, lock-in on an inferior path, or power- and status-based market and hierarchy failure (the ceremonial dimension of the institution). This paper establishes emergent instrumental institutionalized cooperation as a genuine organizational dimension which generates a third ‘attractor’ besides ‘market’ and ‘hierarchy’, i.e., informal network. In this way, an ‘organizational triangle’ can be generated which may serve as a more relevant heuristic device for empirical organizational research. Its ideal corners and some ideal hybrids on its edges (such as ideal clusters and ideal hub&spoke networks) still remain empirically void, but its inner space becomes empirically relevant and accessible. The ‘Organizational Triangle’ is tentatively applied (besides casual reference to corporate behavior that has lead to the current financial meltdown), by way of a set of criteria for instrumental problem-solving and a simple formal algorithm, to the cases of the supplier network of ‘DaimlerChrysler US International’ at Tuscaloosa, AL, the open-source network Linux, and the web-platforms Wikipedia and ‘Open-Source Car’. It is considered to properly reflect what is generally theorized in evolutionary-institutional economics of organizations and the firm and might offer some insight for the coming industrial reconstructions of the car and other industries.Market vs. Hierarchy; Transaction Costs; Complexity; Institutionalization; Network Formation; Hub&Spoke Supplier Networks; Open-Source Networks

Self-stabilizing interval routing algorithm with low stretch factor

A compact routing scheme is a routing strategy which suggests routing tables that are space efficient compared to traditional all-pairs shortest path routing algorithms. An Interval Routing algorithm is a compact routing algorithm which uses a routing table at every node in which a set of destination addresses that use the same output port are grouped into intervals of consecutive addresses. Self-stabilization is a property by which a system is guaranteed to reach a legitimate state in a finite number of steps starting from any arbitrary state. A self-stabilizing Pivot Interval Routing (PIR) algorithm is proposed in this work. The PIR strategy allows routing along paths whose stretch factor is at most five, and whose average stretch factor is at most three with routing tables of size O(n3/2log 23/2n) bits in total, where n is the number of nodes in the network. Stretch factor is the maximum ratio taken over all source-destination pairs between the length of the paths computed by the routing algorithm and the distance between the source and the destination. PIR is also an Interval Routing Scheme (IRS) using at most 2n( 1+lnn)1/2 intervals per link for the weighted graphs and 3n(1+ lnn)1/2 intervals per link for the unweighted graphs. The preprocessing stage of the PIR algorithm consists of nodelabeling and arc-labeling functions. The nodelabeling function re-labels the nodes with unique integers so as to facilitate fewer number of intervals per arc. The arc-labeling is done in such a fashion that the message delivery protocol takes an optimal path if both the source and the destination are located within a particular range from each other and takes a near-optimal path if they are farther from each other

Making local algorithms efficiently self-stabilizing in arbitrary asynchronous environments

This paper deals with the trade-off between time, workload, and versatility in self-stabilization, a general and lightweight fault-tolerant concept in distributed computing.In this context, we propose a transformer that provides an asynchronous silent self-stabilizing version Trans(AlgI) of any terminating synchronous algorithm AlgI. The transformed algorithm Trans(AlgI) works under the distributed unfair daemon and is efficient both in moves and rounds.Our transformer allows to easily obtain fully-polynomial silent self-stabilizing solutions that are also asymptotically optimal in rounds.We illustrate the efficiency and versatility of our transformer with several efficient (i.e., fully-polynomial) silent self-stabilizing instances solving major distributed computing problems, namely vertex coloring, Breadth-First Search (BFS) spanning tree construction, k-clustering, and leader election

Understanding Allosteric Modulation of G-Protein Coupled Receptors

G protein-coupled receptors (GPCRs) which are seven-transmembrane allosteric machine constitutes largest and diverse family of membrane proteins. GPCR participate in activating a diverse range of signaling pathways, in response to ligand perturbation which ranges from neurotransmitters, hormones to photons. The role of GPCRs in a wide range of key physiological processes and their ubiquity in mammalian genome makes them attractive pharmaceutical targets. Signal transduction in GPCR occur mainly, via G-proteins and leads to a cascade of signaling. In addition to the orthosteric site, GPCRs also possesses a topographically distinct allosteric site which contributes to allosteric modulation, i.e long distant ligand binding for activating G proteins and trigger GDP release. The mechanism that governs allosteric activation triggering GDP release is yet uncertain. Differential ligands bind to GPCR\u27s orthosteric sites and can modulate allosteric signaling. Ligands that increase or decrease the GPCR signaling are classified as agonists and antagonists respectively. Compared to orthosteric ligand allosteric modulator through electrostatic repulsion, steric hindrance or conformational stability can select subsets of signaling responses. We in this study are trying to understand the basis of ligand-biased signaling or functional selectivity that leads to long-distance signaling in a receptor. Using the information from crystal structures of the receptor, combined with molecular dynamics simulations, we performed a systematic analysis to identify the basis of conformational selectivity for allosteric bias in GPCRs. Our study explores the conformational landscape of GPCRs as a function of the activity of the receptor. Normal modes analysis (NMA) was used to identify low-frequency modes that describe conformational changes due to large-scale domain motions in the receptor. NMA characterized changes in correlated motions of residues in the rest six global modes and revealed conformation shift starting from the inactive structure. We used MD simulations coupled with network analysis to reveal correlated motion between G-protein Coupling site and ligand binding site. Changes in dynamically correlated residue motion in allosteric networks reveals the characteristic feature of receptor activity in GPCRs. Single point mutations studies were aimed to analyze the changes in the structural scaffold of GPCRs as a result of mutations. Mutational studies facilitated in determining the basis of functional selectivity and changes in the allosteric communication as a result of allosteric binding to the receptor. Single point mutations also revealed residues critical for functional activity of GPCRs. Inter residue contact network responsible for biased signaling using microsecond atomic level simulations reveals differential allosteric modulation. Finally, comparative analysis using mutual information in the internal coordinates of mutants and wild types helped to quantify the allosteric modulation and long-range cooperativity between binding sites in GPCRs

A Framework for Certified Self-Stabilization

We propose a general framework to build certified proofs of distributed self-stabilizing algorithms with the proof assistant Coq. We first define in Coq the locally shared memory model with composite atomicity, the most commonly used model in the self-stabilizing area. We then validate our framework by certifying a non trivial part of an existing silent self-stabilizing algorithm which builds a $k$-hop dominating set of the network. We also certified a quantitative property related to the output of this algorithm. Precisely, we show that the computed $k$-hop dominating set contains at most $\lfloor \frac{n-1}{k+1} \rfloor + 1$ nodes, where $n$ is the number of nodes in the network. To obtain these results, we also developed a library which contains general tools related to potential functions and cardinality of sets