Location Aware Data Aggregation for Efficient Message Dissemination in Vehicular Ad Hoc Networks

An intelligent location-aware data aggregation mechanism for real-time observation, estimation, and efficient dissemination of any kind of traffic information in vehicular ad hoc networks (VANETs) is presented in this paper. The mechanism introduces a location awareness algorithm, enabling spatiotemporal database indexing and providing the location context of the messages without the use of advanced positioning systems such as satellite navigation and digital maps. Intelligent passive clustering and adaptive broadcasting are used to minimize the number of messages exchanged, packet collisions, and network load. The incoming messages are fused by a Kalman filter, allowing the description of the traffic-related information as a system characterized by as many variables as needed, depending on the application design. The scheme allows the comparison of aggregates and single observations, which enables their merging and better overall accuracy. Old information in aggregates is removed by real-time database refreshing, thus leaving only newer relevant information for the driver to make real-time decisions in traffic. The mechanism is generic and can be used for any kind of VANET information. It is evaluated by extensive simulations to show the efficiency and accuracy

Distributed Road Traffic Congestion Quantification Using Cooperative VANETs

The well-known traffic congestion problem in urban environments has negative impact on many areas including economy, environment, health and lifestyle. Recently, a number of solutions based on vehicle-to-vehicle communications were proposed for traffic congestion detection and management. In this paper we present an algorithm designed to enable each vehicle in the network to detect and quantify the level of traffic congestion in completely distributed way, independent of any supporting infrastructure and additional information such as traffic data from local authorities. Based on observations of traffic congestion by every vehicle, and by adapting the broadcast interval, it enables dissemination of the traffic information to other vehicles. The algorithm also makes every vehicle aware about the congestion level on the streets that are spatially separated from their current location by several streets. Its robustness keeps the vehicle's overall knowledge about congestion consistent, despite the short-term changes in vehicle's motion. Since the quantification of congestion is based on per-vehicle basis, the algorithm is able to operate even when only 10% of vehicles in the network are VANET enabled. Data aggregation and adaptive broadcasting are used to ensure that vehicles do not send redundant information about the traffic congestion. The simulations are conducted in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator

Hybrid Imaging in Head and Neck Sarcoidosis

To determine the prevalence of head and neck sarcoidosis (HNS) and evaluate the role of hybrid molecular imaging in HNS. Between 2010 and 2018, 222 patients with chronic sarcoidosis and presence of prolonged symptoms of active disease were referred to FDG PET/CT. Active disease was found in 169 patients, and they were all screened for the presence of HNS. All patients underwent MDCT and assessment of the serum ACE level. Follow-up FDG PET/CT examination was done 19.84 ± 8.98 months after the baseline. HNS was present in 38 out of 169 patients. FDG uptake was present in: cervical lymph nodes (38/38), submandibular glands (2/38), cerebrum (2/38), and bone (1/38). The majority of patients had more than two locations of disease. After FDG PET/CT examination, therapy was changed in most patients. Fourteen patients returned to follow-up FDG PET/CT examination in order to assess the therapy response. PET/CT revealed active disease in 12 patients and complete remission in two patients. Follow-up ACE levels had no correlation with follow-up SUVmax level (ρ = −0.18, p = 0.77). FDG PET/CT can be useful in the detection of HNS and in the evaluation of the therapy response. It may replace the use of non-purposive mounds of insufficiently informative laboratory and radiological procedures

Cultivation With Powdered Meteorite (NWA 1172) as the Substrate Enhances Low-Temperature Preservation of the Extreme Thermoacidophile Metallosphaera sedula

Recent studies have uncovered a vast number of thermophilic species in icy environments, permanently cold ocean sediments, cold sea waters, and cool soils. The survival of thermophiles in psychrobiotic habitats requires thorough investigation of the physiological and molecular mechanisms behind their natural cryopreservation. Such investigations are mainly impeded due to a restricted cultivation of thermophiles at low temperatures under the laboratory conditions. Artificial culture media used under the laboratory conditions usually fail to support cultivation of thermophiles at low-temperature range. In this study we cultivated the extreme thermoacidophilic archaeonMetallosphaera sedulawith the preliminary powdered and sterilized multimetallic extraterrestrial mineral material (the meteorite NWA 1172) under a low temperature regime in laboratory conditions. Our data indicate thatM. sedulawithstands cold stress and can be maintained at low temperatures, when supplemented with the meteorite NWA 1172 as the sole energy source. Cultivation with the meteorite NWA 1172 opens up new, previously unknown psychrotolerant characteristics ofM. sedula, emphasizing that culture conditions (i.e., the "nutritional environment") may affect the microbial survival potential in stress related situations. These observations facilitate further investigation of strategies and underlying molecular mechanisms of the survival of thermophilic species in permanently cold habitats

Milan Milojevic (Art Forum)

Milan Milojevic is head of printmaking at the Tasmanian School of Art. He discusses work made during a recent residency at COFA at UNSW, where he researched 18th and 19th century prints depicting the natural world

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

Decision Making in Patients Undergoing Coronary Artery Revascularization : A New Era in Clinical Trial Design and Evidence-Based Practice

The clinical goals for the care of each patient with complex coronary artery disease are to ensure that the most appropriate treatment is selected, and the best available evidence is used to guide procedural techniques and modify risk factors that could impact life expectancy and health-related quality of life. In this thesis, ef

Common fixed points for self-mappings on partial metric spaces

In this paper, we prove some results of a common fixed point for two self-mappings on partial metric spaces. Our results generalize some interesting results of Ilic et al. (Appl. Math. Lett. 24:1326-1330, 2011). We conclude with a result of the existence of a fixed point for set-valued mappings in the context of 0-complete partial metric spaces

Energy-based Adaptive Compression in Water Network Control Systems

© 2016 IEEE.Contemporary water distribution networks exploit Internet of Things (IoT) technologies to monitor and control the behavior of water network assets. Smart meters/sensor and actuator nodes have been used to transfer information from the water network to data centers for further analysis. Due to the underground position of water assets, many water companies tend to deploy battery driven nodes which last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from the recorder data. To alleviate this problem, efficient data compression enables high-rate sampling, whilst reducing significantly the required storage and bandwidth resources without sacrificing the meaningful information content. This paper introduces a novel algorithm which combines the accuracy of standard lossless compression with the efficiency of a compressive sensing framework. Our method balances the tradeoffs of each technique and optimally selects the best compression mode by minimizing reconstruction errors, given the sensor node battery state. To evaluate our algorithm, real high-sample rate water pressure data of over 170 days and 25 sensor nodes of our real world large scale testbed was used. The experimental results reveal that our algorithm can reduce communication around 66% and extend battery life by 46% compared to traditional periodic communication techniques