Energy-Efficient Computing Solutions for Internet of Things with ZigBee Reconfigurable Devices

Energy-Efficient Computing Solutions for Internet of Things with ZigBee Reconfigurable Devices

Grzegorz Chmaj (Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV, USA) and Henry Selvaraj (Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV, USA)
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJSI.2016010103
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Nowadays we are witnessing a trend with significantly increasing number of networked and computing-capable devices being integrated into everyday environment. This trend is expected to continue. With computing devices available as logic structures, they might use each other's processing capabilities to achieve a given goal. In this paper, the authors propose an architectural solution to perform the processing of tasks using a distributed structure of Internet of Things devices. They also include ZigBee devices that are not connected to the Internet, but participate with the processing swarm using local network. This significantly extends the flexibility and potential of the IoT structure, while being still not a well-researched area. Unlike many high-level realizations for IoT processing, the authors present a realization operating on the communications, computing and near protocol level that achieves energy consumption efficiency. They also include the reconfigurability of IoT devices. The authors' work is suitable to be the base for higher-level realizations, especially for systems with devices operating on battery power. At the same time, the architecture presented in this paper uses minimal centralization, moving maximum responsibilities to regular devices. The proposed realizations are described using linear programming models and their high efficiency is evaluated.
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1. Introduction

Distributed computing is being used for high performance structures such as grids, clusters and public computation systems for many years, and considered as very large system architecture. With the ever increasing popularity of mobile devices, the processing power in consumers’ pockets, raised the researchers’ interests in mobile computing. Reducing device size, together with increasing processors’ computing power and lowering energy consumption, leads to the emergence of computation-capable devices in day-to-day environment. The combination of all the assets, along with high accessibility of Internet, created the Internet of Things (IoT) structures. They contain multiple devices that are connected to the Internet and thus gain the wide spectrum of the new applications. Among those, there are many that require a lot of processing power that is not available on a single device. As other IoT devices are available and presumably might stay in the idle mode, we propose solutions that enable the devices in the nearby/logical group to share their resources and achieve the group's processing goal.

Distributed computing was mainly a domain of cloud and grid computing for many years. It is worth to mention, that distributed processing includes computing and many other architectures, such as distributed web services (Takatsuka, Saiki, Matsumoto and Namamura, 2015). The recent development of the Internet of Things idea has led to the emergence of a new research field integrating these two areas, both in the general case (Botta, de Donato, Persico, and Pescap´e, 2014)(Lumpkins, 2013), and for specific applications (Gachet, de Buenaga, Aparicio, and Padrón, 2012). Also, they are the area of the interest on much lower level – Chiang and Lee (2013) proposed the coordination languages and models for the open distributed systems, presenting the new approach to programming such structures. The system described by Gachet et al. (2012) provides the IoT/Cloud computing for healthcare services. The authors discussed the IoT-based improvements of the quality of life of people with chronic diseases. There were multiple middleware solutions proposed to realize the context-aware computing in Internet of Things (Perera, Zaslavsky, Christen and Georgakopoulos, 2014). Such middleware enables the services that would run on a given infrastructure – typically they are defined on the high level. There are also solutions based on the event driving (Kuhn, Prellwitz, Rohrer, and Sieck, 2013) – authors presented the middleware using event detection and forwarding, and triggering actions based on that. Smart housing is a popular topic when talking about IoT applications, however the solutions proposed in the literature are mostly centrally-managed (Kuhn et al., 2013)(Perera et al., 2014) or do not include energy optimization. Soliman, Abiodun, Hamouda, Zhou, and Lung (2013) presented a solution of integrating the smart home system with the webservices and cloud computing. Embedded systems were used for sensing and cloud computing was used for inter-device interaction. The approach presented by Spanò, Niccolini, Pascoli and Iannaccone (2015) merges the household smart meters with the IoT platform – to optimize the operation of connected devices. The definition of Internet of Things includes devices that are directly connected to the Internet network. Local devices based on protocols such as ZigBee are starting to be included in IoT systems too (Soliman et al., 2013)(Spanò et al., 2015), but this area has not seen significant growth yet. Another part of Internet of Things structures are the wearable devices, often also not directly connected to the Internet, but having the communication capabilities (e.g. using ZigBee). Wearables might deliver very specific type of data (such as motion characteristics (Jara, Bocchi and Genoud, 2013), healthcare information (Castillejo, Martínez, Rodríguez-Molina and Cuerva, 2013)) and inevitably are becoming the increasing share of IoT devices (Hiremath, Yang and Mankodiya, 2014). The multiple types of devices and applications lead to the perspective of interconnected IoTs, as presented by Wirtz and Wehrle (2013) – where authors discussed the possibilities of integrating multiple IoT designs.

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