Developing IoT Applications for Future Networks

Developing IoT Applications for Future Networks

DOI: 10.4018/978-1-5225-1952-2.ch006
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Abstract

Applications in the IoT domain need to manage and integrate huge amounts of heterogeneous devices. Usually these devices are treated as external dependencies residing at the edge of the infrastructure mainly transmitting sensed data or reacting to their environment. Recently, these devices will fuel the evolution of the IoT as they feed sensor data to the Internet at a societal scale. Leveraging volunteers and their mobiles as a sensing data collection outlet is known as Mobile Crowd Sensing (MCS) and poses interesting challenges, with particular regard to the management of sensing resource contributors, dealing with their subscription, random and unpredictable join and leave, and node churn. In addition, with the advent of new wireless technologies, it is expected that the use of Machine-Type Communication (MTC) will significantly increase in next generation IoT. MTC has broad application prospects and market potential. In this chapter, we explore new IoT applications for future IoT paradigms.
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The Machine Type Communication Control (Mtcc) Scheme

For the next generation wireless network, Machine Type Communication (MTC) is gaining an enormous interest as a new communication paradigm. MTC is expected to become a cost-effective solution for improving the wireless communication performance. In MTC, one of the most critical issues is to support data transfers among devices without human interactions. The Machine Type Communication Control (MTCC) scheme is a new MTC control scheme for the future network infrastructure. To effectively support a large number of MTC devices, the MTCC scheme investigates a dual-level interaction mechanism by employing a timed strategy game model.

Development Motivation

In the past decade, mobile data traffic services have been experiencing a phenomenal rise. This ever-increasing data traffic puts significant pressure on the development of a new state-of-the-art communication method. As a new wireless communication paradigm, Machine Type Communication (MTC) is gaining a tremendous attention among mobile network operators and equipment vendors. MTC can support various automated operations without or with minimal human interactions (Niyato, 2014). In particular, it aims to provide ubiquitous connectivity to a variety of network devices in smart metering, telematics, automobile, smart cities, etc. With the different traffic services, MTC can be used in almost everywhere in our everyday life (Kouzayha, 2013; Niyato, 2014).

Due to the unique features of MTC, the MTC method poses several challenges to network operators. First, it is expected to significantly increase the total amount of traffic services. This will definitely cause intense competitions for the wireless spectrum resource. Second, quality of service (QoS) provisioning should be considered seriously. It is essential to ensure different traffic characteristics. Third, system efficiency with the limited network resource is another prominent issue for the MTC management. To satisfy these conflicting requirements, we need a new concept for effective system-wide solutions (Kouzayha, 2013).

During MTC operations, adaptive power control algorithm is a key technique to deal with above challenges. In particular, transmission power control is essential to satisfy QoS requirements while reducing interference and energy consumption (Chaves, 2013). In the wireless spectrum management, spectrum sharing and QoS ensuring are translated into requirements on the quality of received signal, such as the obtained Signal-to-Interference-plus-Noise Ratio (SINR) at the receiver. Therefore, the task of power control is to dynamically adjust the transmission power to the minimum value so that a desired SINR level can be attained at the receiver. Usually, traditional power control algorithms have assumed the perfect knowledge of link quality information. However, this information is subject to errors due to aspects like power control loop delay and measurement uncertainties. Therefore, traditional approaches are impractical methods (Chaves, 2013).

To design a novel and realistic power control algorithm for MTC systems, it is necessary to study a strategic decision making process in each MTC device. Under widely dynamic MTC environments, MTC devices can be assumed as intelligent rational decision-makers, and they select a best-response strategy to maximize their expected payoff in a distributed fashion. This situation is well-suited for the game theory.

The main goal of the MTCC scheme is to maximize system performance while ensuring QoS guarantees. In dynamically changing MTC environments, each individual MTC device can constantly adapt its power level in a non-cooperative game manner. Based on the received power levels, the system operator allocates adaptively the total spectrum resource in a cooperative manner. To effectively handle this reciprocal interaction mechanism over time, the MTCC scheme adopts a timed strategic game model. With the incomplete information, the timed strategic approach can relax the traditional assumption in game theory that all information is completely known; this is the main advantage of the MTCC scheme.

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