Power analysis of local transmission technologies

With the number of Internet of Things (IoT) devices expected to explode to over 20 Billion devices by 2020, it is vital that efficient communication technologies are used. While ideally a single technology would emerge to simplify deployment, in practice the varying power and bandwidth requirements of different devices has led to an industry split over communication technologies, and while a number of new technologies have been designed with IoT in mind, commercial imperatives have meant that existing wireless protocols, in particular Wi-Fi and 433 MHz AM, remain the most prevelent. This article outlines the power usage of these two most common protocols, and considers power aspects of using each protocol in an IoT setting with experiments carried out with real world devices used in current products

MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices

The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or `things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important task as it allows software systems to understand the environment better. Many different hardware devices may involve in the process of collecting and uploading sensor data to the cloud where complex processing can occur. Further, we cannot expect all these objects to be connected to the computers due to technical and economical reasons. Therefore, we should be able to utilize resource constrained devices to collect data from these ICOs. On the other hand, it is critical to process the collected sensor data before sending them to the cloud to make sure the sustainability of the infrastructure due to energy constraints. This requires to move the sensor data processing tasks towards the resource constrained computational devices (e.g. mobile phones). In this paper, we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT middleware for mobile devices, that allows to collect and process sensor data without programming efforts. Our architecture also supports sensing as a service model. We present the results of the evaluations that demonstrate its suitability towards real world deployments. Our proposed middleware is built on Android platform

Sensing as a Service Model for Smart Cities Supported by Internet of Things

The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in ICT to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today infrastructure, platforms, and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the Internet of Things. Our objective is to investigate the concept of sensing as a service model in technological, economical, and social perspectives and identify the major open challenges and issues.Comment: Transactions on Emerging Telecommunications Technologies 2014 (Accepted for Publication

The Internet of Things: the future or the end of mechatronics.

The advent and increasing implementation of user configured and user oriented systems structured around the use of cloud configured information and the Internet of Things is presenting a new range and class of challenges to the underlying concepts of integration and transfer of functionality around which mechatronics is structured. It is suggested that the ways in which system designers and educators in particular respond to and manage these changes and challenges is going to have a significant impact on the way in which both the Internet of Things and mechatronics develop over time. The paper places the relationship between the Internet of Things and mechatronics into perspective and considers the issues and challenges facing systems designers and implementers in relation to managing the dynamics of the changes required

Optimizing Mobile Backhaul Using Machine Learning

The thesis focuses on the analysis of current limitations of the mobile backhaul solutions technology when applied to 5G technology. The fast growth in connected devices along with the introduction of 5G technology is expected to cause a challenge for efficient and reliable network resource allocation. Moreover, massive deployment of Internet of Things and connected devices to the Internet may cause a serious risk to the network security if they are not handled properly. To solve those challenges, the Mobile Back haul (MB) infrastructure must increase capacity, improve reliability, availability and security. Software Defined Networks (SDN) and Machine Learning (ML) techniques were used on top of the basic IP routing to measure and estimate the available resources in the network and apply Traffic Engineering (TE) logic to reallocate available resources to newly added slices. The experiment was performed in a virtual environment using Mininet simulator tool and other opensource software and ML algorithms. In this thesis, a system was developed to measure the existing resources in the mobile backhaul and redistribute dynamically to different network slices either existing or new slices to make sure that each slice requirements are met. The thesis includes an early prototype of the Mobile Backhaul Orchestrator (MBO) that will be simulated to confirm it can effectively allocate resources to new slices while maintaining existing slices, and that it can contain the traffic within a slice during peaks without affecting traffic in other slices

Dynamic Configuration of Sensors Using Mobile Sensor Hub in Internet of Things Paradigm

Internet of Things (IoT) envisions billions of sensors to be connected to the Internet. By deploying intelligent low-level computational devices such as mobile phones in-between sensors and cloud servers, we can reduce data communication with the use of

Dynamic Configuration of Sensors Using Mobile Sensor Hub in Internet of Things Paradigm

Abstract—Internet of Things (IoT) envisions billions of sensors to be connected to the Internet. By deploying intelligent lowlevel computational devices such as mobile phones in-between sensors and cloud servers, we can reduce data communication with the use of intelligent processing such as fusing and filtering sensor data, which saves significant amount of energy. This is also ideal for real world sensor deployments where connecting sensors directly to a computer or to the Internet is not practical. Most of the leading IoT middleware solutions require manual and labour intensive tasks to be completed in order to connect a mobile phone to them. In this paper we present a mobile application called Mobile Sensor Hub (MoSHub). It allows variety of different sensors to be connected to a mobile phone and send the data to the cloud intelligently reducing network communication. Specifically, we explore techniques that allow MoSHub to be connected to cloud based IoT middleware solutions autonomously. For our experiments, we employed Global Sensor Network (GSN) middleware to implement and evaluate our approach. Such automated configuration reduces significant amount of manual labour that need to be performed by technical experts otherwise. We also evaluated different methods that can be used to automate the configuration process. I