A fast, adaptive, and energy-efficient multi-path-multi-channel data collection protocol for wireless sensor networks

Energy consumption, traffic adaptability, fast data collection, etc are the major issues in wireless sensor networks (WSNs). Most existing WSN protocols are able to handle one or two of the above issues with the other(s) being compromised. In order to reduce the energy consumption of wireless sensor nodes while having fast data collection under different traffic generating rates, this paper proposes a fast, adaptive, and energy-efficient multi-path-multi-channel (FAEM) data collection protocol. FAEM makes use of the Basketball Net Topology proposed in the literature, in which a multi-parent-multi-child connection table is pre-established at each node; each node is also pre-assigned a receiving channel which is different from those of the neighboring nodes so as to eliminate the transmission interference. During data transmission, time is divided into duty cycles, and each consists of two phases, namely distributed iterative scheduling phase and slot-based packet forwarding phase. The former is to match parents and children of the entire WSN in a distributed manner in order to determine whether a node should be in upload (to which parent), download (from which child), or sleep mode in a particular slot; while the latter is for nodes to take action according to the schedule. Simulation shows that our protocol is able to achieve lower energy consumption, data reliability and low latency even during a high traffic load

A coherent authentication framework for mobile computing based on homomorphic signature and implicit authentication

Mobile cloud computing is an extension of cloud computing that allow the users to access the cloud service via their mobile devices. Although mobile cloud computing is convenient and easy to use, the security challenges are increasing significantly.One of the major issues is unauthorized access.Identity Management enables to tackle this issue by protecting the identity of users and controlling access to resources. Although there are several IDM frameworks in place, they are vulnerable to attacks like timing attacks in OAuth, malicious code attack in OpenID and huge amount of information leakage when user’s identity is compromised in Single Sign-On. Our proposed framework implicitly authenticates a user based on user’s typing behavior.The authentication information is encrypted into homomorphic signature before being sent to IDM server and tokens are used to authorize users to access the cloud resources.Advantages of our proposed framework are: user’s identity protection and prevention from unauthorized access

Deep learning for diabetic retinopathy analysis : a review, research challenges, and future directions

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR

A Store-and-delivery Based MAC Protocol for Air-ground Collaborative Wireless Networks for Precision Agriculture

Due to rapid population growth, the demand for food is also elevating, which inspires farmers to embrace precision agriculture to increase production by exploiting predictive analytics on relevant real-time data. The exactitude of a prediction is vital to decide the next course of actions to be taken to compensate current demands, which again relies on a competent data acquisition technique. The Media Access Control (MAC) protocols have significant contribution in designing data acquisition technique. In this paper, we propose a new Storeand-Delivery base MAC (SD-MAC) protocol for Air-Ground Collaborative Wireless Networks (AGCWNs) to acquire data efficiently from the sensing devices which are deployed in the agricultural field. Our proposed protocol takes into consideration of the factors of network architecture and transforms them into advantages to attain higher throughput. The performance of the proposed protocol is evaluated using simulations and involving another such protocol, where the proposed protocol outperforms the other protocol

IMPROVING TELEMARKETING INTELLIGENCE THROUGH SIGNIFICANT PROPORTION OF TARGET INSTANCES

In this paper we propose, develop, and test a new single-feature evaluator called Significant Proportion of Target Instances (SPTI) to handle the direct-marketing data with the class imbalance problem. The SPTI feature evaluator demonstrates its stability and outstanding performance through empirical experiments in which the real-world customer data of an e-recruitment firm are used. This research demonstrates that the feature selection using SPTI successfully improves the classifier’s performance in terms of two practical performance metrics. Additionally, we show that it outperforms other well-known feature selection methods and state-of-the-art remedies to the class-imbalance problem. Practically, the findings, when used with the classification model, will help telemarketers to better understand their customers