M. Mazhar Rathore

Urban planning and building smart cities based on the Internet of Things using Big Data analytics

The rapid growth in the population density in urban cities demands that services and an infrastructure be provided to meet the needs of city inhabitants. Thus, there has been an increase in the request for embedded devices, such as sensors, actuators, and smartphones, leading to considerable business potential for the new era of the Internet of Things (IoT), in which all devices are capable of interconnecting and communicating with each other over the Internet. Thus, Internet technologies provide a way of integrating and sharing a common communication medium. With this knowledge, in this paper, we propose a combined IoT-based system for smart city development and urban planning using Big Data analytics. We propose a complete system consisting of various types of sensor deployment, including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, and surveillance objects. A four-tier architecture is proposed that includes 1) Bottom tier-1, which is responsible for IoT sources and data generation and collection, 2) Intermediate tier-1, which is responsible for all types of communication between, for instance, sensors, relays, base stations, and the Internet, 3) Intermediate tier 2, which is responsible for data management and processing using a Hadoop framework, and 4) Top tier, which is responsible for application and usage of the data analysis and the results generated. The system implementation consists of various steps that begin with data generation and move to collection, aggregation, filtration, classification, preprocessing, computing and decision making. The proposed system is implemented using Hadoop with Spark, voltDB, Storm or S4 for real time processing of the IoT data to generate results to establish the smart city. For urban planning or city future development, the offline historical data are analyzed with Hadoop using MapReduce programming. IoT datasets generated by smart homes, smart parking weather, pollution, and vehicle data sets are used for analysis and evaluation. This type of system with full functionality does not currently exist. Similarly, the results demonstrate that the proposed system is more scalable and efficient than existing systems. Moreover, system efficiency is measured in terms of throughput and processing time.

Smart cyber society

The recent development in the field of embedded devices, such as sensors, actuators, and smartphones, etc. is providing a great business potential towards the new era of web of things (WoT); in which all the capillary devices (electronic devices) are capable of interconnecting and communicating with each other over the Internet. Therefore, web technologies provide a way towards integrating and sharing a common communication medium. However, for integrating Cyber-Physical System (CPS) and WoT, a comprehensive architecture and platform is still missing. Therefore, this paper proposes the concept of Smart Cyber Society; propelling the concept of smart home. We then propose the virtual communication platform that is composed of six functional communication layers, which provides a common medium for communication, i.e., same communication language. In addition, a system architecture for smart cyber society is also proposed, which consists of three networked domains, such as cyber home domain (networked-home), cyber society domain (networked of various societies, i.e., hospitals, police station, and fire brigade), and cyber mobile domain (networked of vehicles). Furthermore, the feasibility and efficiency of the proposed system are implemented on Hadoop single node setup on UBUNTU 14.04 LTS coreTMi5 machine with 3.2 GHz processor and 4 GB memory. Sample medical, sensory datasets and fire detection datasets are tested on the proposed system. 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 elderly age people, vehicles and others. Smart cyber-physical society integration with web of things (WoT).Cyber home domain, real-time monitoring of residents in home environment.Cyber society and mobile domain communication through 3G/LTE/Wi-Fi/Sensor/GPS.

An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication

Machine-to-Machine (M2M) communication relies on the physical objects (e.g., satellites, sensors, and so forth) interconnected with each other, creating mesh of machines producing massive volume of data about large geographical area (e.g., living and non-living environment). Thus, the M2M is an ideal example of Big Data. On the contrary, the M2M platforms that handle Big Data might perform poorly or not according to the goals of their operator (in term of cost, database utilization, data quality, processing and computational efficiency, analysis and feature extraction applications). Therefore, to address the aforementioned needs, we propose a new effective, memory and processing efficient system architecture for Big Data in M2M, which, unlike other previous proposals, does not require whole set of data to be processed (including raw data sets), and to be kept in the main memory. Our designed system architecture exploits divide-and-conquer approach and data block-wise vertical representation of the database follows a particular petitionary strategy, which formalizes the problem of feature extraction applications. The architecture goes from physical objects to the processing servers, where Big Data set is first transformed into a several data blocks that can be quickly processed, then it classifies and reorganizes these data blocks from the same source. In addition, the data blocks are aggregated in a sequential manner based on a machine ID, and equally partitions the data using fusion algorithm. Finally, the results are stored in a server that helps the users in making decision. The feasibility and efficiency of the proposed system architecture are implemented on Hadoop single node setup on UBUNTU 14.04 LTS core?i5 machine with 3.2GHz processor and 4GB memory. The results show that the proposed system architecture efficiently extract various features (such as River) from the massive volume of satellite data.

Efficient Graph-Oriented Smart Transportation Using Internet of Things Generated Big Data

The rapid growth in the population density in urban cities and the advancement in technology demands real-time provision of services and infrastructure. Citizens, especially travelers, want to be reached within time to the destination. Consequently, they require to be facilitated with smart and real-time traffic information depending on the current traffic scenario. Therefore, in this paper, we proposed a graph-oriented mechanism to achieve the smart transportation system in the city. We proposed to deploy road sensors to get the overall traffic information as well as the vehicular network to obtain location and speed information of the individual vehicle. These Internet of Things (IoT) based networks generate enormous volume of data, termed as Big Data, depicting the traffic information of the city. To process incoming Big Data from IoT devices, then generating big graphs from the data, and processing them, we proposed an efficient architecture that uses the Giraph tool with parallel processing servers to achieve real-time efficiency. Later, various graph algorithms are used to achieve smart transportation by making real-time intelligent decisions to facilitate the citizens as well as the metropolitan authorities. Vehicular Datasets from various reliable resources representing the real city traffic are used for analysis and evaluation purpose. The system is implemented using Giraph and Spark tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.

Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health

Healthy people are important for any nation’s development. Use of the Internet of Things (IoT)-based body area networks (BANs) is increasing for continuous monitoring and medical healthcare in order to perform real-time actions in case of emergencies. However, in the case of monitoring the health of all citizens or people in a country, the millions of sensors attached to human bodies generate massive volume of heterogeneous data, called “Big Data.” Processing Big Data and performing real-time actions in critical situations is a challenging task. Therefore, in order to address such issues, we propose a Real-time Medical Emergency Response System that involves IoT-based medical sensors deployed on the human body. Moreover, the proposed system consists of the data analysis building, called “Intelligent Building,” depicted by the proposed layered architecture and implementation model, and it is responsible for analysis and decision-making. The data collected from millions of body-attached sensors is forwarded to Intelligent Building for processing and for performing necessary actions using various units such as collection, Hadoop Processing (HPU), and analysis and decision. The feasibility and efficiency of the proposed system are evaluated by implementing the system on Hadoop using an UBUNTU 14.04 LTS coreTMi5 machine. Various medical sensory datasets and real-time network traffic are considered for evaluating the efficiency of the system. The results show that the proposed system has the capability of efficiently processing WBAN sensory data from millions of users in order to perform real-time responses in case of emergencies.

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.

An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication

Machine-to-Machine communication (M2M) is nowadays increasingly becoming a world-wide network of interconnected devices uniquely addressable, via standard communication protocols. The prevalence of M2M is bound to generate a massive volume of heterogeneous, multisource, dynamic, and sparse data, which leads a system towards major computational challenges, such as, analysis, aggregation, and storage. Moreover, a critical problem arises to extract the useful information in an efficient manner from the massive volume of data. Hence, to govern an adequate quality of the analysis, diverse and capacious data needs to be aggregated and fused. Therefore, it is imperative to enhance the computational efficiency for fusing and analyzing the massive volume of data. Therefore, to address these issues, this article proposes an efficient, multidimensional, big data analytical architecture based on the fusion model. The basic concept implicates the division of magnitudes (attributes), i.e., big datasets with complex magnitudes can be altered into smaller data subsets using five levels of the fusion model that can be easily processed by the Hadoop Processing Server, resulting in formalizing the problem of feature extraction applications using earth observatory system, social networking, or networking applications. Moreover, a four-layered network architecture is also proposed that fulfills the basic requirements of the analytical architecture. The feasibility and efficiency of the proposed algorithms used in the fusion model are implemented on Hadoop single-node setup on UBUNTU 14.04 LTS core i5 machine with 3.2GHz processor and 4GB memory. The results show that the proposed system architecture efficiently extracts various features (such as land and sea) from the massive volume of satellite data.

IoT-Based Big Data: From Smart City towards Next Generation Super City Planning

Recently, a rapid growth in the population in urban regions demands the provision of services and infrastructure. These needs can be come up wit the use of Internet of Things IoT devices, such as sensors, actuators, smartphones and smart systems. This leans to building Smart City towards the next generation Super City planning. However, as thousands of IoT devices are interconnecting and communicating with each other over the Internet to establish smart systems, a huge amount of data, termed as Big Data, is being generated. It is a challenging task to integrate IoT services and to process Big Data in an efficient way when aimed at decision making for future Super City. Therefore, to meet such requirements, this paper presents an IoT-based system for next generation Super City planning using Big Data Analytics. Authors have proposed a complete system that includes various types of IoT-based smart systems like smart home, vehicular networking, weather and water system, smart parking, and surveillance objects, etc., for dada generation. An architecture is proposed that includes four tiers/layers i.e., 1 Bottom Tier-1, 2 Intermediate Tier-1, 3 Intermediate Tier 2, and 4 Top Tier that handle data generation and collections, communication, data administration and processing, and data interpretation, respectively. The system implementation model is presented from the generation and collection of data to the decision making. The proposed system is implemented using Hadoop ecosystem with MapReduce programming. The throughput and processing time results show that the proposed Super City planning system is more efficient and scalable.

IoT-based smart city development using big data analytical approach

To meet the needs of urban public and the city development smartly, the use of IoT devices, such as sensors, actuators, and smartphones, etc., and the smart system is the very fast and valuable source. However, interconnecting thousands of IoT devices while communicating with each other over the Internet to establish a smart system, results in the generation of huge amount of data, termed as Big Data. To integrate IoT services in order to get real-time city data and then processing such big amount of data in an efficient way aimed at establishing smart city is a challenging task. Therefore, in this paper, we proposed and developed a smart city system based on IoT using Big Data Analytics. We use sensors deployment including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, surveillance objects, etc. The complete architecture and implementation model is proposed, which is implemented using Hadoop ecosystem in a real environment. The system implementation consists of various steps that start from data generation and collecting, aggregating, filtration, classification, preprocessing, computing and finished at decision making. The efficiency in Big Data processing is achieved using spark over Hadoop. The system is practically implemented by taken smart systems as city data source to develop smart city. The evaluation results show that the proposed system is scalable and efficient.

Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where High-Performance Computing solution has become a key issue and has attracted attention in recent years. However, these systems are either memoryless or computational inefficient. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that enhances the working of traditional MapReduce by incorporating parallel processing algorithm. Moreover, complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed parallel processing algorithm. The proposed system architecture both read and writes operations that enhance the efficiency of the Input/Output operation. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.