Analysis of Performance and Energy Consumption of Wearable Devices and Mobile Gateways in IoT Applications

Nakhkash, Mohammad R.; Gia, Tuan Nguyen; Azimi, Iman; Anzanpour, Arman; Rahmani, Amir M.; Liljeberg, Pasi

doi:10.1145/3312614.3312632

Cited by 25 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the downside is increased latency [129]. Another complexity lies in the effective splitting of tasks into locally-and remotely executable tasks that could run independently on nearby devices [105]. Therefore, task offloading may work effectively for delay-tolerant applications to minimize energy consumption.…”

Section: A Task Offloadingmentioning

confidence: 99%

“…A, N, SW [46], [111] Energy awareness regarding neighboring nodes to select the optimal route [50] Multi parameter cost function for the next hop selection [60] Selective data routing based on the data priority Securityrelated aspects HW, DP, SW [110] Content agnostic privacy and encryption protocol eliminating the need for asymmetric encryption [180], [181] Integration of lightweight cryptography solutions including more appropriate elliptic curve types or algorithm implementations [186] More efficient utilization of manufacturer-provide SoCs accelerated for cryptographic primitives execution [187] Finding trade-offs between the primitive and required level of the provided security Processing limitations HW, DP, SW [54] The use of heterogeneous multicore processor gateway as compared to little cores gateway working as a router [64], [86], [104], [105] Task offloading to leverage high computing resources of nearby devices for improved performance [106] Edge/fog/cloud computing techniques for optimal performance [107] Seamless resource sharing between heterogeneous mobile devices Storage limitations HW [47], [55] Data compression to reduce the size of the dataset for efficient data processing and storage [106] Edge/Fog/Cloud computing techniques for better performance [173] Data summarization and aggregation Lack of hardware acceleration HW, SW [47], [55] Data compression to reduce the size of the data set for more efficient data processing and storage [64], [86], [104], [105] Task offloading to leverage high computing resources of the nearby devices for the improved performance [186] Identifying and use of present hardware acceleration, which may not be accessible by the default Inefficient use of energy consuming modules HW, SW [62] Configurable data acquisition modules [88] Replacing high power consumption modules with low power alternates, e.g., using two accelerometers instead of a gyroscope as...…”

Section: Inefficient Routingmentioning

confidence: 99%

See 1 more Smart Citation

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

View full text Add to dashboard Cite

Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

show abstract

Section: A Task Offloadingmentioning

confidence: 99%

Section: Inefficient Routingmentioning

confidence: 99%

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The following subsections provide a short description of how each quality aspect has been addressed. Performance [20], [23], [24], [30], [33][34][35][36][37][38], [40], [42], [43], [45][46][47][48], [50][51][52][53][54][55][56], [58], [60], [62], [64][65][66][67], [71][72][73] 34…”

Section: (Rq26 and Rq27) Mapping Addressed Quality Aspectsmentioning

confidence: 99%

Model-Based Quality Assessment of Internet of Things Software Applications: A Systematic Mapping Study

Hamarash

2020

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

The Internet of things (IoT) is growing at a fast pace owing to its vast applications in varied fields such as medicine, society, economy, and even the military. This growth cannot continue without establishing high quality. Over the past decade, interest in research for the quality assurance of IoT has gradually grown. However, the discipline is still evolving, and further research is required to investigate the various quality-related aspects. Although assessing the entire system is impractical, to assure the quality of IoT applications, various assessment levels are required. A well-known and established approach to mitigate this difficulty is to model the entire system or a few parts of it for the sake of assessment, which is known as model-based testing. To determine what has been achieved thus far and what is lacking in this direction, this paper presents an extensive study on the use of the model-based approach to assure the quality of IoT applications. The study systematically reviews papers published from 2009 (early publications on IoT) to 2019 that reported the explicit use of models to assess the quality aspects of IoT applications. As a result of an extensive search process, the paper presents the results of scanning and reviewing 390 published papers. Thus far, out of these, 54 studies used the model-based approach to assess at least one quality aspect of an IoT application. In addition to the several relevant research questions that have been addressed in this study, this paper also presents several new insights and approaches for future research.

show abstract

“…Next, other proposals related to the scope of our research are described: an approach for power and energy usage for scientific calculation with and without General-Purpose Unit (GPU) acceleration on RPi devices can be found in [39]; energy and execution time of several wearable and mobile devices, including RPi Zero, are compared in [40] with a benchmark to discuss offloading techniques to use for increasing quality of service (QoS) in IoT applications; a preliminary analysis and modeling of energy consumption of evolutionary algorithms in different devices, including RPi, is introduced in [41]; an estimation of energy consumption in transferring data using an IoT protocol over different QoS levels is presented in [42]; a linear IoT model to deploy processes and data to devices and servers in IoT (considering a RPi as a fog node) reducing the total energy consumption of nodes is introduced in [43]; a study about the service distribution in multi-layer IoT architecture to minimize the total energy consumption is presented in [44]; the evolution of the energy consumption of several RPi models is compared to alternative platforms in [45].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Energy Consumption and Optimization Techniques for Writing Energy-Efficient Code

Corral-García¹,

Lemus-Prieto²,

Pérez-Toledano

2019

Electronics

View full text Add to dashboard Cite

The unprecedented growth of connected devices, together with the remarkable convergence of a wide variety of technologies, have led to an exponential increase in the services that the internet of things (IoT) can offer, all aimed at improving quality of life. Consequently, in order to meet the numerous challenges this produces, the IoT has become a major subject of research. One of these challenges is the reduction of energy consumption given the significant limitations of some devices. In addition, although the search for energy efficiency was initially focused on hardware, it has become a concern for software developers too. In fact, it has become an intense area of research with the principal objective of analyzing and optimizing the energy consumption of software systems. This research analyzes the energy saving that can be achieved when using a broad set of techniques for writing energy-efficient code for Raspberry Pi devices. It also demonstrates that programmers can save more energy if they apply the proposed techniques manually than when relying on other automatic optimization options offered by the GNU compiler collection (GCC). Thus, it is important that programmers are aware of the significant impact these techniques can have on an application’s energy consumption.

show abstract

Analysis of Performance and Energy Consumption of Wearable Devices and Mobile Gateways in IoT Applications

Cited by 25 publications

References 15 publications

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Model-Based Quality Assessment of Internet of Things Software Applications: A Systematic Mapping Study

Analysis of Energy Consumption and Optimization Techniques for Writing Energy-Efficient Code

Contact Info

Product

Resources

About