scholarly journals Adjusting Group Communication in Dense Internet of Things Networks with Heterogeneous Energy Sources

2019 ◽  
Author(s):  
Renato Mota ◽  
André Riker ◽  
Denis Rosário
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Group Communication ◽  
State Of The Art ◽  
Dynamic Control ◽  
Energy Resources ◽  
Extensive Evaluation ◽  
Message Loss ◽  
Loss Event ◽  
Iot Devices

Internet-of-Things (IoT) environments will have a large number of nodes organized into groups to collect and to disseminate data. In this sense, one of the main challenges in IoT environments is to dynamically manage communication characteristics of IoT devices to decrease congestion, traffic collisions, and excessive data collection, as well as to balance the use of energy resources. In this paper, we introduce an energy-efficient and reliable Self Adjusting group communication of dense IoT Network, called SADIN. It configures the communication settings to ensure a dynamic control of IoT devices considering a comprehensive set of aspects, i.e., traffic loss, event relevance, amount of nodes with renewable batteries, and the number of observers. Specifically, SADIN changes the communication interval, the number of data producers, the reliability level of the network. Extensive evaluation results show that SADIN improves system performance in terms of message loss, energy consumption, and reliability compared to state-of-the-art protocol.

scholarly journals The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

2021 ◽  
Vol 2021 (1) ◽  
pp. 209-228
Author(s):  
Yuantian Miao ◽  
Minhui Xue ◽  
Chao Chen ◽  
Lei Pan ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Rapid Development ◽  
Black Box ◽  
Problem Space ◽  
Specific Data ◽  
Learning Techniques ◽  
Audio Data ◽  
Iot Devices ◽  
Asr System

AbstractWith the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy.

scholarly journals Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3208 ◽  
Author(s):  
Armin Babaei ◽  
Gregor Schiele
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
The Internet ◽  
Physical Unclonable Functions ◽  
Iot Devices ◽  
Computational Resources ◽  
The Internet Of Things

Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility with IoT devices with limited computational resources. In this paper, we investigate the maturity of this technology and the challenges toward PUF utilization in IoT that still need to be addressed.

scholarly journals Mobility-Centric Analysis of Communication Offloading for Heterogeneous Internet of Things Devices

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Dmitry Kozyrev ◽  
Aleksandr Ometov ◽  
Dmitri Moltchanov ◽  
Vladimir Rykov ◽  
Dmitry Efrosinin ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Power Efficiency ◽  
Base Stations ◽  
Mathematical Framework ◽  
Content Dissemination ◽  
Iot Devices ◽  
The Impact ◽  
New Infrastructure

Today, the number of interconnected Internet of Things (IoT) devices is growing tremendously followed by an increase in the density of cellular base stations. This trend has an adverse effect on the power efficiency of communication, since each new infrastructure node requires a significant amount of energy. Numerous enablers are already in place to offload the scarce cellular spectrum, thus allowing utilization of more energy-efficient short-range radio technologies for user content dissemination, such as moving relay stations and network-assisted direct connectivity. In this work, we contribute a new mathematical framework aimed at analyzing the impact of network offloading on the probabilistic characteristics related to the quality of service and thus helping relieve the energy burden on infrastructure network deployments.

scholarly journals ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110353
Author(s):  
Mohammad Babar ◽  
Muhammad Sohail Khan
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Computation Time ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Efficient Computation ◽  
Smart Systems ◽  
Iot Devices ◽  
Delay Tolerant

Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.

Energy-Efficient Transaction Serialization for IoT Devices

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Daniel Evans
Keyword(s):  
Data Structure ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Minimal Processing ◽  
Processor Time ◽  
Loss Of Information ◽  
Xml Document ◽  
Dynamic Storage ◽  
Iot Devices ◽  
Performance Comparisons

Two designs, the Transaction Serial Format (TSF) and the Transaction Array Model (TAM), are presented. Together, they provide full, efficient, transaction serialization facilities for devices with limited onboard energy, such as those in an Internet of Things (IoT) network. TSF provides a compact, non-parsed, format for transactions, which can be deserialized with minimal processing. TAM provides an internal data structure, that can be constructed with minimal dynamic storage directly using the elements of TSF. TSF is built from simple lexical units that do not require parsing to be extracted from a serialized transaction. The lexical units contain enough information to efficiently allocate the internal TAM data structure. TSF generality is shown by exhibiting its equivalence to XML and JSON. The TSF representation of any XML document or JSON object can be serialized and deserialized without loss of information, including whitespace. The XML equivalence provides a foundation for the performance comparisons. TSF efficiency is shown by comparing the performance of reference implementations of TSF and TAM, written in C, to the performance of the popular Expat XML library, also written in C. TSF deserialization is shown to reduce processor time by more than 80%, demonstrating the efficiency of the design.

Energy-Efficient System Design for Internet of Things (IoT) Devices

2019 ◽  
pp. 49-74
Author(s):  
Neeta Singh ◽  
Sachin Kumar ◽  
Binod Kumar Kanaujia ◽  
Hyun Chul Choi ◽  
Kang Wook Kim
Keyword(s):  
Internet Of Things ◽  
System Design ◽  
Energy Efficient ◽  
Efficient System ◽  
Iot Devices

scholarly journals Energy-Efficient Industrial Internet of Things Software-Defined Network by Means of the Peano Fractal

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2855 ◽  
Author(s):  
Jesus Jaime Moreno Escobar ◽  
Oswaldo Morales Matamoros ◽  
Ixchel Lina Reyes ◽  
Ricardo Tejeida-Padilla ◽  
Liliana Chanona Hernández ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
State Of The Art ◽  
Economic Benefits ◽  
Wireless Sensor ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Share Data ◽  
Light Diffusion

The Industrial Internet of Things (IIoT) network generates great economic benefits in processes, system installation, maintenance, reliability, scalability, and interoperability. Wireless sensor networks (WSNs) allow the IIoT network to collect, process, and share data of different parameters among Industrial IoT sense Node (IISN). ESP8266 are IISNs connected to the Internet by means of a hub to share their information. In this article, a light-diffusion algorithm in WSN to connect all the IISNs is designed, based on the Peano fractal and swarm intelligence, i.e., without using a hub, simply sharing parameters with two adjacent IINSs, assuming that any IISN knows the parameters of the rest of these devices, even if they are not adjacent. We simulated the performance of our algorithm and compared it with other state-of-the-art protocols, finding that our proposal generates a longer lifetime of the IIoT network when few IISNs were connected. Thus, there is a saving-energy of approximately 5% but with 64 nodes there is a saving of more than 20%, because the IIoT network can grow in a 3 n way and the proposed topology does not impact in a linear way but log 3 , which balances energy consumption throughout the IIoT network.

scholarly journals Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4050 ◽  
Author(s):  
Haneul Ko ◽  
Jaewook Lee ◽  
Seokwon Jang ◽  
Joonwoo Kim ◽  
Sangheon Pack
Keyword(s):  
Internet Of Things ◽  
Outage Probability ◽  
Energy Efficient ◽  
Energy Levels ◽  
Strategy Evaluation ◽  
Battery Capacity ◽  
Markov Decision ◽  
Computation Algorithm ◽  
Cooperative Computation ◽  
Iot Devices

The limited battery capacity of Internet of Things (IoT) devices is a major deployment barrier for IoT-based computing systems. In this paper, we propose an energy efficient cooperative computation algorithm (EE-CCA). In an EE-CCA, a pair of IoT devices decide whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most of tasks before their deadlines, we formulate a constraint Markov decision process (CMDP) problem and the optimal offloading strategy is obtained by linear programming (LP). Meanwhile, an optimization problem of finding pairs of IoT devices (i.e., IoT device pairing problem) is formulated under the optimal offloading strategy. Evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78 % compared with the random offloading scheme while completing tasks before their deadlines with high probability.

Internet of things-based energy-efficient optimized heuristic framework to monitor sportsperson’s health

2021 ◽  
pp. 1-14
Author(s):  
Cui Meng Yao ◽  
Parthasarathy Poovendran ◽  
S. Stewart Kirubakaran
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Energy Efficient ◽  
Survival Rates ◽  
Health And Safety ◽  
Current Medical Research ◽  
Performance Ratio ◽  
Accuracy Ratio ◽  
Fitness Tracking ◽  
Iot Devices

BACKGROUND: Recently, wearable technologies have gained attention in diverse applications of the medical platform to guarantee the health and safety of the sportsperson with the assistance of the Internet of things (IoT) device. The IoT device’s topology varies due to the shift in users’ orientation and accessibility, making it impossible to assign resources, and routing strategies have been considered the prominent factor in the current medical research. Further, for sportspersons with sudden cardiac arrests, hospital survival rates are low in which wearable IoT devices play a significant role. OBJECTIVE: In this paper, the energy efficient optimized heuristic framework (EEOHF) has been proposed and implemented on a wearable device of the sportsperson’s health monitoring system. METHOD: The monitoring system has been designed with cloud assistance to locate the nearest health centers during an emergency. The wearable sensor technologies have been used with an optimized energy-efficient algorithm that helps athletes monitor their health during physical workouts. The monitoring system has fitness tracking devices, in which health information is gathered, and workout logs are tracked using EEOHF. The proposed method is applied to evaluate and track the sportsperson’s fitness based on case study analysis. RESULTS: The simulation results have been analyzed, and the proposed EEOHF achieves a high accuracy ratio of 97.8%, a performance ratio of 95.3%, and less energy consumption of 9.4%, delay of 13.1%, and an average runtime of 98.2% when compared to other existing methods.

scholarly journals Enabling Processing Power Scalability with Internet of Things (IoT) Clusters

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 81
Author(s):  
Jorge Coelho ◽  
Luís Nogueira
Keyword(s):  
Internet Of Things ◽  
Programming Language ◽  
Network Traffic ◽  
Crucial Role ◽  
State Of The Art ◽  
End User ◽  
Processing Power ◽  
Increasing Demand ◽  
Iot Devices ◽  
Traditional Approaches

Internet of things (IoT) devices play a crucial role in the design of state-of-the-art infrastructures, with an increasing demand to support more complex services and applications. However, IoT devices are known for having limited computational capacities. Traditional approaches used to offload applications to the cloud to ease the burden on end-user devices, at the expense of a greater latency and increased network traffic. Our goal is to optimize the use of IoT devices, particularly those being underutilized. In this paper, we propose a pragmatic solution, built upon the Erlang programming language, that allows a group of IoT devices to collectively execute services, using their spare resources with minimal interference, and achieving a level of performance that otherwise would not be met by individual execution.

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