A first Experimental Investigation of the Practical Efficiency of Battery Scheduling

Nowadays, mobile devices are used more and more, and their battery lifetime is a key concern. In this paper, we concentrate on a method called battery scheduling with the aim to optimize the battery lifetime of mobile devices. This technique has already been largely theoretically studied in other papers. It consists, for systems containing multiple batteries, in switching the load from one battery to the other. Then, while following a given scheduling sequence, advantage can be taken from the recovery and rate capacity effects. However, little studies with experimental data of battery scheduling have been found. In this paper we describe a simple setup for measuring the possible gain of battery scheduling, and give some exploratory results for two types of real batteries: a smart Li-Ion battery used in the Thales personal communication system and a more commonly used NiCd battery. The results, so far, show that system lifetime extension is not systematic, and generally can only reach less then 10%

Getting the Most Out of Your VNFs: Flexible Assignment of Service Priorities in 5G

Through their computational and forwarding capabilities, 5G networks can support multiple vertical services. Such services may include several common virtual (network) functions (VNFs), which could be shared to increase resource efficiency. In this paper, we focus on the seldom studied VNF-sharing problem, and decide (i) whether sharing a VNF instance is possible/beneficial or not, (ii) how to scale virtual machines hosting the VNFs to share, and (iii) the priorities of the different services sharing the same VNF. These decisions are made with the aim to minimize the mobile operator's costs while meeting the verticals' performance requirements. Importantly, we show that the aforementioned priorities should not be determined a priori on a per-service basis, rather they should change across VNFs since such additional flexibility allows for more efficient solutions. We then present an effective methodology called FlexShare, enabling near-optimal VNF-sharing decisions in polynomial time. Our performance evaluation, using real-world VNF graphs, confirms the effectiveness of our approach, which consistently outperforms baseline solutions using per-service priorities

Cognitive Radio Networks: A Practical Perspective

Guest Editoria

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

BackgroundA non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.MethodsThis was a prospective case–control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.ResultsThe top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96–0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86–0.9)).ConclusionA patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted

Experimentation with MANETs of Smartphones

Mobile AdHoc NETworks (MANETs) have been identified as a key emerging technology for scenarios in which IEEE 802.11 or cellular communications are either infeasible, inefficient, or cost-ineffective. Smartphones are the most adequate network nodes in many of these scenarios, but it is not straightforward to build a network with them. We extensively survey existing possibilities to build applications on top of ad-hoc smartphone networks for experimentation purposes, and introduce a taxonomy to classify them. We present AdHocDroid, an Android package that creates an IP-level MANET of (rooted) Android smartphones, and make it publicly available to the community. AdHocDroid supports standard TCP/IP applications, providing real smartphone IEEE 802.11 MANET and the capability to easily change the routing protocol. We tested our framework on several smartphones and a laptop. We validate the MANET running off-the-shelf applications, and reporting on experimental performance evaluation, including network metrics and battery discharge rate.Comment: 6 pages, 7 figures, 1 tabl

Mining Event - Based Commonsense Knowledge from Web using NLP Techniques

The real life intelligent applications such as agents, expert systems, dialog understanding systems, weather forecasting systems, robotics etc. mainly focus on commonsense knowledge And basically these works on the knowledgebase which contains large amount of commonsense knowledge. The main intention of this work is to create a commonsense knowledge base by using an effective methodology to retrieve commonsense knowledge from large amount of web data. In order to achieve the best results, it makes use of different natural language processing techniques such as semantic role labeling, lexical and syntactic analysi. Keywords: Automatic statistical semantic role tagger (ASSERT), lexico - syntactic pattern matching, semantic role labeling (SRL

An Optimized Clogging Manage and Fault Executive System

In this paper, a new OCMFES congestion control mechanism is introduced in multi-homing mode. Congestion in each route can be avoided or can be controlled, based on an Active Queue Management (AQM) method. Also, routers compute probability of congestion for the sources on the paths and then notify them. Therefore, the sources can adjust their sending rates on each path effectively and if necessary, can switch to an alternate path to prevent congestion. Simulations have been conducted with Opnet linked with NS2. The simulation shows that the new method can decrease packet loss, increase the amount of transmissions and stabilize queue length, as compared with standard OCMFES. Keywords:  OCMFES; AQM; congestion control; sendin

Towards Energy Efficient Relay Placement and Load Balancing in Future Wireless Networks

This paper presents an energy efficient relay deployment algorithm that determines the optimal location and number of relays for future wireless networks, including Long Term Evolution (LTE)-Advanced heterogeneous networks. We formulate an energy minimization problem for macro-relay heterogeneous networks as a Mixed Integer Linear Programming (MILP) problem. The proposed algorithm not only optimally connects users to either relays or eNodeBs (eNBs), but also allows eNBs to switch into inactive mode. This is possible by enabling relay-to-relay communication which forms the basis for relays to act as donors for neighboring relays instead of eNBs. Moreover, it relaxes traffic load of some eNBs in order to allow them to enter the inactive mode. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. Our performance evaluation shows that an optimal relay deployment with relays acting as donors can significantly improve system energy efficiency

MEBCK from Web using NLP Techniques

The real life intelligent applications such as agents, expert systems, dialog understanding systems, weather forecasting systems, robotics etc. mainly focus on commonsense knowledge And basically these works on the knowledgebase which contains large amount of commonsense knowledge. The main intention of this work is to create a commonsense knowledge base by using an effective methodology to retrieve commonsense knowledge from large amount of web data. In order to achieve the best results, it makes use of different natural language processing techniques such as semantic role labeling, lexical and syntactic analysis. Keywords: Automatic statistical semantic role tagger (ASSERT), lexico - syntactic pattern matching, semantic role labeling (SRL