This work integrates NovaGenesis (NG), a clean-slate IoT architecture, with LoRa technology within low-power wide-area networks (LPWAN), extending previous efforts on NG connectivity with Wi-Fi. The research aims to update the embedded version of NG and develop devices for seamless LoRa and Wi-Fi IoT operation. It evaluates NG’s performance on LoRa and Wi-Fi, focusing on throughput, delay, and packet loss. Despite LPWAN limitations, the results show that the NG deployment is feasible, validating its self-organizing IoT life cycle to maintain service continuity between an ESP-32 and a data client. Performance meets the needs of IoT applications in agribusiness, logistics, and smart monitoring. In addition, a 24-hour environmental monitoring experiment was conducted in Santa Rita do Sapucaí(SRS), Minas Gerais, Brazil, where a commercial weather station was modified to integrate NG, allowing accurate collection of temperature, humidity, atmospheric pressure, wind conditions, solar radiation and UV index. The results met expected diurnal patterns in SRS, proving the reliability and precision of the sensors and communication infrastructure. This solution overcomes common IETF IoT stack limitations in devices naming, information provenance, entities identification, programmability via digital twins, programmability, services and devices self-organization, and trust formation, offering a robust platform for varied IoT scenarios in LPWAN environments. These are the key benefits of applying NovaGenesis for LoRa and Wi-Fi-based environmental monitoring.

Ensuring safe UAV navigation in smart cities requires low latency and high-bandwidth communication, which calls for the adoption of resilient 5G and beyond network infrastructures. In this paper, we address UAV route planning and battery consumption in a scenario involving multiple 5G service providers, within the broader context of smart city and IoT applications. In this study, we evaluate three path search algorithms for route optimization under beyond visual line of sight (BVLOS) navigation conditions. The algorithms are assessed in terms of traveled distance, energy consumption, and the diversity of 5G mobile service providers used along the route. In our methodology, signal coverage data from 5G networks is derived through radio propagation simulations based on real-world deployment information of gNBs, as provided by the national telecommunications regulatory authority. The simulation framework also integrates detailed 3D modeling of the urban environment to more accurately represent signal behavior under realistic conditions. For each optimization algorithm and 5G service provider, we conducted simulations over 5,000 randomly generated UAV routes. The evaluation includes a detailed analysis of the impact of urban obstacles on route selection, considering predefined flight altitudes and battery energy constraints. This approach allows for a comprehensive assessment of each algorithm’s performance under realistic navigation and communication conditions.

The integration of AI with IoT heralds the era of AIoT (Artificial Intelligence of Things). It represents a transformative approach in technology and opens up a new opportunity for deploying machine learning models in embed-ded devices that face resource constraints and operate on the edge of networks. Central to this study is the implementation of computational vision techniques for digit recognition, evaluating various machine learning models, particularly in the context of smart metering. The selected models were converted from GPU-equipped workstations to ESP32-S3 microcontroller-based low-end devices. Through a series of experiments using ESP32-S3 development kits, the MNIST database, and TensorFlow Lite, we explore the effectiveness of these models in smart metering applications, focusing on accuracy, inference times, and the challenges in model conversion. The findings demonstrate the feasibility of executing machine learning inferences on low-end devices with high accuracy in smart meter contexts. However, challenges such as model size limitations, processing speed, conversion difficulties, and potential accuracy loss were noted. Not all models were viable for conversion to TensorFlow Lite. Simpler models like LeNet5 emerged as effective solutions for smart metering applications, balancing size, accuracy, and latency. This work offers practical insights for researchers and engineers looking to implement machine learning in AIoT and smart metering environments, highlighting the trade-offs and considerations for effective deployment.

Plant diseases are one of the factors that compro-mise food production goals. Tomatoes are one of the world's most consumed vegetables and are widely affected by various diseases. Tomato cultivation in greenhouses enables continuous production. In this context, this research focuses on identifying diseases in greenhouse tomato cultivation scenarios. For this study, new datasets were created with two image sizes: the Tomato Leaf Image Dataset (TLID) with image sizes of 256 x 256 pixels and 15,256 images, and the Patch Tomato Leaf Image Dataset (PTLID) with patch sizes of 32 x 32 pixels and 227,218 images. Both datasets comprise seven classes, including four types of diseases, two combinations of diseases on the same leaf, and the healthy leaf. Machine Learning techniques have been widely used to identify plant diseases. This work presents two machine learning methods tested with both datasets. In the proposed models, three convolutional neural networks were combined: a customized CNN, VGG19, and Resnet50, along with two voting classification methods using Hard and Soft de-cisions. The evaluation conducted on the datasets demonstrated that using patches significantly improves results, achieving an accuracy of 90.48%. This technique enables the identification of the disease stage.

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.

Current machine learning techniques for indoor localization of wireless devices assume a single wireless propagation loss setting, making them unfeasible for reliable production deployment. This paper proposes a new indoor localization technique designed for variable propagation loss environments based on deep autoencoder and recurrent neural network (RNN), implemented threefold. This paper proposes a new indoor localization technique designed for variable loss propagation environments based on deep autoencoder and recurrent neural network (RNN), implemented in three stages. First, we extract statistical feature values from collected RSSI. Second, a deep autoencoder is used to remove wireless propagation noises introduced by variable fading settings. Third, an RNN performs the localization task taking into account previous sensor measurements. Experiments performed in 3 simulated testbeds with distinct propagation loss settings have shown that current approaches decrease localization accuracy by up to 30% when a different propagation loss is faced. In addition, our proposed model improved localization accuracy by up to 25.8% regardless of the current environment propagation loss.

A publish-subscribe middleware is an application-independent infrastructure that supports implementing event-based systems, in which event generators (i. e., publishers) notify events to the infrastructure and event consumers (i. e., subscribers) subscribe to the infrastructure for receiving relevant notifications. Subscribers are not directly addressable by the publisher but are indirectly addressed according to the content of the events. This anonymity ensures that publishers and subscribers exchange information without knowing each other, which allows middleware to expand to a massive size on the scale of the Internet and Smart Cities. In this way, this article presents a study on publish-subscribe middlewares for the selective sending of events in Smart Cities. This selective notification delivery is important for some applications such as emergency services in Smart Cities, where only a subset of subscribers should receive the event and handle it as soon as possible.

Environmental sensor networks (ESNs) is an emerging application specific wireless sensor network (WSNs). ESNs support the activities required to characterize and monitor the quality of the environment, allowing the connection of a wide variety of measuring devices. Due to the specifics of environmental monitoring applications, ESNs should meet a set of requirements, in particular, system integration, content routing and reliability. In this context, geographic protocols appear as an important option in which packets are routed to a destination based not on its identity or address but on its geographic position. Geographic protocols are particularly useful for ESN because they allow queries to be routed to regions instead of specific nodes identified by their addresses. In this paper we propose a geographic protocol capable to forward interest queries across multiple paths, allowing the discovery and maintenance of communication paths formed by good quality transmission links. We demonstrated that the proposed protocol is capable to find good quality paths to in ESNs, presenting good performance even in scenarios where the network is submitted to severe variations in the quality of the wireless links.

Currently, there is a wide variety of low-cost radio technologies being used to enable wireless communication in some important emerging applications such as smart grids, smart cities and the Internet of Things (IoT). However, easy access to these new radio technologies brings a security problem due to the fact that it is very easy for a malicious user to perform passive wireless signal scanning on these networks and use this information to launch identity-based attacks. In this paper, we propose a new machine learning based strategy to detect spoofing attacks in wireless sensor networks (WSNs). Based on detailed analytical models for the mobile radio channel, the proposed algorithm combines two classifiers to process and analyze the instant samples of received signal strength to detect attacks. The algorithm is optimized for scenarios where the legitimate node and the attacking node are at the same distance or at a very close distance from each other in relation to the landmark, what is the worst case scenario. The results show that the proposed strategy improves the performance of the attack detection in about 10% regarding a similar approach in the literature.

The use of smart objects in an urban context will be offers services that will help the cities. Thereby, it is possible to implement the Smart Cities concept. Among the services that could be offered is an emergency service calls (e.g., police and firefighters). In an emergency situation, the most appropriate mobile units may be required. Therefore, a middleware can be used to support the activation of the most suitable mobile units. The bigger question is which the vantage of choice the most suitable mobile unit compared to the choice random. In this way this paper presents a new event model and a middleware called Event to Best Subscribers (E2BS) aim to answer this question through two use cases.

We study the cross-layer energy efficiency of cooperative MIMO networks. At the physical layer, the transmit power determines the outage probability and the number of contending nodes per unit area. At the medium access control (MAC) layer, accessing the channel takes additional time and energy, which impacts the throughput and the energy efficiency. Then, we propose a new cooperative MAC protocol based on IEEE 802.11 in order to coordinate cooperation and to improve the energy efficiency. Moreover, we focus on slow fading scenarios and we analyze the performance of antenna selection (AS), singular value decomposition (SVD) diversity beamforming, and spatial multiplexing (SM) transmission schemes. Results show that SVD and SM have smaller delay and, consequently, larger throughput; however, AS is more energy efficient.

This paper presents a mobility-aware channel allocation strategy for clustered ad hoc network. Our main novelty is to consider the mobility associated with the number of times that the channel with larger spectral distance is allocated to guide the channel allocation process, while quickly responding to changes in the network topology. In our performance evaluation, we use a realistic mobility model based on user behavior and we consider the evaluation metrics of throughput, packet delivery rate, end-to-end delay, overhead, and spectral distance. Obtained results show that our strategy presents lower overhead when compared with TABU algorithm, higher throughput, and lower end-to-end delay when compared with RANDOM and LD algorithms.

The Advanced Metering Infrastructure (AMI) is a key component of the Smart Grid architecture. The Neighborhood Area Network (NAN) is the portion of the AMI that enables two-way communication between electric, gas and water meters and City Utilities. Many companies are currently deploying wireless NAN architectures based on the IEEE 802.15.4g technology while the Routing Protocol for Low-Power and Lossy Networks (RPL) has been considered as the mesh routing protocol. In this paper, we investigate the dynamics of RPL for routing path maintenance in the presence of jamming attacks. A precise interference model is implemented and tested under a fully compliant RPL draft implementation. The quality of the survivors routing paths in terms of ETX metric is investigated for different density of gateways. The results provide insights for an efficient NAN design in order to minimize the impacts of jamming attacks in the RPL performance.

The Advanced Metering Infrastructure (AMI) is a fundamental component of the Smart Grid architecture. The AMI consists of a collection of Neighborhood Area Networks (NANs), which interconnects the smart meters to the utility company. In this paper, we address two important performance metrics regarding the NAN design, the topology's resilience and the network capacity. We propose an analysis methodology in order to determine the appropriate transmission power and the required number of gateways for wireless-enabled mesh-connected architectures. We employ a graph-theoretic approach for the analysis. Furthermore, we assume wireless NANs based on the new IEEE 802.15.4g standard. A planning tool has been implemented using software Mathematica in order to automate our approach. Simulation results show interesting tradeoffs between the performance metrics and the network design parameters, thus providing useful insights for the NAN designer.

Many applications of wireless sensor networks require that nodes, besides monitoring a given phenomenon, must be able to detect and communicate asynchronous events (e.g. alarms), implying that they have to often listen to the medium in idle mode, which is inherently energy wasteful. In such a scenario time synchronization is crucial to efficiently operate in duty-cycles and minimize energy consumption. In this work we assess the impact of the trade-off between spending energy with more frequent synchronizations and in return saving it by reducing the idle listening window necessary for the desired reliability of the communication. The optimal frequency of time synchronizations is obtained analytically and corroborated by numerical results, showing that several times less overall energy may be spent with a finer synchronization when compared with maintaining the minimum clock precision required by the phenomenon being monitored, greatly extending the life-span of the network.

Retransmissions due to decoding errors have a big impact on the energy budget of low-power wireless communication devices, which can be reduced by using hybrid automatic repeat request (HARQ) techniques. Nevertheless, this reduction comes at the cost of extra energy consumption introduced by the added computational load. No complete analysis of the tradeoff between retransmissions reduction and baseband consumption of low-power communications over fading channels has been reported so far. In this paper, we study the energy efficiency achievable by HARQ schemes when the code rate of the error-correcting code is optimized. For this purpose, we develop an energy consumption model that focuses on simple HARQ (S-HARQ) and Chase combining (HARQ-CC) transmissions, which are studied under fast-fading and block-fading scenarios with Nakagami-m fading. The retransmission statistics are analyzed, and expressions for the expected number of transmission trials are derived. Using this framework, it is shown that transmission schemes with high diversity gain are the most efficient choice for long range transmissions, which in our case correspond to HARQ-CC and codes with low code rate. On the other hand, schemes with good multiplexing capabilities are optimal for short link distances, which in our analysis correspond to S-HARQ and high code rates. It is also shown that HARQ-CC can effectively extend the transmission range of a low-power communication device.

Saving energy is vital in real-world underwater acoustic networks, since a lot of energy is spent to transmit acoustic waves and battery replacement is highly undesirable. In this paper, we study how the energy consumption of underwater acoustic communications can be reduced in order to extend the lifetime of underwater sensor nodes. We analyze the effect of selecting the best frequency within a given operating range for various link distances. We also consider the use of error correcting codes, and optimize the code rate and the signal to noise ratio for each link distance and frequency. Our results show that energy consumption can be greatly reduced by optimizing code rate. In particular, it is shown that uncoded transmissions can be more energy efficient for a considerable range of link distances than fixed rate convolutional codes which are popular in commercial devices. Results also show that the optimal number of average retransmissions is very small (less than one) for all studied link distances. This is an encouraging result, as acoustic communication channels impose much longer delays than RF channels and therefore schemes which require several retransmissions impose unfeasible delay constrains.

We propose a method for planning large mesh-based WSN with multiple sinks. The relationship between the WSN capacity, sensor power transmission and the outage probability of the path between sensor nodes and sinks is very complex. In order to reduce the problem complexity, we use a multistage approach, where sensor node clustering, sink position selection, sensor node power and channel assignment are solved as independent problems. The multistage approach is used in accordance to two different strategies, named Geometry-Based Clustering (GBC) and Path-Based Clustering (PBC). In the first strategy, the clustering of sensor nodes and the sink selection is based exclusively on the sensor nodes geometric disposition. The second strategy solves the same problem by determining the sink positions by a search method that optimizes the WSN capacity under outage probability constraints.

In this paper we analyze the energy efficiency of multi-hop communication in wireless sensor networks when packet transmissions between adjacent nodes employ Hybrid ARQ (HARQ) with Chase combining (CC) technique under Rayleigh block-fading channels. The energy analysis can be constrained by either a target outage probability or end-to-end delay. Closed form expressions for the end-to-end outage probability and energy consumption are derived. Results show how to determine the optimal number of retransmissions of the HARQ-CC scheme for a given number of relays between the source and destination nodes, in order to achieve the target constraints. The impact of the path loss environment conditions on the overall energy consumption is also investigated. Finally, we analyze the energy consumption assuming a visual sensor network application with multiple cameras and different outage and delay constraints.

The performance and complexity of turbo decoding using rate k/n constituent codes are investigated. The conventional, minimal and sectionalized trellis modules of the constituent convolutional codes are utilized. The performance metric is the bit error rate (BER), while complexity is analyzed based on the number of multiplications, summations and comparisons required by the max-log-MAP decoding algorithm. Our results show that the performance depends on how the systematic bits are grouped in a trellis module. The best performance is achieved when the k systematic bits are grouped together in the same section of the module, so that the log-likelihood ratio (LLR) of the k-bit vector is calculated at once. This is a characteristic of the conventional trellis module and of some of the sectionalizations of the minimal trellis module. Moreover, we show that it is possible to considerably reduce the decoding complexity with respect to the conventional trellis if a particular sectionalization of the minimal trellis module is utilized. In some cases, this sectionalization is found within the best performing group, while in some other cases a small performance loss can be traded off for a large complexity reduction.

This paper investigates the use of ensembles of artificial neural networks in predicting long-term Internet traffic. It discusses a method for collecting traffic information based on flows, obtained with the NetFlow protocol, to build the time series. It also proposes four traffic forecasting models based on ensembles of TLFNs (Time-Lagged FeedFoward Networks), each one differing from the others by the way it reads the training data and by the number of artificial neural networks used in the forecasts. The proposed prediction models are confronted with the classic method of Holt-Winters, by comparing the mean absolute percentage error (MAPE) of the forecasts. It is concluded that the proposed models perform well, and can be considered a good option for planning network links that transport Internet traffic.

An important design goal for wireless local area networks (WLANs) is the performance. It is well known that the major issue challenge to the performance in WLANs is the wireless interference. This leads to an ambience that depends on the channel assignments among interfering access points (AP). Due to the limited number of non-overlapping channels, severe interference scenarios may arise if an unappropriate spectrum planning is considered. In our study we considered WLANs scenarios where APs belong to different administrative domains, a common situation in dense urban deployments. In such environment the use of centralized algorithms is not appropriate and the already proposed distributed methods do no guarantee optimal channel assignment. In this paper, we formalize the channel allocation as a distributed constraint optimization problem (DCOP) and propose a new cooperative channel allocation strategy using the distributed pseudo tree-optimization procedure (DPOP). The adjacent channel interference is analytically formulated for DPOP. The simulation results show that the proposed strategy always achieves the optimal solution and is scalable in terms of the number of exchanged messages.

In this paper we propose an energy efficient image transmission strategy for wireless sensor networks, which combines wavelet-based image decomposition and cooperative communication. In the proposed scheme we use selective decode-and-forward (SDF) cooperation, so that a relay node collaborates with the source by forwarding only a lower-resolution version of the original image, obtained via discrete wavelet transform (DWT). We show that the proposed SDF-DWT strategy is more energy efficient than non-cooperative single-hop and multi-hop, also outperforming the regular SDF scheme. In addition, we show that our method can achieve the energy efficiency of incremental DF (IDF), without the need of a feedback channel.

The performance of a wireless local area network depends on the channel assignments among neighboring access points (APs). The limited number of non-overlapping channels may lead to severe interference scenarios if no appropriated spectrum planning is employed. Additionally, in dense urban areas it is usual to find wireless networks scenarios with interfering APs belonging to different administrative domains. In such cases the use of centralized algorithms is not feasible and the already proposed distributed methods do not guarantee optimal channel assignment. In this paper, we formalize the channel allocation as a distributed constraint optimization problem and propose a new cooperative channel allocation strategy using the distributed pseudotree-optimization procedure (DPOP). The adjacent channel interference is analytically formulated for DPOP. The simulation results show that the proposed strategy always achieves the optimal solution and is scalable in terms of the number of exchanged messages.

Truncated Type-I and Type-II HARQ schemes are compared, in terms of throughput, when incremental decode and forward relaying is used. The comparison is based on the outage analysis of both schemes. Two different scenarios are considered: ad-hoc relaying, where all users are at ground level, and infra-structured relaying, where the relay and the destination antennas are at a higher height than the user. Moreover, we also consider the effect of rate adaptation. Results show that Type-II (incremental redundancy) only significantly outperforms Type-I schemes (selection and Chase combining) in the case of ad-hoc relaying without rate adaptation, and in the low SNR region.

We propose a partial retransmission (PR) scheme to be used in block fading channels. The proposed method sends only a fraction of the original transmitted codeword at each retransmission, and makes use of an appropriate power allocation and of symbol by symbol combining at the receiver. The performance is investigated by means of the outage probability and the throughput. The proposed PR scheme is shown to outperform both the incremental redundancy (IR) and Chase combining (CC) HARQ methods, for some signal to noise ratio values and for the scenarios considered in this paper.

In this paper we extend the overlay cognitive radio model by introducing multiple secondaries. The new model allows for multiple concurrent transmissions in a wireless network. We apply the proposed scheme for the communication between nodes in a wireless mesh network. Numerical results show that considerable capacity gains are achieved when using the proposed overlay cognitive radio model with multiple secondaries.

We propose a hybrid ARQ scheme using recursive convolutional codes, the turbo principle and combining diversity. While spending the same energy for transmission, the proposed scheme presents a better throughput for most of the SNR range and a smaller decoding complexity than another well-known method.

We propose the application of the overlay cognitive radio model to the communication of links within a wireless mesh networks. Based on the communication paradigm proposed by Jovicic and Viswanath, it is possible to have two concurrent transmissions in a given interference region. As as result, considerable network capacity gains are achieved. For the simple case of a network with a chain topology, the gains are as high as 33%.

This paper proposes an alternative cryptography protocol based on padding for wireless networks. It uses an orthogonal set of rotation matrices and a three-pass exchanging protocol to reach the encryption. The communicating parties do not need to know cryptographically nothing from each other in order to guarantee the communication privacy. The security of the algorithm is based on the continuous changing of the orthogonal matrix set used on the encryption process. The proposed protocol does not require any kind of keys pre- distribution. This feature is desirable for wireless ad hoc networks, where there is no predefined infrastructure, as required on classical secure channel encryption. The prototype shows that the proposal is feasible and can be advantageous when compared to One-Time Padding.

This paper analyzes the energy efficiency of wireless sensor networks using different error control schemes. An analytical model is presented to evaluate the energy efficiency in Nakagami-ra fading channels. The model is applied to error control schemes of Bluetooth technology. Some custom error control schemes and adaptive techniques using different FEC and ARQ strategies are analyzed. Performance results are obtained through analysis for networks with different number of hops and fading channel conditions.

We propose a hybrid ARQ scheme using recursive convolutional codes and the turbo principle. In the first transmission the system encodes the data through a systematic recursive convolutional encoder. In the second transmission data is interleaved prior to being encoded by a recursive convolutional encoder. Then, from the second transmission on the receiver operates as a turbo decoder. The proposed method is compared with a popular scheme known in the literature. It is shown that, while spending the same energy for transmission, the proposed scheme presents both smaller FER and decoding complexity.

A hybrid ARQ scheme for multiple transmit antennas systems is presented. Space-diversity is achieved through the use of random signal mapping (RSM). The use of RSM instead of other typical space-time coding schemes, as space-time trellis codes or space-time block codes, allows that more antennas may be added to the system with negligible complexity increase at the receiver. Coding gain is achieved with a standard parallel turbo code. Numerical results for frame error rate, throughput and number of required retransmissions for a varying number of transmit antennas are presented. The impact of the proposed scheme in the performance of a higher layer protocol, as TCP, is also investigated through computer simulations.

An unequal error protection strategy for minimising the impact of error bursts in the storage or transmission of compressed data has been proposed. The strategy is designed for the LZSS algorithm, which is a dynamic dictionary compression method. Through extensive computer simulations we investigate which information classes within LZSS are more sensitive to burst errors. Based on these results, an unequal error protection strategy that greatly reduces the error propagation during the decompression process has been proposed. Numerical results using text files from a common database are presented.

This paper studies different error control schemes in wireless sensor networks with Bluetooth technology. The tradeoff between reliability and energy consumption of Bluetooth packets are analyzed, using different error control techniques, such as retransmission and channel coding. The AUX1 packet is utilized for custom coding and also adaptive techniques are proposed based on the number of hops of the network. The wireless channel is modeled with Rayleigh fading. The results obtained may be used as references to determine the packet type in a sensor application

Bluetooth is a promising wireless technology designed for short-range ad hoc networks. This paper addresses issues related to Bluetooth performance in Nakagami-m fading channels. This distribution can model different fading intensities through parameter m. We evaluate the throughput and the packet loss rate of Bluetooth links using asynchronous packets. Relations between packet error rate and node distances are then derived, and a new piconet scheduling algorithm based on the estimated channel state information is then proposed. This polling scheme detects fading conditions and avoids transmission in bad channel conditions. The best strategy is defined using the received signal-to-noise ratio and the Nakagami fading parameter m. Simulation results demonstrate the algorithm performance for different traffic conditions.

A method of analysis for comparing the performance of time-division-coded modulation and superposition-coded modulation schemes with unequal error protection has been proposed. The performance of practical channel codes was modeled heuristically by using a signal-to-noise ratio loss factor /spl lambda/ and it was shown that the superiority of superposition-coded modulation depends on the degree of inequality in protection prescribed. A different approach, using the channel cutoff rate R/sub 0/ instead of /spl lambda/, is proposed here. Results of the analysis of the performance of both approaches are compared, and they are also compared to those results obtained from practical design examples.