Internet Of Things Nodes Research Articles

An Edge-computing flow meter reading recognition algorithm optimized for agricultural IoT network

Groundwater resources in Nebraska, U.S. are closely monitored by 23 Natural Resources Districts (NRDs) located across the state. Growers who use groundwater for irrigation are required to have flow meters installed at wells to monitor their water usage. However, many of these flow meters are still being read and recorded through in-person visits, which can be time-consuming and costly. Although some flow meters in Nebraska are monitored remotely by telemetry-enabled camera systems, yearly telemetry costs are high and making long-term operation financially burdensome. Using less expensive network protocol, such as Internet of Things (IoT), to transmit flow meter readings could enable new monitoring opportunities. However, there are challenges in directly transmitting flow meter images via IoT due to limited bandwidth. Therefore, in this study, we developed an algorithm using object detection deep learning techniques, i.e. You Only Look Once (YOLO) that can be programmed at an IoT node which can recognize readings from images of flow meters onsite before transmitting. The developed algorithm could significantly reduce data size and is essential for flow meter monitoring in an IoT network setting. The developed algorithm achieved 95.35% accuracy when recognizing 1,248 real-world flow meter images obtained at the courtesy of North Platte Natural Resources District (NPNRD) in western Nebraska. The framework and algorithm were also tested in a real-world scenario on a flow meter installed on a linear-move sprinkler irrigation system and showed promising results. By leveraging IoT and deep learning techniques, this research has the potential to revolutionize flow meter monitoring, reducing costs and improving efficiency in the management of groundwater resources in Nebraska, and potentially in other regions as well.

Energy Efficient and Secure Neural Network–based Disease Detection Framework for Mobile Healthcare Network

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.

Sum Rate Optimization for Multiple Access in Multi-FD-UAV-Assisted NOMA-Enabled Backscatter Communication Network

With the rapid development of the Internet of Things (IoT) network, research on low-power and energy-saving devices has attracted extensive attention from both academia and the industry. Although the backscatter devices (BDs) that utilize the environmental power to activate circuits and transmit signals are a promising technology to be deployed as IoT nodes, it is challenging to design a flexible data backhaul scheme for massive BDs. Therefore, in this paper, we consider an unmanned-aerial-vehicle (UAV)-assisted backscatter communication network, where BDs are served by multiple full-duplex (FD) UAVs with the non-orthogonal multiple access (NOMA) schemes and modulate their signals on the downlink signals, which are generated by the UAVs to serve the coexisting regular user equipments (UEs). To maximize the sum rate of the considered system, we construct an optimization problem to optimize the reflection coefficient of BDs, the downlink and the backhaul transmission power, and the trajectory of UAVs jointly. Since the formulated problem is a non-convex optimization problem and is difficult to solve directly, we decouple the original problem into three sub-problems and solve them with the successive convex approximation (SCA) method, thereby addressing the original problem by a block coordinate descent (BCD)-based iterative algorithm. The simulation results show that, compared with the benchmark schemes, the proposed algorithm can obtain the highest system sum rate and utilize limited time-frequency resources more efficiently.

Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability

The internal components of a smart building interact through a compatible fabric and logic. A smart building integrates systems, structure, services, management, and their interrelationships to create a dynamic and cost-efficient environment. Smart buildings reduce the amount of cooling and heating load required to cool and heat spaces, thereby lowering operating costs and energy consumption without sacrificing occupant comfort. Smart structures are an Internet of Things (IoT) concern. The Internet of Things is a global network that virtualizes commonplace objects. The Internet of Things infuses non-technical objects with technology. IoT development has led to the creation of new protocols based on architectures for wireless sensor networks. Energy conservation extends the life and improves the performance of these networks, while overcoming the limitations of IoT node batteries. This research seeks to develop a data transmission model for routing IoT data in smart buildings. Utilization of intelligent object clustering and particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), and fractional chaotic order particle swarm optimization (FCPSO) optimization methods. Using the proposed algorithm to minimize energy consumption in the IoT is possible due to the algorithm’s ability to mitigate the problem by considering the number of parameters that can have a significant impact on performance, which is the goal of many optimization approaches.

Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm

The Internet of Things (IoT) connects numerous sensor nodes and devices, resulting in an increase in the bandwidth and data rates. However, this has led to a surge in data-hungry applications, which consume significant energy at battery-limited IoT nodes, causing rapid battery drainage. As a result, it is imperative to find a reliable solution that reduces the power consumption. A power optimization model utilizing a modified genetic algorithm is proposed to manage power resources efficiently and reduce high power consumption. In this model, each access point computes the optimal power using the modified genetic algorithm until it meets the fitness criteria and assigns it to each cellular user. Additionally, a weight-based user-scheduling algorithm is proposed to enhance network efficiency. This algorithm considers both the distance and received signal strength indicator (RSSI) to select a user for a specific base station. Furthermore, it assigns appropriate weights for the distance, and the RSSI helps increase the spectral efficiency performance. In this paper, the user-scheduling algorithm was assigned equal weights and combined with the power optimization model to analyze the power consumption and spectral efficiency performance metrics. The results demonstrated that the weight-based user-scheduling algorithm performed better and was supported by the optimal allocation of weights using a modified genetic algorithm. The outcome proved that the optimal allocation of transmission power for users reduced the cellular users’ power consumption and improved the spectral efficiency.

Optimal Cluster-Based Topology and Deep LSTM-Based Prediction Method for Data Reduction in IoT

In the Wireless Sensor Network (WSN), the data prediction approach is needed to attain data effectively by diminishing node energy consumption. Hence, in this research, Water cycle Fire Fly Optimization and Deep Long Short-Term Memory (WCFO+ Deep LSTM) approach is employed for aggregation and reduction of data. The processes involved in the developed method are node simulation, cluster-based topology construction, routing tree construction, and data aggregation. Initially, IoT nodes are simulated in the network environment. The cluster-based topology construction is made using the WCFO algorithm. The WCFO is developed by the integration of the Firefly Optimization Algorithm (FOA) and Water cycle algorithm (WCA). The cluster-based topology is constructed by considering the objective function that includes the parameters, including distance, delay, link quality, and energy. After that, the routing process is performed using developed WCFO approach for constructing a routing tree and estimating the optimal path. Finally, the Deep LSTM is trained by the proposed WCFO algorithm, which is utilized for executing data reduction and data aggregation process with minimum energy consumption. The devised WCFO+ Deep LSTM approach achieved better performance in terms of prediction error, delay, energy, and Packet delivery ratio (PDR) with values 0.029, 0.001[Formula: see text]s, 0.161[Formula: see text]J and 99.054%, respectively.

Intelligent multichannel cross-layer framework for enhanced energy-efficiency in 6G-IoT wireless networks

The sixth-generation (6G) communication technology is expected to enable a fully-connected world with enhanced wireless connectivity, accessibility, reliability, and performance. 6G systems will be critical in addressing the massive-connectivity requirements for Internet-of-Things (IoT) networks. Cognitive radio (CR) technology is considered a promising solution in 6G networks that can provide tremendous spectrum opportunities to support large-scale IoT deployments. Due to the IoT’s energy-constrained and massively connected nature, existing CR-based communication protocols cannot be directly implemented in 6G-IoT networks. This is because they are usually designed to enhance spectrum-utilization in networks with finite users. Therefore, energy-efficient protocols are required to support the massive-connectivity requirement for energy-constrained IoT nodes. Non-persistent Carrier-Sense-Multiple-Access (CSMA)-based protocols are recognized as energy-effective spectrum-access paradigms for networks with massive users. This paper proposes a novel energy-efficient multi-channel cross-layer MAC framework that utilizes a non-persistent CSMA protocol in CR-enabled 6G-IoT networks. Based on a joint adaptation of the physical and MAC layer parameters, our framework aims to improve IoT networks’ energy efficiency (EE). Specifically, we formulate and solve the per-bit EE maximization problem subject to several design constraints. Numerical results show that our multi-channel cross-layer framework enhances network EE by up to 50% compared to a reference single-channel cross-layer design.

Energy-Efficient Multiprocessor-Based Computation and Communication Resource Allocation in Two-Tier Federated Learning Networks

In conventional federated learning (FL), multiple edge devices holding local data jointly train a machine learning model by communicating learning updates with a centralized aggregator without exchanging their data samples. Owing to the communication and computation bottleneck at the centralized aggregator and inaccurate learning model caused by the non-IID data, we here consider a two-tier FL network, in which IoT nodes are the core clients that hold data, the model aggregators at the middle tier are the low altitude aerial platforms (UAVs), and the model aggregator at the top-most layer is the high altitude aerial platform (UAV with relatively high altitude). Under the assumption that each IoT node has parallel computing ability, we study the energy-efficient computation and communication resource allocation in such a network within some time budget. Upon formulating the problem as an optimization problem, we solve the computation and communication resource allocation problems as the separate subproblems within a time frame, and then propose an iterative algorithm to solve the entire problem jointly. More specifically, We solve both the energyefficient computation and communication resource allocation subproblems using the dual decomposition technique, and then apply a bisection search-based recursive technique to solve the entire energy efficiency problem jointly. Moreover, we propose offline and online client scheduling schemes that not only select the optimal edge nodes for association but also assign workload to each client based on the data quality and workload constraint. With real data, extensive simulations are conducted to verify the effectiveness of the proposed resource allocation scheme. The results further reveal that the learning performance not only is dependent on the computation and communication energy consumption of the FL process but also the model divergence weight owing to the non-IID data at client IoT nodes.

Robustly Operating: Passive Near-Field Communication Systems in Metal Environments

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Internet of Things</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoT</i> ) refers to a structure that links everyday objects and the Internet. To enable the IoT, a massive deployment of wireless IoT nodes is required; predictions indicate that trillions of these nodes will be needed. This trend raises sustainability issues on environmental, economic, and societal levels. Concerning environmental and economic problems, a significant design challenge is to create wireless IoT nodes that operate at ultralow power to avoid the ecotoxicity of batteries and the prohibitive maintenance costs of battery replacement <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . Another design challenge is to reach high levels of integration based on low-cost CMOS technologies <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> to limit the carbon footprint and costs associated with the nodes’ production and end of life. Concerning societal issues, a significant design challenge is to guarantee the security and privacy of data in IoT nodes.

Allocation of power in NOMA based 6G-enabled internet of things using multi-objective based genetic algorithm

Abstract Sixth generation (6G)-enabled internet of things (IoT) requires significant spectrum resources to deliver spectrum availability for massive IoT’s nodes. But the existing orthogonal multiple access limits the full utilization of limited spectrum resources. The non-orthogonal multiple access (NOMA) exploits the potential of power domain to improve the connectivity for 6G-enabled IoT. An efficient quality of service (QoS) aware power allocation approach is required to enhance the spectral efficiency and energy of NOMA based 6G-enabled IoT nodes. The multi-objective genetic algorithm (MOGA) is used to resolve the non-convex problem by considering the successive interference cancellation (SIC), QoS, and transmission power. Extensive experiments are drawn by using the Monte Carlo simulation to evaluate the significant improvement of the proposed model. Experimental results indicate that the proposed power allocation model provides good performance of the NOMA based IoT network.

The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods

Recently, the widespread adoption of the Internet of Things (IoT) model has led to the development of intelligent and sustainable industries that support the economic security of modern societies. These industries can offer their participants a higher standard of living and working services via digitalization. The IoT also includes ubiquitous technology for extracting context information to deliver valuable services to customers. With the growth of connected things, the related designs often suffer from high latency and network overheads, resulting in unresponsiveness. The continuous transmission of enormous amounts of sensor data from IoT nodes is problematic because IoT-based sensor nodes are highly energy-constrained. Recently, the research community in the field of IoT and digitalization has labored to build efficient platforms using machine learning (ML) algorithms. ML models that run directly on edge devices are intensely interesting in the context of IoT applications. The use of intelligence ML algorithms in the IoT can automate training, learning, and problem-solving while enabling decision-making based on past data. Therefore, the primary aim of this research is to provide a systematic procedure to review the state-of-the-art on this scope and offer a roadmap for future studies; thus, a structure is introduced for industry sustainability, based on ML methods. The publications were reviewed using a systematic approach that divided the papers into four categories: reinforcement learning, semi-supervised learning, unsupervised learning, and supervised learning. The results showed that ML models could manage IoT-enabled industries efficiently and provide better results compared to other models, with significant differences in learning time and performance. The study findings are considered from a variety of angles concerning the industrial sector’s capacity management of the new elements of Industry 4.0 by combining the industry IoT and ML. Additionally, unique and relevant instructions are provided for the designers of expert intelligent production systems in industrial domains.

Grace: Low-cost time-synchronized GPIO tracing for IoT testbeds

Testbeds have become a vital tool for evaluating and benchmarking applications and algorithms in the Internet of Things (IoT). IoT testbeds commonly consist of low-power IoT devices augmented with observer nodes providing control, debugging, logging, and often also power-profiling capabilities. Today, the research community operates numerous testbeds, sometimes with hundreds of IoT nodes, to allow for detailed and large-scale evaluation. Most testbeds, however, lack opportunities for tracing distributed program execution with high accuracy in time, for example, via minimally invasive, distributed GPIO tracing. And the ones that do, like Flocklab, are built from custom hardware, which is often too complex, inflexible, or expensive to use for other research groups.This paper closes this gap and introduces Grace, a low-cost, retrofittable, distributed, and time-synchronized GPIO tracing system built from off-the-shelf components, costing less than €20 per node. Grace extends observer nodes in a testbed with (1) time-synchronization via wireless sub-GHz transceivers and (2) logic analyzers for GPIO tracing and logging, enabling time-synchronized GPIO tracing at a frequency of up to 8 MHz. We deploy Grace in a testbed and evaluate it, showing that it achieves an average time synchronization error between nodes of 1.53 μs using a single time source, and 15.3 μs between nodes using different time sources, sufficient for most IoT applications.

Network-aware RF-energy harvesting for designing energy efficient IoT networks

Energy Harvesting plays a crucial role in improving the network lifetime of an IoT application. Radio Frequency (RF) energy is one of the most prominent ambient energy sources available that can be used for harvesting energy. Moreover, a number of dedicated RF-energy transmitters is required for sufficient energy transfer to the power deficit IoT nodes. However, the appropriate positioning of these RF-energy transmitters in the network is a challenging issue. In this context, a network-aware RF-energy transmitter positioning scheme is proposed in this work. This RF-energy transmitter deployment scheme considers different network performance parameters such as energy-hole information, node-connectivity information, and data routing information for RF-energy transmitter placement. Apart from that, the amount of RF-energy harvested is constrained to the RF-energy carried by the propagating RF-waves. Moreover, the wireless medium uncertainties result in severe attenuation of the propagating RF-waves. In this regard, a robust hybrid RF-wave propagation model is also proposed. The proposed model amalgamates different wave propagation mechanisms for signal strength prediction in the propagating RF-waves. This helps in improving the modelling accuracy of the RF-waves. The proposed model also predicts the amount of residual RF-energy available in the propagating RF-wave for harvesting. This model is also validated against the practical datasets generated from experiments. The proposed transmitter positioning scheme, along with the hybrid RF-wave propagation model, overcome the energy hole issue in the IoT networks and improves energy efficiency. Extensive simulations are conducted under various network scenarios to evaluate the energy harvesting performance of the proposed methods. It is observed that the proposed network-aware RF-energy harvesting scheme improves the network performance in terms of energy harvesting, network coverage, and the lifetime of IoT network.

Lightweight multi-hop routing protocol for resource optimisation in edge computing networks

Data transmission over power line communication (PLC) infrastructure will proliferate lightweight Internet of Things (IoT) nodes in 5G and 6G networks. Consequently, a corresponding lightweight multi-hop routing protocol (LMRP) with reduced path loss and computational complexities will be required at the edges of PLC networks to connect the cloud sinks. Using a multi-layered system architecture, we present an LMRP for optimal routing and highlight the components of a smart PLC network comprising edge power pool orchestration, edge layer service provisioning, fog latency layer, and cloud resilient backbone. The LMRP reduces path loss and node failure states at the edge while optimising throughputs, minimum cost flow, and signal stability. A multi-hop deterministic testbed is designed and applied in three different locations to estimate path loss leveraging TelosB IoT node, Raspberry Pi (RPI) with NesC, and Java scripted logger application. Three different testbeds of varying path loss characteristics at the Federal University of Technology Owerri (FUTO) are used while the analysis was completed at Manchester Metropolitan University engineering LAB. The result of PL mitigation in Location 1 (sonic FUTO) shows 33.89%, 33.25%, and 32.77% with genetic algorithm (GA), particle swarm optimisation (PSO), and the proposed LMRP, respectively. In Location 2 (Old SEET Complex, FUTO), the PL obtained are 33.81%, 33.57%, and 32.62%, while Location 3 (New SEET Complex, FUTO) yields 33.65%, 33.41%, and 32.74% in PL mitigation for GA, PSO, and LMRP, respectively. Despite improved PL mitigation, the results also show that the proposed scheme offers a lightweight routing performance of at least 76.30% compared to similar schemes.

Joint resource scheduling and trajectory design for multi-UAV-assisted uplink NOMA networks

The combination of unmanned aerial vehicle (UAV) communication and non-orthogonal multiple access (NOMA) is of great significance to support massive connections in the Internet of things (IoT). This paper studies a multi-UAV-assisted communication network, where the UAVs are employed as aerial access points to receive data from IoT nodes with NOMA adopted for uplink transmission. The aim is to maximize the average sum rate of the network, with the requirements of the individual upload of each node. To handle the node clustering problem, the spectral clustering and maximum weight matching algorithm are first integrated to divide the IoT nodes. Then, the time-varying NOMA user grouping scheme is implemented accordingly. Furthermore, by considering the trajectory decomposition and successive hover-and-fly structure, the complex UAV trajectory problem is transformed into a linear programming and the optimal time-continuous trajectory is achieved. Extensive simulations validate the effectiveness of the proposed algorithms for the UAV-assisted NOMA network.

Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach

Establishing service-driven IoT systems that are reliable, efficient, and stable requires building trusted IoT environments to reduce catastrophic and unforeseen damages. Hence, building trusted IoT environments is of great importance. However, we cannot assume that every node in wide-area network is aware of every other node, nor can we assume that all nodes are trustworthy and honest. As a result, prior to any collaboration, we need to develop a trust model that can evolve and establish trust relationships between nodes. Our proposed trust model uses subjective logic as a default artificial reasoning over uncertain propositions to collect recommendations from other nodes in the IoT environment. It also manages and maintains existing trust relationships established during direct communications. Furthermore, it resists dishonest nodes that provide inaccurate ratings for malicious reasons. Unlike existing trust models, our trust model is scalable as it leverages a Fog-based hierarchy architecture which allows IoT nodes to report/request the trust values of other nodes. We conducted extensive performance studies, and confirm the efficiency of our proposed trust model. The results show that at an early stage of the simulation time (i.e., within the first 2% of the number of transactions), our trust model accurately captures and anticipates the behavior of nodes. Results further demonstrate that our proposed trust model isolates untrustworthy behavior within the same FCD and prevents untrustworthy nodes from degrading trustworthy nodes’ reputations.

Home Chimney Pinwheels (HCP) as Steh and Remote Monitoring for Smart Building IoT and WSN Applications

Smart, and ultra-low energy consuming Internet of Things (IoTs), wireless sensor networks (WSN), and autonomous devices are being deployed to smart buildings and cities, which require continuous power supply, whereas battery usage has accompanying environmental problems, coupled with additional maintenance cost. We present Home Chimney Pinwheels (HCP) as the Smart Turbine Energy Harvester (STEH) for wind; and Cloud-based remote monitoring of its output data. The HCP commonly serves as an external cap to home chimney exhaust outlets; they have very low inertia to wind; and are available on the rooftops of some buildings. Here, an electromagnetic converter adapted from a brushless DC motor was mechanically fastened to the circular base of an 18-blade HCP. In simulated wind, and rooftop experiments, an output voltage of 0.3 V to 16 V was realised for a wind speed between 0.6 to 16 km/h. This is sufficient to operate low-power IoT devices deployed around a smart city. The harvester was connected to a power management unit and its output data was remotely monitored via the IoT analytic Cloud platform "ThingSpeak" by means of LoRa transceivers, serving as sensors; while also obtaining supply from the harvester. The HCP can be a battery-less "stand-alone" low-cost STEH, with no grid connection, and can be installed as attachments to IoT or wireless sensors nodes in smart buildings and cities.

Reliable cluster based data aggregation scheme for IoT network using hybrid deep learning techniques

BackgroundSeveral IoT nodes are deployed in the monitoring environment to ensure reliability. In both sensor and sink nodes, the same data is sensed and forwarded. While redundant data maintains reliability, sink nodes waste energy processing the redundant data. ObjectiveIn order to upholdthe compromiseamong energy ingesting and reliability, necessary to eliminate the redundancies in sensed data up to an appropriate level. Data aggregation algorithms currently assign time slots based on data sensing period and program rate, disregarding packet loss and latency. MethodologyIn this paper, we suggest a cluster based reliable data aggregation (CRDA) scheme for IoTnetwork which ensures data collection and aggregation in energy efficient manner and transfer to another end very effectively. We first introduce a monarch and sine-cosine (MSC) algorithm to form clusters by grouping the IoT sensors which ensures the effective data transferring. In data aggregation phase, we utilize the multiple design metrics to compute the trust degree of each IoT sensors and design an improved sunflower optimization (ISFO) algorithm to optimize the design constraints. The highest trust degree owned swelling is act as cluster head (CH) of the cluster which ensures data aggregation. A reformative optimal–learning-based deep neural network (ROL-DNN) is then used to compute routes between IoT sensors which ensures reliabledata aggregation and transferring. Resultsand analysis: Finally, we validate our proposed routing with the different simulation scenario and their results are compared with the existing routing protocols to prove the effectiveness.

Experimental analysis of RSSI-based localization algorithms with NLOS pre-mitigation for IoT applications

In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).

LIGHT WEIGHT SECURITY ALGORITHM FOR LOW POWER IOT DEVICE

Many IOT devices(mainly networks of IOT devices or networks that are deployed to monitor calamity situations) are deployed in an arbitrary and unplanned fashion. For any sensor in such a network can end up being adjacent node to any other IOT node in the network. To Establish a secure communication between every pair of adjacent IOTs node in such a network, each sensor node x in the network needs to store n -1 number of symmetric keys that sensor node x shares with all the other sensor nodes, where n is the number of sensor nodes in the present network. This memory storage requirement of the keying protocol is various, especially when n is large and the available storage in each sensor node is modest. Previous efforts to redesign this keying protocol and reduce the number of keys to be stored in each sensor node have produced protocols that are vulnerable to impersonation, eavesdropping, and collusion attacks. In this paper, we present a fully secure protocol.