Hadoop-Based Intelligent Care System (HICS) : Analytical Approach for Big Data in IoT

The Internet of Things (IoT) is increasingly becoming a worldwide network of interconnected things that are uniquely addressable, via standard communication protocols. The use of IoT for continuous monitoring of public health is being rapidly adopted by various countries while generating a massive volume of heterogeneous, multisource, dynamic, and sparse high-velocity data. Handling such an enormous amount of high-speed medical data while integrating, collecting, processing, analyzing, and extracting knowledge constitutes a challenging task. On the other hand, most of the existing IoT devices do not cooperate with one another by using the same medium of communication. For this reason, it is a challenging task to develop healthcare applications for IoT that fulfill all user needs through real-Time monitoring of health parameters. Therefore, to address such issues, this article proposed a Hadoop-based intelligent care system (HICS) that demonstrates IoT-based collaborative contextual Big Data sharing among all of the devices in a healthcare system. In particular, the proposed system involves a network architecture with enhanced processing features for data collection generated by millions of connected devices. In the proposed system, various sensors, such as wearable devices, are attached to the human body and measure health parameters and transmit them to a primary mobile device (PMD). The collected data are then forwarded to intelligent building (IB) using the Internet where the data are thoroughly analyzed to identify abnormal and serious health conditions. Intelligent building consists of (1) a Big Data collection unit (used for data collection, filtration, and load balancing); (2) a Hadoop processing unit (HPU) (composed of Hadoop distributed file system (HDFS) and MapReduce); and (3) an analysis and decision unit. The HPU, analysis, and decision unit are equipped with a medical expert system, which reads the sensor data and performs actions in the case of an emergency situation. To demonstrate the feasibility and efficiency of the proposed system, we use publicly available medical sensory datasets and real-Time sensor traffic while identifying the serious health conditions of patients by using thresholds, statistical methods, and machine-learning techniques. The results show that the proposed system is very efficient and able to process high-speed WBAN sensory data in real time

The roles and values of wild foods in agricultural systems

Almost every ecosystem has been amended so that plants and animals can be used as food, fibre, fodder, medicines, traps and weapons. Historically, wild plants and animals were sole dietary components for hunter–gatherer and forager cultures. Today, they remain key to many agricultural communities. The mean use of wild foods by agricultural and forager communities in 22 countries of Asia and Africa (36 studies) is 90–100 species per location. Aggregate country estimates can reach 300–800 species (e.g. India, Ethiopia, Kenya). The mean use of wild species is 120 per community for indigenous communities in both industrialized and developing countries. Many of these wild foods are actively managed, suggesting there is a false dichotomy around ideas of the agricultural and the wild: hunter–gatherers and foragers farm and manage their environments, and cultivators use many wild plants and animals. Yet, provision of and access to these sources of food may be declining as natural habitats come under increasing pressure from development, conservation-exclusions and agricultural expansion. Despite their value, wild foods are excluded from official statistics on economic values of natural resources. It is clear that wild plants and animals continue to form a significant proportion of the global food basket, and while a variety of social and ecological drivers are acting to reduce wild food use, their importance may be set to grow as pressures on agricultural productivity increase.</jats:p

Threshold-based generic scheme for encrypted and tunneled Voice Flows Detection over IP Networks

VoIP usage is rapidly growing due to its cost effectiveness, dramatic functionality over the traditional telephone network and its compatibility with public switched telephone network (PSTN). In some countries, like Pakistan, the commercial usage of VoIP is prohibited. Internet service providers (ISPs) and telecommunication authorities are interested in detecting VoIP calls to either block or prioritize them. So detection of VoIP calls is important for both types of authorities. Signature-based, port-based, and pattern-based VoIP detection techniques are inefficient due to complex and confidential security and tunneling mechanisms used by VoIP. In this paper, we propose a generic, robust, efficient, and practically implementable statistical analysis-based solution to identify encrypted, non-encrypted, or tunneled VoIP media (voice) flows using threshold values of flow statistical parameters. We have made a comparison with existing techniques and evaluated our system with respect to accuracy and efficiency. Our system has 97.54% direct rate and .00015% false positive rate

Privacy-Preserving Multipoint Traffic Flow Estimation for Road Networks

Intelligent transportation systems necessitate a fine-grained and accurate estimation of vehicular traffic flows across critical paths of the underlying road network. However, such statistics should be collected in a manner that does not disclose the trajectories of individual users. To this end, we introduce a privacy-preserving protocol that leverages roadside units (RSUs) to communicate with the passing vehicles, in order to construct encrypted Bloom filters stemming from random vehicle IDs that are chosen secretly by the individual vehicles. Each Bloom filter represents the set of vehicle IDs that contacted the RSU but may also be used to estimate the traffic flow between any number of RSUs. More precisely, we designed a probabilistic model that approximates multipoint traffic flows by estimating the number of common vehicles among a given set of RSUs. Through extensive simulation experiments, we demonstrate that our protocol is very accurate—with a minor deviation from the real traffic flow—and show that it reduces the estimation error by a large factor, when compared to the current state-of-the-art approaches. Furthermore, our implementation of the underlying cryptographic primitives illustrates the feasibility, practicality, and scalability of the system

Handling Big Microarray Data: A Novel Approach to Design Accurate Fuzzy-Based Medical Expert System

The genes data produced by microarray experiments is complex in terms of dimensions and samples. It consumes a lot of computation power and time when it is processed for a disease analysis while working with an expert system. At the same time, data can help doctors identify a patient&#x2019;s health condition if it is presented in a meaningful way and processed on time. Several methods have been proposed to reduce the dimensions of medical microarray data and optimize its search space with minimal accuracy loss. However, the discretization of continuous gene-values in the process of dimension reduction is failed to preserve the inherent meaning of genes. Also, ensuring high accuracy and interpretability in the reduction process may result in extra processing time, which is unfavorable for time-critical applications. To overcome these issues, in this paper, we propose a dimension reduction method in conjunction with a fuzzy expert system (FES) optimization approach, while keeping an accuracy-interpretability-speedy tradeoff in mind. To accomplish this, we use a fuzzy rough set on ${f}$ -information to identify meaningful genes without changing their original values. We propose a conditionally guided particle swarm optimization for faster knowledge acquisition, where the velocity is adjusted based on a predefined update probability, resulting in a faster search. A big data processing architecture is designed using the Hadoop ecosystem along with a $MapReduce$ -equivalent algorithm of the proposed method for speedy processing, enabling parallel processing on microarray data to reduce dimensions and perform classification through knowledge extraction. The proposed method is thoroughly tested on eleven microarray datasets by considering accuracy-interpretability-speed tradeoff. The results show that the proposed method is effective in identifying disease-causing genes while also understanding the patient&#x2019;s genetic profile with only a few operations and a small amount of CPU time. Statistical tests are also run to validate the proposed method&#x2019;s efficacy in comparison to other methods

Data Transmission Scheme Using Mobile Sink in Static Wireless Sensor Network

Multihop communication in wireless sensor network (WSN) brings new challenges in reliable data transmission. Recent work shows that data collection from sensor nodes using mobile sink minimizes multihop data transmission and improves energy efficiency. However, due to continuous movements, mobile sink has limited communication time to collect data from sensor nodes, which results in rapid depletion of node’s energy. Therefore, we propose a data transmission scheme that addresses the aforementioned constraints. The proposed scheme first finds out the group based region on the basis of localization information of the sensor nodes and predefined trajectory information of a mobile sink. After determining the group region in the network, selection of master nodes is made. The master nodes directly transmit their data to the mobile sink upon its arrival at their group region through restricted flooding scheme. In addition, the agent node concept is introduced for swapping of the role of the master nodes in each group region. The master node when consuming energy up to a certain threshold, neighboring node with second highest residual energy is selected as an agent node. The mathematical analysis shows that the selection of agent node maximizes the throughput while minimizing transmission delay in the network

Context-Aware Mobile Sensors for Sensing Discrete Events in Smart Environment

Over the last few decades, several advancements in the field of smart environment gained importance, so the experts can analyze ideas for smart building based on embedded systems to minimize the expense and energy conservation. Therefore, propelling the concept of smart home toward smart building, several challenges of power, communication, and sensors’ connectivity can be seen. Such challenges distort the interconnectivity between different technologies, such as Bluetooth and ZigBee, making it possible to provide the continuous connectivity among different objects such as sensors, actuators, home appliances, and cell phones. Therefore, this paper presents the concept of smart building based on embedded systems that enhance low power mobile sensors for sensing discrete events in embedded systems. The proposed scheme comprises system architecture that welcomes all the mobile sensors to communicate with each other using a single platform service. The proposed system enhances the concept of smart building in three stages (i.e., visualization, data analysis, and application). For low power mobile sensors, we propose a communication model, which provides a common medium for communication. Finally, the results show that the proposed system architecture efficiently processes, analyzes, and integrates different datasets efficiently and triggers actions to provide safety measurements for the elderly, patients, and others

A multi-parameter based vertical handover decision scheme for M2M communications in HetMANET

The Machine-to-Machine (M2M) communication has the potential to connect millions of devices in the near future. Since they agree on this potential, several standard organizations need to focus on improved general architecture for M2M communications. Currently, there is a lack of consensus to improve the general feasibility of M2M communication. Heterogeneous Mobile Ad hoc Networks (HetMANETs) can normally be considered appropriate for M2M challenges. When a mobile node (MN) moves inside a HetMANET, various challenges including a selection of the target network and energy efficient scanning take place, which need to be addressed for efficient handover. To cope with these issues, we propose a handover management scheme that efficiently initiates a handover process and selects an optimal network. Our proposed scheme is composed of two phases, i.e., i) the MN performs handover triggering based on the optimization of the Receive Signal Strength (RSS) from an access point/base station (AP/BS), and, ii) the network selection process is carried out by considering different parameters such as delay, jitter, velocity, network load, and energy consumption by the network interface. Moreover, if there are more networks available, then the MN selects the one that can provide the highest quality-of- service (QoS) using the Elimination and Choice Expressing Reality (ELECTRE) decision model. The performance of the proposed scheme is compared in the context of the number of handovers, average stay-time of an MN in the network, and energy consumption against periodic and adaptive scanning. Similarly, a two- state Markov model is defined that efficiently distribute the number nodes on the available access points and base stations. The proposed scheme efficiently optimizes the handoff related parameters and outperforms existing schemes. 2015 IEEE.Scopus2-s2.0-8496490634