A Statistical Geometry Approach to Distance Estimation in Wireless Sensor Networks

none3Algorithmic approaches to the estimation of pairwise distances between the nodes of a wireless sensor network are highly attractive to provide information for routing and localization without requiring specific hardware to be added to cost/resource-constrained nodes. This paper exploits statistical geometry to derive robust estimators of the pairwise Euclidean distances from topological information typically available in any network. Extensive Monte Carlo experiments conducted on synthetic benchmarks demonstrate the improved quality of the proposed estimators with respect to the state of the art.openV. Freschi; E. Lattanzi; A. BoglioloFreschi, Valerio; Lattanzi, Emanuele; Bogliolo, Alessandr

Combating Trafficking in Persons: A directory of organisations

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.ASI_2003_HT_UK_Combating_Trafficking.pdf: 445 downloads, before Oct. 1, 2020

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance

Host Based Intrusion Detection for VANETs: A statistical approach to Rogue Node Detection

In this paper, an intrusion detection system (IDS) for vehicular ad hoc networks (VANETs) is proposed and evaluated. The IDS is evaluated by simulation in the presence of rogue nodes (RNs) that can launch different attacks. The proposed IDS is capable of detecting a false information attack using statistical techniques effectively and can also detect other types of attacks. First, the theory and implementation of the VANET model that is used to train the IDS is discussed. Then, an extensive simulation and analysis of our model under different traffic conditions is conducted to identify the effects of these parameters in VANETs. In addition, the extensive data gathered in the simulations are presented using graphical and statistical techniques. Moreover, RNs are introduced in the network, and an algorithm is presented to detect these RNs. Finally, we evaluate our system and observe that the proposed application-layer IDS based on a cooperative information exchange mechanism is better for dynamic and fast-moving networks such as VANETs, as compared with other techniques available

Multi -Layer Based Data Aggregation Algorithm for Convergence Platform of IoT and Cloud Computing

Sensor Networks (SN) are deployed in smart domain to sense the environment which is essential to provide the services according to the users need. Hundreds or sometimes thousands of sensors are involved in sensor networks for monitoring the target phenomenon. Large scale of sensory data have to be handle by the sensor network which create several problems such as waste of sensors energy, data redundancy. To overcome these deficiencies one most practice solution is data aggregation which can effectively decrease the massive amount of data generated in SNs by lessening occurrence in the sensing data. The aim of this method is to lessen the massive use of data generated by surrounding nodes, thus saving network energy and providing valuable information for the end user. The effectiveness of any data aggregation technique is largely dependent on topology of the network. Among the various network topologies clustering is preferred as it provides better controllability, scalability and network maintenance phenomenon. In this research, a data aggregation technique is proposed based on Periodic Sensor Network (PSN) which achieved aggregation of data at two layers: the sensor nodes layer and the cluster head layer. In sensor node layer set similarity function is used for checking the redundant data for each sensor node whereas Euclidean distance function is utilized in cluster head layer for discarding the redundancy of data between different sensor nodes. This aggregation technique is implemented in smart home where sensor network is deployed to capture environment related information (temperature, moisture, light, H2 level). Collected information is analyzed using ThinkSpeak cloud platform. For performance evaluation amount of aggregated data, number of pairs of redundant data, energy consumption, data latency, and data accuracy are analyzed and compared with the other state-of-art techniques. The result shows the important improvement of the performance of sensor networks

A measure preserving mapping for structured Grassmannian constellations in SIMO channels

In this paper, we propose a new structured Grassmannian constellation for noncoherent communications over single-input multiple-output (SIMO) Rayleigh block-fading channels. The constellation, which we call Grass-Lattice, is based on a measure preserving mapping from the unit hypercube to the Grassmannian of lines. The constellation structure allows for on-the-fly symbol generation, low-complexity decoding, and simple bit-to-symbol Gray coding. Simulation results show that Grass-Lattice has symbol error rate performance close to that of a numerically optimized unstructured constellation, and is more power efficient than other structured constellations proposed in the literature.This work was supported by Huawei Technologies Sweden, under the project GRASSCOM. The work of D. Cuevas was also partly supported under grant FPU20/03563 funded by Ministerio de Universidades (MIU), Spain. The work of Carlos Beltr´an was also partly supported under grant PID2020-113887GB-I00 funded by MCIN/AEI /10.13039/501100011033. The work ofI. Santamaria was also partly supported under grant PID2019-104958RB-C43(ADELE) funded by MCIN/ AEI /10.13039/501100011033

EHRO-N 2012 Annual Activity Report

This report contains information on the activities performed under the framework of EHRO-N or the European Human Resource Observatory for the Nuclear Energy Sector in the year 2012. The mission of EHRO-N is to provide 1) qualified data on the needs regarding human resources in the nuclear field within the European Union, and 2) high-level expert recommendations on EU-wide nuclear E&T actions, promoting lifelong learning and cross border mobility. Following the EHRO-N objectives numerous activities were performed in 2012. These fall under the following headings in the present report: • Two Senior Advisory Group (SAG) meetings • E&I workshop and a visit to the Energy Institute of the Istanbul Technical University • EHRO-N “Putting into Perspective” Report 2012 • EHRO-N presence and presentation of its activities at the VGB training and career event (in original: VGB Studentenkurs „Kerntechnik“) • EHRO-N Contribution to the SET-Plan • Preparation of the EHRO-N Roadmap 2020 preparation • Launch of the survey on the Mobility of Nuclear Professionals • Attendance at conferences (ENC 2012) • Studies by EHRO-N • Guidelines on the way to produce a capacity building exercise nationally • Contributions to other DGs of the EC • Networking and EHRO-N relations with other organisations • EHRO-N website • ECVET ActivitiesJRC.F.4-Nuclear Reactor Integrity Assessment and Knowledge Managemen

An investigation into the irregular military dynamics in Yugoslavia, 1992-1995

Abstract This dissertation makes an original contribution to knowledge of how irregular military actors operate in modern mass atrocity crises, providing an evidencebased multi-perspective analysis of the irregular military dynamics that accompanied the violent collapse of Yugoslavia (1991-1995). While it is broadly accepted that paramilitary or irregular units have been involved in practically every case of genocide in the modern world, detailed analysis of these dynamics is rare. A consequence of paramilitary participation in atrocity crises –which can be seen in academic literature, policy-making, and in popular understanding– has been to mask the continued dominance of the state in a number of violent crises where, instead of a vertically organised hierarchical structure of violence, irregular actors have comprised all or part of the military force. Here, analysis of structures of command and control, and of domestic and international networks, presents the webs of support that enable and encourage irregular military dynamics. The findings suggest that irregular combatants have participated to such an extent in the perpetration of atrocity crimes because political elites benefit by using unconventional forces to fulfil devastating socio-political ambitions, and because international policy responses are hindered by contexts where responsibility for violence is ambiguous. The research also reveals how grassroots armed resistance can be temporarily effective but, without the benefits of centralised capabilities, cannot be easily sustained. While the variety of irregular military activity that took place in former Yugoslavia was significant, it is clear that the irregular dynamics were more substantial and more effective when operating within, or in close coordination with, structures where the state retained greater powers of central command and control. Furthermore, the dissertation identifies substantial loopholes in current atrocity prevention architecture and suggests the utilisation by state authorities of irregular combatants as perpetrators in atrocity contexts will continue until these loopholes are addressed