Challenges and research directions for Internet of Things

Hammoudi, Sarra; Aliouat, Zibouda; Harous, Saad

doi:10.1007/s11235-017-0343-y

Cited by 63 publications

(40 citation statements)

References 29 publications

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“…In this way, IoT allows all kinds of physical objects to connect to the Internet and interact without the need for human intervention, turning these objects into intelligent devices [ 1 ]. However, despite its growth potential it still faces many challenges, mainly related to addressing, routing, standardization, restricted physical capabilities (e.g., energy, processing, and memory), security, privacy, and openness [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices

Corral-García¹,

González-Sánchez²,

Pérez-Toledano

2018

Sensors

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The Internet of Things (IoT) is faced with challenges that require green solutions and energy-efficient paradigms. Architectures (such as ARM) have evolved significantly in recent years, with improvements to processor efficiency, essential for always-on devices, as a focal point. However, as far as software is concerned, few approaches analyse the advantages of writing efficient code when programming IoT devices. Therefore, this proposal aims to improve source code optimization to achieve better execution times. In addition, the importance of various techniques for writing efficient code for Raspberry Pi devices is analysed, with the objective of increasing execution speed. A complete set of tests have been developed exclusively for analysing and measuring the improvements achieved when applying each of these techniques. This will raise awareness of the significant impact the recommended techniques can have.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices

Corral-García¹,

González-Sánchez²,

Pérez-Toledano

2018

Sensors

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“…represents the architecture of IoT based Sensor Data Collection of Carbon Monoxide. This Architecture consists of four major layers [6]. First layer is Sensing Layer which is responsible for collecting the data related to the Carbon monoxide emissions.…”

Section: Iot Based Sensor Data Collection Of Carbon Monoxidementioning

confidence: 99%

“…Universal Unique IDentifier (UUID) is a method of allot-ting unique identity to the smart object. Cost, energy consumption, size, resource, deployment method, topology of the network, Protocols are perspectives to be considered in designing the sensing layer of an IoT [6].The techniques used in the sensing layer are the Wireless Sensor Networks, RFID, barcode and so on.…”

Section: Sensing Layer (Wireless Sensor Devices)mentioning

confidence: 99%

IoT for monitoring carbon monoxide (CO) emissions using wireless sensor networks in smart cities

Paruchuri¹,

Rajesh²

2018

IJET

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A Smart city is characterized by the efficient use of information technology and industrial assets for financial improvement. Internet of Things (IoT) is an arrangement of embedded devices that communicates by using the internet and uniquely addressable with standard protocols. Application areas of Internet of things are smart cities, environmental protection, smart transportation, healthcare, agriculture and public security. Development of Smart cities leading to Pollution. Air Pollution is an environmental health concern for the public. Carbon Monoxide is a harmful gas to the human beings and also the leading cause of air pollution. So it is necessary to develop a mechanism for the detection of carbon monoxide molecules. This paper presents a survey on the methods of measuring the Carbon Monoxide emissions by using Wireless Sensor Networks. Availability of CO data from a monitoring program can be utilized for providing the awareness to the public about the air pollution.

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“…The perturbations that lead to a change in the synaptic parameters of the implemented spiking neural network often result in a greatly degraded performance of the network [11]. At the same time, smaller sensors require smaller networks due to power restrictions [12]. Thus, taking neuromorphic computing to the next step and developing small size SNNs that are also resilient to perturbations of their parameters is a necessity.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

Dimovska

Johnston

Schuman

et al. 2019

2019 IEEE 10th Annual Ubiquitous Computing, Electronics &Amp; Mobile Communication Conference (UEMCON)

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Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the developed hardware can greatly depend on the size of the network that is being evaluated on the chip. Furthermore, the accuracy of a trained SNN that is evaluated on chip can change due to voltage and current variations in the hardware that perturb the learned weights of the network. While efforts are made on the hardware side to minimize those perturbations, a software based strategy to make the deployed networks more resilient can help further alleviate that issue. In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults. We leverage an evolutionary algorithm to train the SNNs and propose a multiobjective fitness function to optimize the size and resiliency of the SNN. We demonstrate that this strategy leads to well-performing, small-sized networks that are more resilient to hardware faults.

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Challenges and research directions for Internet of Things

Cited by 63 publications

References 29 publications

Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices

Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices

IoT for monitoring carbon monoxide (CO) emissions using wireless sensor networks in smart cities

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

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