Fatima Hussain

Medium access control techniques in M2M communication: survey and critical review

Machine-to-machine (M2M) communication has received increasing attention in recent year. An M2M network exhibits some salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small packets, energy constrained and low or no mobility. A large number of M2M terminals may exist in a small area with many trying to simultaneously and randomly access for channel resources, which will result in overload and access problem. This increased signalling overhead and diverse requirements of machine-type communication (MTC) devices call for the development of flexible and efficient scheduling and random access techniques. In an M2M scenario, where the network is operating at high offered load with a large number of contending transmitters, distributed random access techniques are more appropriate than centralised scheduling techniques because of less control messages and better channel utilisation. There is a need for comparison of various medium access methods that can be used in the development of an efficient hybrid M2M and human to human network. In this article, we review and compare various scheduling and random access techniques in cellular networks, particularly in Long-Term Evolution. We also discuss how successful they are to fulfill the unique requirements of M2M communication and networking. Resource management in M2M networks with a large number of MTC devices is also discussed from the access point of view. Energy efficiency, being one of the main challenges of quality-of-service-constrained M2M communication, is also discussed. Minimisation of the energy consumption is tightly bound to channel access and hence considered in the comparison of various medium access control protocols. Finally, some potential research directions related to access control and resource allocation are presented for future work. Copyright

Multi-objective MTC device controller resource optimization in M2M communication

Machine to machine (M2M) communication has received increasing attention in recent years. It exhibits features such as large number of devices and low data rates. In order to accommodate massive, energy efficient M2M traffic and to reduce the access delay and signalling overhead, the recommendation is to introduce a clustered network structure. In this effort, we consider machines (or devices) in a macro cell divided into clusters. The machines belonging to a cluster communicate to the cluster head/controller, which then aggregates the traffic and relays to the eNB. Unlike related work that focuses on formation of clusters and their energy consumption, in this paper we investigate the multi-objective optimization problem of throughput maximization and power control of cluster heads in interference-limited M2M communication. Our objective is to maximize the number of admitted MTC device controllers with least interference caused to conventional (human) devices such that their quality of service (QoS) is not affected by the M2M communications. To maximize the number of machines that can communicate while meeting the interference constraints of human devices and machines themselves, we formulate a mixed-integer non-linear programming (MINLP) problem. OPTI toolbox is used to optimize the power, capacity and maximum number of admitted machines collectively.

Internet of Everything

Internet of Things (IoT) can be defined as “interconnection of things”, that is used to sense, process and report real world information. The applications and usage of the internet are expanding on a daily basis and IoT is the new approach for incorporating the internet into personal, professional and social life. IoT can be seen as a connected set of anyone, anything, anytime, anyplace, any service, and any network in a more general sense. It is envisioned as billion of sensors, wired or wireless, connected to the internet that will generate large amount of data which need to be analyzed, interpreted and utilized. This data can leads to creation of new opportunities for growth and innovation. Major building blocks for IoT is comprised of sensor devices, interconnecting technologies, big data collection and management, and end applications. This book discusses the perspectives and challenges behind each block systematically. It also address how these “smart things” can help in the design of a smarter and more promising future of interconnected world.

Resource allocation and congestion control in clustered M2M communication using Q-learning

In this paper, we apply a Q-learning algorithm to carry out slot assignment for machine type communication devices (MTCDs) in machine-to-machine communication. We first make use of a K-means clustering algorithm to overcome the congestion problem in an machine-to-machine network where each MTCD is associated with one controller. Subsequently, we formulate the slot selection problem as an optimisation problem. Then, we present a solution using the Q-learning algorithm to select conflict-free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyse the performance of the proposed algorithm considering different parameters related to its operation. The convergence time, that is, the time required to reach a solution, decreases with increasing value of learning rate, whereas the convergence probability increases. In addition, for smaller values of learning rate, the convergence time decreases with increasing reward values. We also compare with simple ALOHA and channel-based scheduled allocation and show that the proposed Q-learning-based technique has a higher probability of assigning slots compared with these techniques. Copyright

Multi-objective resource allocation in interference-limited M2M communication networks

In this paper, we propose a multi-objective optimisation-based solution to the problem of resource allocation in interference-limited machine to machine M2M communication. We consider machine type communication devices MTCDs in a clustered network structure, where they are divided into clusters and the devices belonging to a cluster communicate to cluster head or controller. The cluster head aggregates the traffic and relays from MTCDs to eNB and vice versa. We maximise the number of admitted MTCD controllers and throughput with least interference caused to conventional primary users. We formulate the problem as a mixed-integer nonlinear problem with multiple objectives and apply meshed adaptive direct search MADS algorithm which gives guaranteed convergence. Simulation results show the effects of varying different parameters on cumulative throughput and the number of admitted MTCD controllers.

Cross Layer Optimization for ZigBee-Based Transmission Line Monitoring and Data Collection

A hybrid network architecture comprised of wireless sensor networks and cellular network is considered to monitor the high voltage transmission lines (HVTL). Corona noise is one of the sources of electromagnetic interference in HVTL. Automatic repeat request (ARQ) is utilized to increase the system reliability and to reduce the errors due to impulsive Corona noise. By using ARQ, link reliability is increased at the expense of communication delay. In this paper, the objective is to find the optimum location of ZigBee gateways such that reliability and latency constraints are satisfied. A cross-layer optimization technique is used to illustrate the effects of physical and MAC layer parameters on the QoS of the data collection.

IoT Applications and Business Models

This chapter discusses various IoT applications. It explains how IoT will shape our lives in the near future. As it is a widely held opinion that conventional business models cannot adequately support IoT applications, the chapter also elaborates on the need for formulating specialized models for IoT businesses to remain viable. Current challenges in terms of IoT structure, governance, and services are also presented.

Big Data and Fog Computing

In this chapter, we discuss big data generated from IoT devices and related challenges. We also discuss suitability of cloud and Fog computing for specific IoT applications. Afterwards, we present architecture for smart parking system and discus the suitability of Fog platform for this scenario.

Communication Technologies in IoT Networks

In this chapter, various types of IoT sensors and mode of communication between them is discussed. We also discuss cooperative mode of operation in sensor networks and outlines various topologies that can be utilized. Performance analysis of cooperative communication with respective to IoT networks is also presented.

OFCDM-based small femtocells embedded in OFDM-based macro cellular network

To extend indoor coverage and offload cellular network traffic, femtocell technology becomes more promising. Multi-carrier spread spectrum technology has proven to be effective in multiple access and provides robust performance in fading radio channels. In this paper, VSF-OFCDM is utilized for sub-carrier allocation with two dimensional spreading (2D) for femto users (FUEs) in small cells that are deployed within an OFDMA macro network. The performance of femto/macro hybrid network is investigated by deriving closed form expressions for the SINR for FUEs and macro users (MUEs) in uplinks, that are impacted by different types of interferers, 2D spreading factors and load conditions. We also evaluate the BER performance of FUEs and MUEs through Monte Carlo simulation in interference- and noise-limited scenarios for different parameters.