We turn on the lights in our house from a desk in an office miles away. Our refrigerator alerts us to buy milk on the way home. A package of cookies on the supermarket shelf suggests that we buy it, based on past purchases. The cookies themselves are on the shelf because of a "smart" supply chain. When we get home, the thermostat has already adjusted the temperature so that it's toasty or bracing, whichever we prefer. This is the Internet of Things -- a networked world of connected devices, objects, and people. In this book, Samuel Greengard offers a guided tour through this emerging world and how it will change the way we live and work. Greengard explains that the Internet of Things (IoT) is still in its early stages. Smart phones, cloud computing, RFID (radio-frequency identification) technology, sensors, and miniaturization are converging to make possible a new generation of embedded and immersive technology. Greengard traces the origins of the IoT from the early days of personal computers and the Internet and examines how it creates the conceptual and practical framework for a connected world. He explores the industrial Internet and machine-to-machine communication, the basis for smart manufacturing and end-to-end supply chain visibility; the growing array of smart consumer devices and services -- from Fitbit fitness wristbands to mobile apps for banking; the practical and technical challenges of building the IoT; and the risks of a connected world, including a widening digital divide and threats to privacy and security. Finally, he considers the long-term impact of the IoT on society, narrating an eye-opening "Day in the Life" of IoT connections circa 2025.

The Internet of Things

Mobile crowd-sensing (MCS) is a new sensing paradigm that takes advantage of the extensive use of mobile phones that collect data efficiently and enable several significant applications. MCS paves the way to explore new monitoring applications in different fields such as social networks, lifestyle, healthcare, green applications, and intelligent transportation systems. Hence, MCS applications make use of sensing and wireless communication capabilities provided by billions of smart mobile devices, e.g., Android and iOS-based mobile devices. The aim of this paper is to identify and explore the new paradigm of MCS that is using smartphone for capturing and sharing the sensed data between many nodes. We discuss the main components of the infrastructure required to support the proposed framework. The existing and potential applications leveraging MCS are laid out. Furthermore, this paper discusses the current challenges facing the collection methodologies of the participants’ data in task management. The recent issues in the MCS findings are reviewed as well as the opportunities and challenges in sensing methods are analyzed. Finally, open research issues and future challenges facing MCS are highlighted.

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A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

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A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

Vietnam is striving to develop dynamically and overcome many human resource challenges. As the economy expands, the demand for jobs and human resource development has become increasingly critical. The pressures from reform and international integration are forcing many changes. New graduates must provide proof of their academic capabilities, while also actively developing their social capital to support their job search process. In fact, social capital is an essential capital for personal development as well as professional development of new graduates. This paper applies Fuzzy Analytical Hierarchy Process based on Geometric Mean Methodology to evaluate factors for measuring the social capital of graduates at Ho Chi Minh City Open University in Vietnam. The research results highlight the important role of social networks for graduates, in which a linking network is the most important, a bonding network is the second most important, and a bridging network is the third most important. In addition, the research shows that trust plays an even more important role than networks; and specific belief is more important than general belief.

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Application of Fuzzy Analytical Hierarchy Process based on Geometric Mean Method to Prioritize Social Capital Network Indicators

Wireless mobile network likely will be a set of self-directed systems. In which they are not only based on the unfixed infrastructure-less wireless network. This paper gives a major scope for routing ad-hoc network using fuzzy hierarchical ant colony optimization routing by maintaining the sustainability of the cluster head inside a cluster is very difficult in the adhoc network. The selected cluster head among the nodes is based on the quality of service parameter of the nodes. The weight assigned to the cluster head will determine how long the cluster head will hold. The fuzzy hierarchical ant colony optimization routing is used to select the optimized cluster head by way of in a cluster and the optimal cluster head will increase the routing. Ant colony optimization, Fuzzy Rules for Cluster Head Selection Process and Cluster’s Gateway are the three processes involved in the proposed FHACO protocol. The parameters taken for our proposed algorithm are the buffer size, energy consumption, routing overhead, packet delivery ratio and end to end delay throughput of wireless system. The system is implemented in network simulator-2 and it is assessed to gather weighted clustering technique. The experimental analysis shows that fuzzy hierarchical ant colony optimization produces a better result in maintaining the persistence of the cluster head.

Fuzzy hierarchical ant colony optimization routing for weighted cluster in MANET

Mobile environment has many issues due to mobility, energy limitations and status changing over time. Routing method is an important issue and has a significant impact in mobile networks, whereas selecting the optimum routing path will reduce the wasting in network resources, reduce network overhead and increase network reliability and lifetime. To decide which path will achieve the networks objectives, we need to construct a new routing algorithm that uses context attributes of a mobile device such as available bandwidth, residual energy, connection number and mobility value. In this paper, we propose a new mobile nodes ranking scheme based on the combination of two multi-criteria decision making approaches, the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) in Fuzzy environments. The Fuzzy AHP is used to analyze the structure of the clusterhead selection problem and to determine weights of the criteria, while the Fuzzy TOPSIS method is used to obtain the final mobile node ranking value. By basing on node ranking, we propose a new cluster based routing algorithm select the optimal clusterheads and the best routing path. Our simulation results show that the proposed method increases the network accuracy and lifetime and reduces network overhead.

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Cluster-Based Context-Aware Routing Protocol for Mobile Environments

Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establish a new and fastgrowing sensing paradigm to satisfy this need, which is called Mobile Crowd Sensing (MCS). The MCS uses different sensing abilities to acquire local knowledge through enhanced mobile devices. In MCS, it is very important to collect high-quality sensory data that satisfies the needs of all assigned tasks and the task organizers with a minimum cost for the participants. One of the most important factors which affect the MCS cost is how to schedule different sensing tasks which must be assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring high-quality sensory data. In this paper, the problem of task scheduling the which have mutual sensor is formulated and a scheduling method to minimize the energy consumption by reducing the sensor utilization is proposed. The proposed method will incentive the users to participate in multiple tasks at the same time, which minimizes the total cost of the performed tasks and increases his rewards. The experimental results by using synthetic and real data show that the proposed scheduling method can minimize the energy consumption and preserve the task requirements compared to existing algorithms.

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Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

Recently, mobile crowd sensing (MCS) is an important approach for collecting data from the participants in the urban streets. The main problem in collecting data process is how to cover all segments the street sides and select a minimal number of participants in each street segment that preserves the energy of the mobile devices and prolongs the MCS network lifetime. To solve this problem, a new flow coverage scheme is proposed to cover a specific street and achieve the coverage requirements. The proposed scheme is based on using a modified localization method that uses a minimal usage of GPS and utilizes the Zig-bee technology to communicate with the neighbor nodes and estimate the distance between nodes by using the Time of Arrival method. Experimental results by using a real data show that the proposed localization and coverage scheme can achieve high localization accuracy, reduce the usage of location sensors, and prove that the proposed street coverage scheme achieves the coverage requirements.

Energy Efficient Flow Coverage Scheme for Mobile Crowd Sensing in Urban Streets

Quality of information is an emerging issue in the mobile crowd sensing (MCS). MCS is an essential computing paradigm that tasks everyday mobile devices to form crowd sensing networks. Nowadays, there is an increasing demand to provide real-time environmental information such as air quality, noise level, traffic condition. However, the openness of crowd sensing exposes the system to malicious and erroneous participation, inevitably resulting in poor data quality. This brings forth an important issue of false data detection and correction in crowd sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. To improve the quality of the collected sensory data, the system server needs to consider the factors which are influencing the quality of the collected data. So, to acquire a high-quality sensory data, the MCS needs some efficient platforms to enhance the collected data, select the best participants from a group of users, and determine the perfect coverage type of sensing location and the exact sensing time which will achieve high-quality sensory data with low cost. In this paper, we will study the factors which are affected the MCS data quality, and propose a statistical MCS data quality model which can be used to collect the sensory data based on the data requester requirements to improve the quality of the data and selects the best users to participate in the sensing task for collecting the requested sensory data.

Statistical-Based Data Quality Model for Mobile Crowd Sensing Systems

Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establishes a new and fast-growing sensing paradigm to satisfy this need which is called mobile crowd sensing (MCS). In MCS, the optimality of sensory data quality may not be satisfied due to the existence of inexperienced users and uncoordinated task management. This paper proposes a novel participant selection schemes for enhancing the data quality in MCS. The proposed selection schemes minimise incentive payments by selecting some participants while still satisfying sensory data quality constraint. Multiple criteria factors are used to evaluate the data quality of candidate users for selecting a minimal set of users with the best data quality values by using fuzzy logic controller. These factors include a user experience and a quality of sensory units of a mobile device with each user. The experimental results by using synthetic and real data show that the proposed selection schemes can gather high-quality sensory data with low cost compared to existing schemes.

Almohammady S. Alsharkawy

Papers

Cluster-Based Context-Aware Routing Protocol for Mobile Environments

Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

Energy Efficient Flow Coverage Scheme for Mobile Crowd Sensing in Urban Streets

Statistical-Based Data Quality Model for Mobile Crowd Sensing Systems

Multiple criteria-based efficient schemes for participants selection in mobile crowd sensing