The impact of interference on the performance of a multi-path metropolitan wireless mesh network

Wireless mesh networks (WMNs) have attracted much attention lately for providing efficiently wireless services with high quality of service (QoS). Metropolitan WMNs are a low-cost solution for providing broadband wireless internet access in large areas. One of the fundamental issues of wireless communications is interference. In WMNs interference can be caused by simultaneous transmissions at links internal to the mesh network or by external sources. In this work we perform extensive measurements in a multi-radio metropolitan WMN deployed in the city of Heraklion, Greece. The basic goal is to investigate the impact of interference on the performance of the multi-path WMN. Towards that goal, we perform measurements with FTP, video streaming and raw data traffic for two scenarios, one with an efficient channel assignment (CA) that accounts for interference and another with a random CA that results in high internal and external interference in the network. The results prove that interference creates severe performance degradation, with regards to high delay, high packet losses, low throughput and low signal-to-interference and noise ratio (SINR). As a result, the metropolitan WMN becomes unable to support multi-path flows and demanding applications with an acceptable QoS. © 2011 IEEE

Fen-Phen Litigation Against American Home Products Corporation: The Widespread Use of Fenfluramine (Pondimin) and Dexfenfluramine (Redux) for Weight Loss, The Health Problems Associated with Those Drugs, the Resulting Litigation Against American Home Prod

The fen-phen affair has spawned thousands of lawsuits and a multitude of scientific studies concerning valvular heart disease. It has also led to a lively debate over off-label prescriptions, medical monitoring, and class actions. The proposed Settlement Agreement, while an attempt to put an end to the valvular litigation, will not be the end of the fen-phen affair. The Settlement Agreement has been widely criticized and plaintiffs' lawyers have indicated that there will be a large number of opt-outs, which could lead to a termination of the Agreement by American Home Products. In addition, PPH claims are generally not settled by the Agreement and plaintiffs are free to pursue those claims in court. American Home Products may be facing liability for fen-phen for years to come, and there is still much to be learned about the valvular heart disease associated with Redux and Pondimin use. In addition, there are new diet drug concerns related to an attempt to recreate fen-pen by the use of Prozac in place of Redux and/or Pondimin, and the new diet drug Meridia works in a very similar way to Redux and Pondimin. The lessons learned from fen-phen will be invaluable in other mass-tort pharmaceutical drug liability situations, and also with other diet drugs that are currently or soon to be on the market

Item Graph Convolution Collaborative Filtering for Inductive Recommendations

Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in the absence of side information, the majority of existing models adopt an approach of randomly initialising the user embeddings and optimising them throughout the training process. This strategy makes these algorithms inherently transductive, curtailing their ability to generate predictions for users that were unseen at training time. To address this issue, we propose a convolution-based algorithm, which is inductive from the user perspective, while at the same time, depending only on implicit user-item interaction data. We propose the construction of an item-item graph through a weighted projection of the bipartite interaction network and to employ convolution to inject higher order associations into item embeddings, while constructing user representations as weighted sums of the items with which they have interacted. Despite not training individual embeddings for each user our approach achieves state of-the-art recommendation performance with respect to transductive baselines on four real-world datasets, showing at the same time robust inductive performance

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Experimentation as a service over semantically interoperable Internet of Things testbeds

Infrastructures enabling experimental assessment of Internet of Things (IoT) solutions are scarce. Moreover, such infrastructures are typically bound to a specific application domain, thus, not facilitating the testing of solutions with a horizontal approach. This paper presents a platform that supports Experimentation as s Service (EaaS) over a federation of IoT testbeds. This platform brings two major advances. Firstly, it leverages semantic web technologies to enable interoperability so that testbed agnostic access to the underlying facilities is allowed. Secondly, a set of tools ease both the experimentation workflow and the federation of other IoT deployments, independently of their domain of interest. Apart from the platform specification, the paper presents how this design has been actually instantiated into a cloud-based EaaS platform that has been used for supporting a wide variety of novel experiments targeting different research and innovation challenges. In this respect, the paper summarizes some of the experiences from these experiments and the key performance metrics that this instance of the platform has exhibited during the experimentation

Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation

The 14th ACM Recommender Systems conference (RecSys '20), Virtual Event, 22-26 September 2020Nowadays we commonly have multiple sources of data associated with items. Users may provide numerical ratings, or implicit interactions, but may also provide textual reviews. Although many algorithms have been proposed to jointly learn a model over both interactions and textual data, there is room to improve the many factorization models that are proven to work well on interactions data, but are not designed to exploit textual information. Our focus in this work is to propose a simple, yet easily applicable and effective, method to incorporate review data into such factorization models. In particular, we propose to build the user and item embeddings within the topic space of a topic model learned from the review data. This has several advantages: we observe that initializing the user and item embeddings in topic space leads to faster convergence of the factorization algorithm to a model that out-performs models initialized randomly, or with other state-of-the-art initialization strategies. Moreover, constraining user and item factors to topic space allows for the learning of an interpretable model that users can visualise.Science Foundation IrelandInsight Research Centre2020-10-06 JG: PDF replaced with correct versio

A lightweight framework for secure life-logging in smart environments

As the world becomes an interconnected network where objects and humans interact with each other, new challenges and threats appear in the ecosystem. In this interconnected world, smart objects have an important role in giving users the chance for life-logging in smart environments. However, smart devices have several limitations with regards to memory, resources and computation power, hindering the opportunity to apply well-established security algorithms and techniques for secure life-logging on the Internet of Things (IoT) domain. The need for secure and trustworthy life-logging in smart environments is vital, thus, a lightweight approach has to be considered to overcome the constraints of smart objects. The purpose of this paper is to present in details the current topics of life-logging in smart environments, while describing interconnection issues, security threats and suggesting a lightweight framework for ensuring security, privacy and trustworthy life-logging. In order to investigate the efficiency of the lightweight framework and the impact of the security attacks on energy consumption, an experimental test-bed was developed including two interconnected users and one smart attacker, who attempts to intercept transmitted messages or interfere with the communication link. Several mitigation factors, such as power control, channel assignment and AES-128 encryption were pplied for secure life-logging. Finally, research into the degradation of the consumed energy regarding the described intrusions is presented

Design and performance evaluation of a lightweight wireless early warning intrusion detection prototype

The proliferation of wireless networks has been remarkable during the last decade. The license-free nature of the ISM band along with the rapid proliferation of the Wi-Fi-enabled devices, especially the smart phones, has substantially increased the demand for broadband wireless access. However, due to their open nature, wireless networks are susceptible to a number of attacks. In this work, we present anomaly-based intrusion detection algorithms for the detection of three types of attacks: (i) attacks performed on the same channel legitimate clients use for communication, (ii) attacks on neighbouring channels, and (iii) severe attacks that completely block network's operation. Our detection algorithms are based on the cumulative sum change-point technique and they execute on a real lightweight prototype based on a limited resource mini-ITX node. The performance evaluation shows that even with limited hardware resources, the prototype can detect attacks with high detection rates and a few false alarms. © 2012 Fragkiadakis et al