Internet Of Things End Devices Research Articles

Combination of Task Allocation and Approximate Computing for Fog-Architecture-Based IoT

Achieving energy efficiency is always a primary concern for fog-architecture-based Internet of Things (IoT) applications. As the IoT devices are typically of small sizes and powered by battery energy, it is essential to address the energy consumption at all levels from the circuit to the system. Two of the promising solutions at circuit and system levels are approximate computing and energy-aware task allocation, respectively. However, the existing task allocation approaches are designed without considering the aspect of approximate computing. In this work, we fill this gap and aim to maximize the network lifetime subject to the accuracy requirements of the applications. By considering both the approximate computing and task allocation simultaneously, a nonlinear problem is obtained to allocate the tasks for the devices (fog nodes and IoT end devices) and to select the corresponding execution modes (tasks in approximate or exact modes). To efficiently solve this problem, a centralized algorithm is first proposed by transferring the above nonlinear problem as a linear programming (LP) problem. As executing the centralized algorithm is a challenge for the resource-limited IoT devices, this work further proposes an optimal distributed algorithm based on Dantzig-Wolfe decomposition to solve the problem of tasks distribution and execution modes selection. The centralized large-scaled LP problem is decomposed into small-scaled subproblems, which can be efficiently solved by each IoT device. The proposed algorithms are tested by extensive simulations. The results demonstrate that the distributed algorithm achieves the same results as the centralized algorithm, and both of them significantly outperform the previous approaches.

Fog data management: A vision, challenges, and future directions

Cloud computing with its key facets and its inherent advantages still faces several challenges in the Internet of Things (IoT) ecosystem. The distance among the IoT end devices and cloud computing might be a problem for latency-sensitive applications such as catastrophe management and content transference applications. Fog computing is a novel paradigm to address such issues that playacts a significant role in massive and real-time data management systems in an IoT environment. Particularly IoT data management by fog computing is one important phase for latency reduction in latency-sensitive applications and necessary to generate more skilled knowledge and intelligent decisions. In this study, we used the SLR (systematic literature review) method to survey fog data management to understand the various topics and main contexts in this domain that have been newly offered. The target of this article is classifying and analyzing the researches about the fog data management domain which has been released from 2014 to 2019. A context-based taxonomy is offered for fog data management including data processing, data storage and data security based on the context of papers that are elected with the SLR method in our study. Based on presented technical taxonomy, the grouped papers in any context are compared with each other pursuant to some metrics of fog data management reference model. Then, for any selected research, the new findings, advantages, and weaknesses are debated. Finally, based on studies the open issues in fog data management and their related challenges for future researches are highlighted.

Hardware Security of Fog End-Devices for the Internet of Things.

The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

Threats and Vulnerabilities to IoT End Devices Architecture and suggested remedies

Due to decentralization of Internet of Things(IoT) applications and anything, anytime, anywhere connectivity has increased burden of data processing and decision making at IoT end devices. This overhead initiated new bugs and vulnerabilities thus security threats are emerging and presenting new challenges on these end devices. IoT End Devices rely on Trusted Execution Environments (TEEs) by implementing Root of trust (RoT) as soon as power is on thus forming Chain of trust (CoT) to ensure authenticity, integrity and confidentiality of every bit and byte of Trusted Computing Base (TCB) but due to un-trusted external world connectivity and security flaws such as Spectre and meltdown vulnerabilities present in the TCB of TEE has made CoT unstable and whole TEE are being misutilized. This paper suggests remedial solutions for the threats arising due to bugs and vulnerabilities present in the different components of TCB so as to ensure the stable CoT resulting into robust TEE.

Approximate CPU Design for IoT End-Devices with Learning Capabilities

With the rise of Internet of Things (IoT), low-cost resource-constrained devices have to be more capable than traditional embedded systems, which operate on stringent power budgets. In order to add new capabilities such as learning, the power consumption planning has to be revised. Approximate computing is a promising paradigm for reducing power consumption at the expense of inaccuracy introduced to the computations. In this paper, we set forth approximate computing features of a processor that will exist in the next generation low-cost resource-constrained learning IoT devices. Based on these features, we design an approximate IoT processor which benefits from RISC-V ISA. Targeting machine learning applications such as classification and clustering, we have demonstrated that our processor reinforced with approximate operations can save power up to 23% for ASIC implementation while at least 90% top-1 accuracy is achieved on the trained models and test data set.

A New Planning-Based Collision-Prevention Mechanism in Long-Range IoT Networks

Wireless communication is prone to collisions, resulting from multiple devices transmitting at the same time. It implies subsequent retransmissions, which increase energy consumption of communicating devices and reduce throughput of the network. This is especially critical in Internet of Things (IoT) networks, in which the number of connected devices grows rapidly and the available energy of IoT end devices is rather limited (e.g., energy harvesting and battery powered). The number of retransmissions must be reduced in order the networks to be sustainable. However, in long-range wireless IoT networks, the most effective collision-resolution techniques using a transmission-channel listening to detect collisions cannot be reliably used due to various problems, such as a hidden-node problem or environment interference. In this article, a new solution of this problem is proposed, which consists of a new communication-planning mechanism for low-speed long-range IoT networks with a huge number of communicating energy-constrained devices. The access points (APs) (or IoT gateways) are used to plan the periodically repeated communication into a transmission schedule, allowing only a single IoT device to communicate at a time. This approach results in reduction of collisions, which leads to the increased network throughput, smaller delays, and lower power requirements of energy-constrained devices. The experiments indicate that the proposed approach provides better communication efficiency than the LoRaWAN and Sigfox collision-resolution techniques, when more than 15, respectively 125, end devices communicate with a single AP.

MIGOU: A Low-Power Experimental Platform with Programmable Logic Resources and Software-Defined Radio Capabilities.

The increase in the number of mobile and Internet of Things (IoT) devices, along with the demands of new applications and services, represents an important challenge in terms of spectral coexistence. As a result, these devices are now expected to make an efficient and dynamic use of the spectrum, and to provide processed information instead of simple raw sensor measurements. These communication and processing requirements have direct implications on the architecture of the systems. In this work, we present MIGOU, a wireless experimental platform that has been designed to address these challenges from the perspective of resource-constrained devices, such as wireless sensor nodes or IoT end-devices. At the radio level, the platform can operate both as a software-defined radio and as a traditional highly integrated radio transceiver, which demands less node resources. For the processing tasks, it relies on a system-on-a-chip that integrates an ARM Cortex-M3 processor, and a flash-based FPGA fabric, where high-speed processing tasks can be offloaded. The power consumption of the platform has been measured in the different modes of operation. In addition, these hardware features and power measurements have been compared with those of other representative platforms. The results obtained confirm that a state-of-the-art tradeoff between hardware flexibility and energy efficiency has been achieved. These characteristics will allow for the development of appropriate solutions to current end-devices’ challenges and to test them in real scenarios.

Disclosing and Locating Concurrency Bugs of Interrupt-Driven IoT Programs

The Internet of Things (IoT) is envisioned as a distributed network formed by many end devices, e.g., the motes of wireless sensor network (WSN). These important IoT end devices enable ubiquitous sensing of environments and provide reliable services for mission-critical applications. However, programs running on WSN devices are typically interrupt-driven and prone to interrupt-induced concurrency bugs, which are primarily caused by erroneous interleavings among interrupt procedure instances (IPIs) (namely, executions of interrupt processing logic). In this paper, we use a set of dynamic bug patterns to characterize the concurrency bugs due to buggy access-interleavings among IPIs to shared resources, including shared memory locations and shared communication channels. By matching the above bug patterns, a dynamic analysis approach called disclosing and locating concurrency bugs of interrupt-driven IoT programs based on dynamic bug patterns (Daemon) is proposed to automatically detect and locate concurrency bugs in WSN programs. A GUI tool of Daemon is developed. As the empirical studies exhibit, the tool can discover concurrency bugs effectively and locate the buggy source lines visually.

Experimental Evaluation of an RSSI-Based Localization Algorithm on IoT End-Devices

In recent years, wireless sensor networks (WSNs) have experienced a significant growth as a fundamental part of the Internet of Things (IoT). WSNs nodes constitute part of the end-devices present in the IoT, and in many cases location data of these devices is expected by IoT applications. For this reason, many localization algorithms for WSNs have been developed in the last years, although in most cases the results provided are obtained from simulations that do not consider the resource constraints of the end-devices. Therefore, in this work we present an experimental evaluation of a received signal strength indicator (RSSI)-based localization algorithm implemented on IoT end-devices, comparing its results with those obtained from simulations. We have implemented the fuzzy ring-overlapping range-free (FRORF) algorithm with some modifications to make its operation feasible on resource-constrained devices. Multiple tests have been carried out to obtain the localization accuracy data in three different scenarios, showing the difference between simulation and real results. While the overall behaviour is similar in simulations and in real tests, important differences can be observed attending to quantitative accuracy results. In addition, the execution time of the algorithm running in the nodes has been evaluated. It ranges from less than 10 ms to more than 300 ms depending on the fuzzification level, which demonstrates the importance of evaluating localization algorithms in real nodes to prevent the introduction of large overheads that may not be affordable by resource-constrained nodes.

Frequency Resource Allocation and Interference Management in Mobile Edge Computing for an Internet of Things System

Internet of Things (IoT) systems are characterized by highly automated operating environments, which comprise several IoT end devices (IDs) that generate vast amounts of data with strict real-time communication and high data rate requirements. Edge computing facilities are an alternative to traditional cloud computing and support massive data processing in IoT systems while reducing the burden on data centers. In this paper, we consider an edge-based IoT system that comprises an edge server (ES), edge gateways (EGs), and IDs that communicate wirelessly. The EGs reduce the load on the ES by preprocessing data received from ID. However, it may not be possible for a few EGs to accommodate a sheer number of IDs, given the limited computing power and communication coverage of the EGs. Therefore, it is necessary for a few IDs to directly connect to the ES without the support of EGs. Thus, we propose a resource orchestration scheme between EGs and ES and/or among EGs based on a Lagrangian and the Karush–Kuhn–Tucker condition. The scheme allocates optimal resources by considering the computing capacities of EGs and ES and manages interference among the EGs to maximize the efficiency of IoT systems. The performance evaluation indicates that the proposed scheme outperforms the existing schemes in terms of aggregate throughput, latency, data reception rate, and workload fairness among EGs by 42%, 59%, 37%, and 40%, respectively.

LDAF: Low-Bandwidth Distributed Applications Framework in a Use Case of Blockchain-Enabled IoT Devices.

In this paper, we present Low-Bandwidth Distributed Applications Framework (LDAF)—an application-aware gateway for communication-constrained Internet of things (IoT) devices. A modular approach facilitates connecting to existing cloud backend servers and managing message formats and APIs’ native application logic to meet the communication constraints of resource-limited end devices. We investigated options for positioning the LDAF server in fog computing architectures. We demonstrated the approach in three use cases: (i) a simple domain name system (DNS) query from the device to a DNS server, (ii) a complex interaction of a blockchain—based IoT device with a blockchain network, and (iii) difference based patching of binary (system) files at the IoT end devices. In a blockchain smart meter use case we effectively enabled decentralized applications (DApp) for devices that without our solution could not participate in a blockchain network. Employing the more efficient binary content encoding, we reduced the periodic traffic from 16 kB/s to ~1.1 kB/s, i.e., 7% of the initial traffic. With additional optimization of the application protocol in the gateway and message filtering, the periodic traffic was reduced to ~1% of the initial traffic, without any tradeoffs in the application’s functionality or security. Using a function of binary difference we managed to reduce the size of the communication traffic to the end device, at least when the binary patch was smaller than the patching file.

Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes.

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

A Group Authentication Protocol on Multilayer Structure for Privacy-Preserving IoT Environment

In the Internet of Things (IoT) systems, large amounts of data are accumulated from anywhere at any time, which may attack individuals' privacy, especially when systems are utilized in medical and everyday environments. With the promise of IoT's proactive systems, the integration of smart things into standard Internet creates several security challenges, because most Internet technologies, communication protocols and sensors are not designed to support IoT. Recent research studies have shown that launching security / privacy attacks against IoT active systems, in particular, Wearable Medical Sensor (WMS) systems, may lead to catastrophic situations and life-threatening conditions. Therefore, security threats and privacy concerns in the IoT area should be actively studied. This causes us in this paper to create a privacy authentication protocol for IoT end-devices on a four-layer structure that does not have the ability to accurately identify the device of request's sender so that some attacks can be minimized. We used the Blakley Sharing scheme to design a key generation and distribution system for secure communications between edge devices and end devices and examined the security properties of the protocol for the five common attacks in the IoT. The results of the experiments show that the proposed authentication protocol by the Blakley method is more efficient with increasing number of instructions in both fog structures and in a without fog structure, which shows a higher flexibility of the Blakley method than the Schemer because of the increasing number of instructions indicating increasing the number of nodes in the network.

Computational Methods for Network-Aware and Network-Agnostic IoT Low Power Wide Area Networks (LPWANs)

In this paper, we tackle the design issue of optimal deployment of low power wide area network (LPWAN) Internet of Things (IoT) gateways (GWs). We classify GW deployment problem into two different categories, i.e., network-aware and network-agnostic. In network-aware GW deployment, precise location of IoT end devices (EDs) is known and thus the design questions are: 1) where to place GWs, i.e., to maximize received signal strength and 2) given received signal strength which GW should the ED be associated with to balance the network load. For, Network-agnostic GW deployment, same questions are answered in the absence of precise knowledge for the locations of EDs. For the network-aware deployment we borrow tools from machine-learning such as ${K}$ -means clustering for determination of optimal GW location. Subsequently, the link assignment problem is presented as an integer linear programming optimization. We prove that the network-agnostic GW deployment principle of placement of GWs at highest altitudes, if applied automatically, may lead to very deteriorated network performance increasing the network operational costs. Consequently, we introduce the concept of network-agnostic GW placement algorithm whereby the location of GWs can be estimated without prior knowledge of specific locations of EDs and we use it as a guiding principle to design spatial algorithm for finding GW locations. We show that spatial algorithm can, in principle, provide effective GW placement suggestions compared to a network-aware method such as ${K}$ -means clustering. We show that using a computational method for GW placement like ${K}$ -means or spatial algorithm, has a potential of creating competitive network performance using just the same number of GWs, thus cutting down the financial costs of the network and increasing its sustainability.

Achieving Privacy-Preserving Subset Aggregation in Fog-Enhanced IoT

Fog-enhanced IoT (Internet of Things) is a fast-growing technology in which many firms and industries are currently investing to develop their own real-time and low latency scenarios. Compared with the traditional IoT, fog-enhanced IoT can offer a higher level of efficiency and stronger security by providing local data pre-processing, filtering, and forwarding mechanisms. However, fog-enhanced IoT faces some security and privacy challenges, since fog nodes are deployed at the network edge and may not be fully trustable. In this paper, we present a new privacy-preserving subset aggregation scheme, called PPSA, in fog-enhanced IoT scenarios, that enables a query user to gain the sum of data from a subset of IoT devices. To identify the subset, inner product similarity of the normalized vectors in the query user side and each IoT device is securely computed. If the inner product is greater than the user’s specified threshold, IoT device’s data will be privately aggregated to form the final response. To successfully launch privacy-preserving subset aggregation in the proposed scheme, we employ the Paillier homomorphic encryption to encrypt user’s attribute vector, similarity threshold, IoT end-devices’ data, as well as the intermediate results. To the best of our knowledge, this work is the first one to address the privacy-preserving subset aggregation in fog-enhanced IoT. We analyze and extensively evaluate the efficiency and security of the proposed PPSA scheme, and the detailed analysis and results indicate that our proposed PPSA scheme can practically achieve privacy-preserving subset aggregation with significant communication and computational cost saving.

Efficient Network Planning for Internet of Things With QoS Constraints

In the Internet of Things (IoT) era, a vast number of (smart) end devices forward their traffic to the Internet, either by direct communication to LTE networks, or by multihop transmissions to a specific gateway. Acquiring both types of communication capabilities for IoT end devices would be unnecessarily costly. Instead, for a cost effective IoT infrastructure, only devices performing as gateways could be fully equipped with such capabilities, while the remaining devices could have simple low cost wireless transceivers, of differing transmission specifications, to forward/relay the traffic toward a gateway. Furthermore, the IoT devices network should comply with specific quality of service (QoS) requirements, specified for each IoT device. In this context, the IoT network planning problem, where we have to select the number of gateways and their locations along with respective transceivers for IoT devices, is key to provide a low cost and QoS aware IoT infrastructure. We formulate the planning problem as an integer linear program (ILP) that minimizes the total cost of the devices deployed in the network, while achieving the mandatory QoS requirements. We also present a heuristic algorithm of lower complexity that was observed to provide solutions near the optimal ones, in scenarios that we tracked optimal solutions with the ILP. A variety of performance evaluation results exhibits the effectiveness of the proposed algorithms in terms of network cost and efficiency.

A Subthreshold Baseband Processor Core Design With Custom Modules and Cells for Passive RFID Tags

Sophisticated subthreshold passive radio frequency identification tag’s baseband processor (BBP) core design for ultralow-power Internet of Things end devices is presented in this paper. Custom logic cells and tailored logic architectures are applied to eliminate timing violations when the operating voltage is much lower than nominal level. For the consideration of limited availability of radio frequency power, power-aware scheme is applied to the key modules, including PIE decoding and command receiving. Furthermore, Galois linear feedback shift register and double-edge-triggered techniques help to improve clock efficiency and reduce the impact of frequency variation in data link portions. Importantly, a novel custom ratioed logic style is adopted in key modules to fundamentally speed up signals’ propagation at ultralow-voltage. The proposed BBP was fabricated in 90-nm CMOS as well as the regular design with the same function. It was also implemented in the tag chip’s fabrication. In measurement the proposed design indicates good robustness and is much more competent for subthreshold operation. It can operate below 0.3 V with power consumption below 130 nW.

A privacy-preserving and accountable authentication protocol for IoT end-devices with weaker identity

In large-scale Internet of Things (IoT) systems, huge volumes of data are collected from anywhere at any time, which may invade people’s privacy, especially when the systems are used in medical or daily living environments. Preserving privacy is an important issue, and higher privacy demands usually tend to require weaker identity. However, previous research has indicated that strong security tends to demand strong identity, especially in authentication processes. Thus, defining a good tradeoff between privacy and security remains a challenging problem. This motivates us to develop a privacy-preserving and accountable authentication protocol for IoT end-devices with weaker identity, which integrates an adapted construction of short group signatures and Shamir’s secret sharing scheme. We analyze the security properties of our protocol in the context of six typical attacks and verify the formal security using the Proverif tool. Experiments using our implementation in MacBook Pro and Intel Edison development platforms show that our authentication protocol is feasible in practice.