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Benefiting from the deployment flexibility and the line-of-sight (LoS) channel conditions, unmanned aerial vehicle (UAV) has gained tremendous attention in data collection for wireless sensor networks. However, the high-quality air-ground channels also pose significant threats to the security of UAV aided wireless networks. In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the remote ground base station (BS). First, during the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective of maximizing the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate of transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme. peerReviewed

Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators (KPI) such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a special-purpose Industrial Internet of Things (IIoT) network comprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose 6G IIoT network incorporating seven functional building blocks categorized into: special-purpose functionalities and enabling technologies. The former consists of Wireless Environment Control, Traffic/Channel Prediction, Proactive Resource Management and End-to-End Optimization functions; whereas the latter includes Synchronization and Coordination, Machine Learning and Artificial Intelligence Algorithms, and Auxiliary Functions. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network., 11 pages, 5 figures

Current collision avoidance techniques deployed on Unmanned Aerial Vehicles (UAVs) rely on short-range sensors, such as proximity sensors, cameras, and microphones. Unfortunately, their efficiency is significantly limited in several situations; for instance, when a remote UAV approaches at high velocity, or when the surrounding environment is impaired (e.g. fog, noise). In the cited cases, to avoid collisions and maintain self-separation, UAVs often rely on the indiscriminate broadcast of their location. Therefore, an adversary could easily identify the location of the UAV and attack it, e.g., by physically shutting it down, launching wireless jamming attacks, or continuing tracking its movements. To address the above-introduced threats, in this paper we present PPCA, a lightweight, distributed, and privacy-preserving scheme to avoid collisions among UAVs. Our solution, based on an ingenious tessellation of the space, is accompanied by a thorough analytical model and is supported by an extensive experimental campaign performed on a real 3DR-Solo drone. The achieved results are striking: PPCA can efficiently and effectively avoid collisions among UAVs, by requiring a limited bandwidth and computational overhead (84.85% less than traditional privacy-preserving proximity testing approaches), while providing unique benefits in terms of privacy of the participating UAVs.

During last decade biomedical devices have become more and more performing, covering an increasing number of usages. In particular, subcutaneous devices represent one the most invasive yet innovative application; their battery management is one of the most challenging topics because, once implanted, they can be handled only through surgery, so wireless charge is the only way to feed subcutaneous systems with appreciable energy in a controlled manner. But differently from conventional consumer devices, in this scenario the energy delivered necessarily runs into human body and tissues, so the main challenge consists in maximizing the "energy delivered vs. energy dissipated in tissues" ratio, in order to guarantee human safety first while also conveying an appreciable amount of energy to the implanted device After analysing the state-of-the-art of the principal techniques, this paper proposes a novel architecture for low-power wireless charging, based on a Wiegand sensor used as a transducer in order to fulfill all the requirements from both electrical and medical points of view; such architecture is implemented inside an IC prototype in 0.35 μm lithography that acts as an ultra-low-power DC-DC buck converter and battery manager.

We discuss the need for a confluence of localization, sensing and communications if RISs are to be deployed in a self-adaptive manner in the dynamically evolving rich-scattering settings that are typical for 6G deployment scenarios such as factories. We establish that in such problems the rich-scattering wireless channels are subject to a highly nonlinear deterministic double-parametrization through both the RIS and uncontrolled moving objects. Therefore, acquiring full context-awareness through localization and sensing is a prerequisite for RIS-empowered communications. Yet, the byproducts of this daunting communications overhead can feed many appliances that require context awareness, such that overhead concerns may vanish. We illustrate the essential steps for operating a self-adaptive RIS under rich scattering based on a prototypical case study. We discover that self-adaptive RISs outperform context-ignorant RISs only below a certain noise threshold that depends, among other factors, on how strongly uncontrolled perturbers impact the wireless channel. We also discuss ensuing future research directions that will determine the conditions under which RISs may serve as technological enabler of 6G networks., 7 pages, 5 figures, submitted to an IEEE Journal

With the integration of communication and computing, it is expected that part of the computing is transferred to the transmitter side. In this paper we address the general problem of Frequency Modulation (FM) for function approximation through a communication channel. We exploit the benefits of the Discrete Cosine Transform (DCT) to approximate the function and design the waveform. In front of other approximation schemes, the DCT uses basis of controlled dynamic, which is a desirable property for a practical implementation. Furthermore, the proposed modulation allows to recover both the measurement and the function in a single transmission. Our experiments show that this scheme outperforms the double side-band (DSB) modulation in terms of mean squared error (MSE). This can also be implemented with an agnostic receiver, in which the function is unknown to the receiver. Finally, the proposed modulation is compatible with some of the existing transmission technologies for sensor networks., Comment: Paper accepted in IEEE Open Journal of Signal Processing (2023)

Wireless sensor networks face many challenges, the major one being energy. Batteries are the main source of energy for the sensor nodes. When the battery is depleted, it must either be charged or replaced. This may be expensive or impossible to do. Energy harvesting has been proposed as an alternative. The energy is harvested and then stored in a battery. However, even if the battery is not in use, it experiences current leakages. We study the performance of a single node, which has data packets and energy tokens. The energy that is harvested is kept in reserve as energy tokens in an energy buffer and utilised by the data packets for transmission. This paper investigates the impact of imposing a threshold on the token buffer of the system. The problem considered is managing the energy buffer by taking into account storage of energy, usage by the data packets and energy leakage. The proposed model considers the transmission of high and low priority data packets. To ensure that there are tokens available in the system to transmit the high-priority data packets in case the arrival rate of the low-priority data packets is too high at the expense of high priority data packets, a threshold is imposed on the token buffer. To illustrate our approach, a Geo/Geo/1/k system is modelled and finite Markov chain model tools are used to analyse it. Numerical examples, which show how performance measures such as the mean number of data packets and tokens in the system are affected by energy harvesting, leakage and threshold, are presented. From the results obtained we show that the model can be utilised in the analysis and control of a wireless sensor network, as it captures the usage and leakage of energy. A trade-off between threshold and rate of leakage exists.

Cyber-physical-social systems (CPSS) as an emerging computing paradigm are revolutionizing the relationship between humans, devices and physical environments and will be expected to have a fundamental impact and influence in our daily lives. In a typical CPSS scenario, plenty of nodes (or devices) often adopt wireless distributed contention protocols to transmit sensed environment states for decision making. Therefore, designing a high-efficient and collision-less contention protocol is of great importance for wireless networking. As is known, in the classic time-to-frequency (T2F) protocol, each node signals on one randomly chosen subcarrier for contention. Though reducing the conventional time-domain contention time, T2F leads to many contention collisions (i.e., multiple nodes choose the same subcarrier) when the number of nodes is large. Aiming at this challenge, in this paper, we propose M-T2F (where each node signals on multiple randomly chosen subcarriers for contention) and analyze its consensus achievement. Specifically, M-T2F supports more nodes for transmission but introduces less collision, benefiting from lots of multicarrier choice combinations. Since the subset relationship between subcarrier choices poses a challenge to reach a consensus among nodes, M-T2F enables all nodes to reach a consensus by excluding the subset relationship and reduces contention time by utilizing wireless broadcast characteristic

Sensor nodes in wireless sensor networks (WSN) are used for perceiving, monitoring, and controlling a wide range of applications. Owing to the small size of sensor nodes and limited power sources, energy saving is critical for ensuring network longevity. Protocols in different layers consume energy for their function. It is possible to significantly reduce energy usage by including energy-efficiency measures in the protocol design. Most protocols in the literature focus on the energy efficiency in individual layers. Recent studies have shown that cross-layer designs are more energy efficient than individual layer designs. Therefore, this study presents a cross-layer protocol design that combines network and data link layers to minimize energy consumption. This article proposes a novel "Quality of Service Based Cross-layer (QSCL) Protocol" by combining the IEEE 802.15.4-based MAC protocol and the LEACH-based routing protocol. The dynamic duty cycle of the IEEE 802.15.4 protocol was modified based on the amount of data present in the node, which minimized the energy consumption of the data-transfer mechanism. The cluster head (CH) selection of the LEACH-based protocol was modified to consider the average residual energy (RE) of the nodes and their distance from the sink. This helps preserve the energy in the CH, thereby extending the network lifetime. Simulation studies demonstrated that the proposed QSCL outperformed the existing protocols by prolonging the network lifetime.

This paper deals with smart home energy management through load scheduling and optimal use of available energy sources. In this study, three energy sources were considered: the national electricity grid, photovoltaic (PV) energy, and the storage unit. The PV array can provide the maximum power to the load at a given operating point where the output power changes with temperature, radiation and load. Therefore, a real-time controller is proposed to track the maximum power. An energy management algorithm has been proposed in a smart home to achieve the main goal of making the electricity bill as low as possible. The algorithm involves scheduling loads by assigning a priority to each load. The loads are supplied with the required power according to their priorities and the available energy. The obtained results indicate that supplying the PV system with a fuzzy-based MPPT indicates an increase in system efficiency. The results also showed that the use of energy management based on load scheduling led to a significant reduction in the electricity bill.

The centralized computing model in industrial Internet of Things (IIoT) leads to large delay and unbalanced traffic, which strictly restricts the adoption of IIoT in industrial applications demanding high network performance. To cope with the problem, researchers resort to the in-network computation model in which the computation capability is distributed among nodes in IIoT. Existing works on the in-network computation assume that the network connectivity built in advance meets the performance requirement of the in-network computation model. Nevertheless, no node placement methods have been proposed to build network connectivity supporting in-network computation. For this reason, we propose an in-network-computation-oriented node placement (INP) algorithm. The INP algorithm first decomposes the whole problem into several delay constrained relay node placement problems, and then, solves them sequentially. Moreover, we prove that the INP algorithm ensures explicit time complexity and approximation ratio. Finally, we verify the efficiency of this work through extensive simulations and preliminary experiments.

The most essential part of any internet of things (IoT) model is the wireless sensor networks (WSN); latest technologies are combined with the applications of WSN results in fast, efficient, flexible as well as economical models. These networks are highly prone to attacks considering their characteristic nature, which includes self-organization, a topology that is dynamic, large-scale and constrained on the resources. Various models have been proposed for the detection of attacks in these wireless sensor networks. This research work proposes three-phase consensus based secured data sharing (TCSDS) in IoT environment. TCSDS adopts the consensus-based protocol for designing the security model in this research. Furthermore, TCSDS comprises three distinctive phases, each phase consisting of novel algorithm. First phase includes the setting up threshold value for sensor nodes. Second phase includes the efficient data packet transmission and third phase includes the efficient and secure routing, which tends to discard the unsecured nodes. TCSDS is evaluated considering the different parameter like energy consumption, malicious packet detection and throughput. Further comparison with the existing model is carried out based on the classified and misclassified packet; through the comparative analysis, it is observed that the TCSDS approach simply outperforms the existing model.

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

Recent advancements have demonstrated that Wireless Sensor Networks (WSNs) that harvest energy from the surrounding environment have the potential to enable continuous network operations using renewable energy sources. However, the spatio-temporal nature of such networks results in significant variations and only allows for intermittent recharging opportunities, leading to minimal data service sustainability. Among the numerous challenges faced by WSNs, energy-aware routing represents a critical constraint that limits the network's performance. Battery limitations also pose a significant challenge, particularly for battery-powered sensors that consume significant amounts of energy and quickly deplete their battery life. Therefore, this research paper explores various techniques for recharging sensor nodes (SNs) and proposes a robust model that can be utilized in an ad hoc on-demand distance vector routing protocol (AODV) scenario. This model not only improves the energy efficiency of WSNs but also enhances their overall lifespan and reduces traffic congestion.

The Internet of Things relies on wireless sensor networks (WSNs) to gather data and transmit it to centralized databases. Most of the power used by these sensors goes toward detecting or collecting and delivering the data because they run on batteries and have limited resources. Wormhole attacks are the most devastating threat that may befall these networks, making data security a top priority throughout data exchange. The communication, security, and performance of the network are severely compromised by these assaults since they are launched without first gathering essential information. Due to limited resources in sensors, it is more difficult to prevent in an IoT-based network environment. If an unwanted node is present in the system, it will slow everything down. In order to keep malicious software and hacking attempts out of WSNs, secure routing protocols must be implemented. Since security is paramount in WSN, several different safe routing protocols have been created to enhance the efficiency with which packets may be sent. Using the multipath link routing protocol along with improved blow fish model (MLRP-IBFM), our proposed strategy creates a safe path for private IoT data to travel between sensor nodes with varying levels of available power. Data transfer rates, power consumption, latency from beginning to finish, system lifespan, and information storage space are only few of the performance metrics where this routing protocol excels when compared to two other current routing protocols.

This study introduces a novel system for detecting humans inside a building by utilizing RF signals from the building’s exterior. Existing RF communication devices encounter signal attenuation issues when passing through walls, limiting their effectiveness. In contrast, our system employs a low-power, long-distance communication signal operating at 433 MHz to enhance signal permeability, enabling the accurate detection of individuals within the building. The system analyzes received signal strength indicator (RSSI) data using variance and mean analysis algorithms to determine the presence or absence of people. The evaluation results indicate promising average accuracies of 88% for the variance analysis algorithm and 97.7% for the mean analysis algorithm. The proposed system holds potential for real-world deployment, particularly in challenging scenarios such as fire incidents, where pre-installation is challenging. Continued research and development efforts aim to enhance the system’s performance and address any limitations, making it more effective and robust in various practical applications.

We perform a comprehensive analysis of packet losses occurring at an AQM buffer in which the packet deletion probability is relative to the size of the queue. Several characteristics of the loss process are derived: the number of deletions in an interval of length t, the temporary intensity of deletions at arbitrary time, the steady-state loss ratio, and the number of losses if there is no service. All of them are obtained for a general deletion probability function and an advanced model of the arrival process, which incorporates, among other things, the autocorrelation of traffic. Analytical results are accompanied by examples in which numerical values are obtained for several configurations of the system. Using these examples, the dependence of the loss process on the initial system state, deletion probability function, and traffic autocorrelation are discussed.

This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements.

Wireless sensor network (WSNs) are network of Sensor Nodes (SNs) with inherent sensing, processing and communicating abilities. One of current concerns in wireless sensor networks is developing a stable clustered heterogeneous protocol prolonging the network lifetime with minimum consumption of battery power. In the recent times, many routing protocols have been proposed increasing the network lifetime, stability in short proposing a reliable and robust routing protocol. In this paper we study the impact of hierarchical clustered network with sensor nodes of two-level heterogeneity. The main approach in this research is to develop an enhanced multi-hop DEEC routing protocol unlike DEEC. Simulation results show the proposed protocol is better than DEEC in terms of FDN (First Dead Node), energy consumption and Packet transmission.

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead.

Advancements in smart-network technologies enabled the Internet of Things to expand globally. IoT-based Wireless sensor networks play an essential role in collecting and aggregating data in applications like military and health care, where security and power efficiency are significant challenges. Due to vulnerable attacks on several nodes or devices, IoT-based WSN becomes an uncertain environment. This paper aims to offer a fault tolerant, robust, and secure packet forwarding scheme that incorporates more intelligent features and powerful algorithms to eliminate DoS attacks. Initially, routing is performed, with the optimal path determined by recovering malicious nodes detected in an IOT-based WSN environment using the proposed algorithm ICARS- Intelligent Channel aware Reputation scheme based on generation of Resolver Node and Intelligent Route Request and Route Response methods. Next to that, encryption is used to ensure safe transmission. This paper proposes a Modified Rijndael Algorithm (MRA) for secured authentication. By integrating ICARS with MRA, this proposed strategy removes the attacking situation and makes the system more resistant to DoS attacks. The result section proved that the proposed approach increases the throughput, decreases the retransmission ratio, increases energy efficiency, decreases end-to-end delay, and increases Packet Delivery Ratio.

The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, among which the Thread protocol has emerged as a promising option. Thread is IEEE 802.15.4 based and has advanced capabilities like mesh networking, IPv6 support, and multiple gateways providing no single point of failure. This paper presents the design and evaluation of a low-cost mesh network using Raspberry Pi, nRF52840 dongle, and OpenThread 1.2 (i.e., an open-source software implementation of the Thread protocol stack). The research elaborates on the hardware and software solutions used, as well as the network topologies adopted. To evaluate the performance of the developed system, extensive real-time tests are performed, considering parameters, such as jitter, packet loss, and round trip time. These tests effectively demonstrate the effectiveness of the Thread network. Furthermore, the impact of varying payload size and bitrate on the network is analyzed to understand its influence. The behavior of the multi-hop network is also examined under link failure scenarios, providing insights into the network’s robustness. Our findings provide valuable insights for researchers interested in designing low-cost and efficient mesh networks for various IoT applications, including home automation, building/campus monitoring systems, distributed industrial IoT applications, and smart city infrastructure.

As sensor nodes communicate via wireless channels, information security is essential for wireless sensor networks. Efficient protection mechanisms to ensure data security among nodes are critical. This study developed the bi-directed grouping (top-down grouping (TDG) and bottom-up grouping (BUG)) methods. In this study, we propose a group-based key distribution method, “aggregator-based grouping” (ABG), which combines the advantages of TDG and BUG to address the security issues of nodes. It employs horizontal and vertical searches, which are based on breadth-first and aggregator searches, respectively. A node performs encryption and decryption only when it requires either data aggregation or inter-group communication. The secure aggregation method can be applied to key-grouping management. We compared the proposed method with TDG and BUG using the same number of groups and network structure. For a network with maximum group members of 50 (total sensor nodes = 1000), compared with TDG and BUG, ABG reduced the number of encryption and decryption operations by ~36%. ABG avoids unnecessary encryption and decryption in the network.

The problem of strong edge coloring discusses assigning colors to the edges of a graph such that distinct colors are assigned to any two edges which are either adjacent to each other or are adjacent to a common edge. The least number of colors required to define a strong edge coloring of a graph is called its strong chromatic index. This problem is equivalent to the problem of assigning collision-free frequencies to the links between the elements of a wireless sensor network. In this article, we discuss a novel way of generating new graphs from existing graphs. This graph construction is known as inflating a graph. We discuss the strong chromatic index of graphs generated by inflating some common classes of graphs and graphs derived from them. In particular, we consider the cycle graph, which is symmetric in nature, and graphs such as the path graph and the star graph, which are not symmetric. Further, we analyze the factors which influence the strong chromatic index of these inflated graphs.

Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.

As radio frequency (RF) hardware continues to improve, two-way ranging (TWR) has become a viable approach for high-precision ranging applications. The precision of a TWR system is fundamentally limited by estimates of the time offset T between two platforms and the time delay τ of a signal propagating between them. In previous work, we derived a family of optimal “one-shot” joint delay–offset estimators and demonstrated that they reduce to a system of linear equations under reasonable assumptions. These estimators are simple and computationally efficient but are also susceptible to channel impairments that obstruct one or more measurements. In this work, we formulate an extended Kalman filter (EKF) for this class of estimators that specifically addresses this limitation. Unlike a generic KF approach, the proposed solution specifically integrates the estimation process to minimize the computational complexity. We benchmark the proposed first- and second-order EKF solutions against the existing one-shot estimators in a MATLAB Monte Carlo simulation environment. We demonstrate that the proposed solution achieves comparable estimation performance and, in the case of the second-order solution, reduces the computation time by an order of magnitude.

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks’ locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

The widespread application of wireless sensor networks (WNSs) is obstructed by the severely limited energy constraints and security threat for sensor nodes. Since traditional routing and security schemes are not suited for these networks, a large part of research focusses on energy efficient routing protocols while extending the network lifetime. Uneven distribution of communication loads result in network partitioning. Traditional novel en-route filtering approaches, notably commutative cipher based en-route filtering (CCEF) saves energy by early filtering of false reports. However this approach main focus is security not network lifetime is limited by fixed paths and underlying routing not suitable for WSNs. In order to cater these problems we propose energy efficient routing and pre-deterministic key distribution with dynamic path selection in CCEF. Modified CCEF (MCCEF) aims at saving energy and extending network lifetime while maintaining filtering power as in CCEF. Experimental results demonstrate the validity of our approach with an average of three times network lifetime extension, 5.022% energy savings, and similar filtering power as the original scheme.

Silicon-based kinetic energy converters employing variable capacitors, also known as electrostatic vibration energy harvesters, hold promise as power sources for Internet of Things devices. However, for most wireless applications, such as wearable technology or environmental and structural monitoring, the ambient vibration is often at relatively low frequencies (1–100 Hz). Since the power output of electrostatic harvesters is positively correlated to the frequency of capacitance oscillation, typical electrostatic energy harvesters, designed to match the natural frequency of ambient vibrations, do not produce sufficient power output. Moreover, energy conversion is limited to a narrow range of input frequencies. To address these shortcomings, an impacted-based electrostatic energy harvester is explored experimentally. The impact refers to electrode collision and it triggers frequency upconversion, namely a secondary high-frequency free oscillation of the electrodes overlapping with primary device oscillation tuned to input vibration frequency. The main purpose of high-frequency oscillation is to enable additional energy conversion cycles since this will increase the energy output. The devices investigated were fabricated using a commercial microfabrication foundry process and were experimentally studied. These devices exhibit non-uniform cross-section electrodes and a springless mass. The non-uniform width electrodes were used to prevent pull-in following electrode collision. Springless masses from different materials and sizes, such as 0.5 mm diameter Tungsten carbide, 0.8 mm diameter Tungsten carbide, zirconium dioxide, and silicon nitride, were added in an attempt to force collisions over a range of applied frequencies that would not otherwise result in collisions. The results show that the system operates over a relatively wide frequency range (up to 700 Hz frequency range), with the lower limit far below the natural frequency of the device. The addition of the springless mass successfully increased the device bandwidth. For example, at a low peak-to-peak vibration acceleration of 0.5 g (peak-to-peak), the addition of a zirconium dioxide ball doubled the device’s bandwidth. Testing with different balls indicates that the different sizes and material properties have different effects on the device’s performance, altering its mechanical and electrical damping.

The low-energy adaptive clustering hierarchy (LEACH) protocol has been developed to be implemented in wireless sensor networks (WSNs) systems such as healthcare and military systems. LEACH protocol depends on clustering the employed sensors and electing one cluster head (CH) for each cluster. The CH nodes are changed periodically to evenly distribute the energy load among sensors. Updating the CH node requires electing different CH and re-clustering sensors. This process consumes sensors’ energy due to sending and receiving many broadcast and unicast messages thus reduces the network lifetime, which is regarded as a significant issue in LEACH. This research develops a new approach based on modifying the LEACH protocol to minimize the need of updating the cluster head. The proposal aims to extend the WSN’s lifetime by maintaining the sensor nodes’ energy. The suggested approach has been evaluated and shown remarkable efficiency in comparison with basic LEACH protocol and not-clustered protocol in terms of extending network lifetime and reducing the required sent messages in the network reflected by 15%, and, in addition, reducing the need to reformatting the clusters frequently and saving network resources.

With the advent of 5G technology, the notion of latency got a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework of timing requirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems. Funding Agencies|Velux Foundation under Villum Investigator Grant WATER; Danish Council for Independent Research [8022-00284B]; European Research Council (ERC) through the European Union [101003431]; Swedish Research Council (VR); ELLIIT; CENIIT

In modern medicine, smart wireless connected devices are gaining an increasingly important role in aiding doctors' job of monitoring patients. More and more complex systems, with a high density of sensors capable of monitoring many biological signals, are arising. Merging the data offers a great opportunity for increasing the reliability of diagnosis. However, a huge problem is constituted by synchronization. Multi-board wireless-connected monitoring systems are a typical example of distributed systems and synchronization has always been a challenging issue. In this paper, we present a distributed full synchronized system for monitoring patients' health capable of heartbeat rate, oxygen saturation, gait and posture analysis, and muscle activity measurements. The time synchronization is guaranteed thanks to the Fractional Low-power Synchronization Algorithm (FLSA).

Refereed/Peer-reviewed Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

Long-Range (LoRa) transmission technology is potentially a suitable solution in abundant applications such as smart cities, smart industries, smart health, and others, although it is challenging and complex to implement. LoRa is a non-cellular modulation technology for Long-Range Wide-Area Networks (LoRaWAN) and is suitable for Internet of Things (IoT) solutions through its long-range and low-power consumption characteristics. The present paper provides a comprehensive analysis of LoRa wireless transactions through several measurements, which differ in LoRa parameter configuration. The results showed dependency of the power consumed by the transaction on the selected Effective Isotropic Radiated Power (EIRP). The quantity of energy consumed by the transaction also significantly depends on the selected data rate (combination of the spread factor and bandwidth) and payload. Web of Science 28 3 36 30

Energy and spectrum efficiency for energy management systems based on wireless sensor networks in intelligent structures and powered by ambient energy harvesting (EH) are the main problems in wireless sensor networks. Herein, we consider relay selection methods. To address this issue, we proposed the optimal multiantenna power beacon opportunistic relay selection (OMPB-ORS) protocol, which uses decoding and forward methods, in which the relay wireless sensor nodes and the second source are energy-restricted and can harvest energy from a power beacon (PB) multiantenna to transmit aggregated information data from source to destination. The proposed protocol based on specific s witching t ime r eceiver a rchitecture e nhances e nd-to-end p erformance f or m aximum h ardware impairments and interference for the transceiver. To evaluate the performance, we compared our proposed protocol with best ORS (B-ORS), conventional ORS (C-ORS), and hybrid partial relay selection (H-PRS) protocols. Using the Rayleigh-fading channel, the simulation is driven based on asymptotic and exact form expressions of throughput (TP) and outage probability (OP). Simulation results show that the OMPB-ORS protocol achieves a higher TP and OP than all compared protocols.

Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment., 12 pages, 8 figures

The sensor nodes in wireless sensor networks (WSNs). inherit low processing, shorter range and low power features with the miniature size to offer wireless transmission. Therefore, in order to fulfil the long-time data sensing need, several Quality of Service (QoS) parameters such as: lesser delay, higher throughput and packet delivery ratio (PDR) with minimum overhead must to be improved. In recent years extensive research efforts have been made to ameliorate these parameters to achieve optimum QoS. However, the rate adaptation and congestion control in WSNs are still least explored areas. The traffic congestion in WSNs is the main reason that results in higher delay and low throughput. In this paper, a new rate aware congestion control (RACC) mechanism has been proposed which defines three levels of congestion based on which the data rate, throughput, overhead and the delay. RACC at the transport layer, improves congestion by source rate regulation at the specific hotspot areas. Further, RACC has been applied to different modulation schemes like: 16 QAM (Quadrature Amplitude Modulation), BPSK (Binary Phase Shift Keying) and QPSK (Quadrature Phase Shift Keying) to test the optimum modulation scheme for the proposed approach. The testing is done to ensure the that the data can be sent to the longer distant sensors using appropriate modulation technique suitable for the congestion model (RACC). The simulation outcomes in NS2 tool confirms the improvement of RACC over existing techniques (Delay-aware congestion control protocol (DACC) and Joint energy replenishment and load balancing (J-ERLB)) in WSNs. The overall improvement for RACC over existing techniques follows an improvement percentage 17% for throughput parameter, for packet delivery ratio, it is 8.35%, while for normalized routing overhead it shows 0.56% and for MAC Overhead, average end to end delay, and average remaining energy it shows 0.64%, 2.04% and 59.28% improvement respectively.

We introduce a novel ultra-low power system for tracking animal movements over long periods with an unprecedented high-temporal-resolution. The localization principle is based on the detection of cellular base stations using a miniaturized software-defined radio, weighing 2.0 g, including the battery, and having a size equivalent to two stacked 1-euro cent coins. Therefore, the system is small and lightweight enough to be deployed on small, wide-ranging, or migrating animals, such as European bats, for movement analysis with an unprecedented spatiotemporal resolution. The position estimation relies on a post-processing probabilistic RF pattern-matching method based on the acquired base stations and power levels. In several field tests, the system has been successfully verified, and a run-time of close to one year has been demonstrated.

Cognitive radio wireless sensor networks (CR-WSNs) are a type of WSNs that use cognitive radio technology to enhance the spectrum utilization and energy efficiency. This paper proposes an energy-efficient resource allocation algorithm (EERAA) to prolong the lifetime of a WSN-based smart irrigation system under realistic scenarios. In the proposed algorithm, power allocation and subcarrier assignment are performed consecutively. Considering the impact of the intercarrier interference (ICI) caused by timing offset, the problem of maximizing network-averaged capacity is formulated considering power and interference constraints in realistic scenarios. The obtained results reveal that the proposed algorithm attempts to maximize the averaged capacity of the CR-WSN subject to the total power constraint and tolerable interference. Numerically, the proposed algorithm can reduce the network energy consumption by up to 30%, compared with conventional approaches, while maintaining a high level of system performance in terms of secondary users’ (SUs) averaged capacity.

Traditional fundus image-based diabetic retinopathy (DR) grading depends on the examiner’s experience, requiring manual annotations on the fundus image and also being time-consuming. Wireless sensor networks (WSNs) combined with artificial intelligence (AI) technology can provide automatic decision-making for DR grading application. However, the diagnostic accuracy of the AI model is one of challenges that limited the effectiveness of the WSNs-aided DR grading application. Regarding this issue, we propose a WSN architecture and a parallel deep learning framework (HybridLG) for actualizing automatic DR grading and achieving a fundus image-based deep learning model with superior classification performance, respectively. In particular, the framework constructs a convolutional neural network (CNN) backbone and a Transformer backbone in a parallel manner. A novel lightweight deep learning model named MobileViT-Plus is proposed to implement the Transformer backbone of the HybridLG, and a model training strategy inspired by an ensemble learning strategy is designed to improve the model generalization ability. Experimental results demonstrate the state-of-the-art performance of the proposed HybridLG framework, obtaining excellent performance in grading diabetic retinopathy with strong generalization performance. Our work is significant for guiding the studies of WSNs-aided DR grading and providing evidence for supporting the efficacy of the AI technology in DR grading applications.

Several industries use wireless sensor networks (WSN) for various tasks such as monitoring, data transmission, and data gathering. They find applications in the industrial internet of things (IIoT). WSNs are utilized to track and monitor changes in the environment. Since they include multiple small sensor nodes (SN), they are severely constrained, so resource management geared toward energy efficiency is crucial in this kind of network. Minimizing the power to interpret, transmit, and store data between various sensors poses important challenges. Experts have considered various ways to address these issues that unavoidably affect the network’s performance: reducing energy usage while maintaining system throughput remains the primary research issue. Another important concern relates to network security. Specifically, intrusion detection and avoidance are major concerns. In this work, we introduce the meta-heuristic-based secure and energy-efficient routing (MHSEER) protocol for WSN-IIoT. The protocol learns the forwarding decisions using the number of hops, connection integrity characteristics, and accumulated remaining energy. To make the method more secure, the protocol also employs counter-encryption mode (CEM) to encrypt the data. A meta-heuristics study designed to achieve reliable learning is used in the suggested protocol. The protocol consists of two stages. The first stage uses a heuristics method to improve the option for dependable data routing. Security based on a computationally simple and random CEM is accomplished in the second stage. The proposed MHSEER protocol has been compared to the secure trust routing protocol for low power (Sectrust-RPL), heuristic-based energy-efficient routing (HBEER), secure and energy-aware heuristic-based routing (SEHR), and secure energy-aware meta-heuristic routing (SEAMHR) in terms of packet drop ratio, throughput, network delay, energy usage, and faulty pathways. The proposed protocol increases throughput to 95.81% and decreases the packet drop ratio, packet delay, energy consumption, and faulty pathways to 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively.

Wireless sensor nodes are usually powered by batteries that have limited energy capacity. In many applications, the nodes are installed in inaccessible locations where they are problematic to replace or recharge. Therefore, energy optimization is crucial for increasing the node's lifetime. This study presents a method for the analysis and prediction of the energy consumption of Sigfox-based wireless sensor nodes. The method is illustrated in a use case where the nodes monitor the water level in drainage lines in cities to improve surface and wastewater management. We propose a formal model-based technique using the UPPAAL statistical model checker tool to model and analyze the node's lifetime. Statistical model checking (SMC) provides a highly scalable technique for the performance analysis of complex cyber-physical systems. The model captures the energy-related behavior of the node, the Sigfox radio specification, and the sensor, each parameterized with values from the device's datasheets. Furthermore, we calibrate the model using measurements obtained from real-world hardware. Finally, we evaluate a collection of strategies to optimize the battery lifetime of the node. We simulate the model with a 10000-mAh battery, and the results indicate that we can extend the node's lifetime from 202 days to 2.71 years using our most optimized transmission strategy.

The Internet of Things (IoT) integrates different advanced technologies in which a wireless sensor network (WSN) with many smart micro-sensor nodes is an important portion of building various IoT applications such as smart agriculture systems, smart healthcare systems, smart home or monitoring environments, etc. However, the limited energy resources of sensors and the harsh properties of the WSN deployment environment make routing a challenging task. To defeat this routing quandary, an energy-efficient routing protocol based on grid cells (EEGT) is proposed in this study to improve the lifespan of WSN-based IoT applications. In EEGT, the whole network region is separated into virtual grid cells (clusters) at which the number of sensor nodes is balanced among cells. Then, a cluster head node (CHN) is chosen according to the residual energy and the distance between the sink and nodes in each cell. Moreover, to determine the paths for data delivery inside the cell with small energy utilization, the Kruskal algorithm is applied to connect nodes in each cell and their CHN into a minimum spanning tree (MST). Further, the ant colony algorithm is also used to find the paths of transmitting data packets from CHNs to the sink (outside cell) to reduce energy utilization. The simulation results show that the performance of EEGT is better than the three existing protocols, which are LEACH-C (low energy adaptive clustering hierarchy), PEGASIS (power-efficient gathering in sensor information systems), and PEGCP (maximizing WSN life using power-efficient grid-chain routing protocol) in terms of improved energy efficiency and extended the lifespan of the network.

The research is aimed at the development of new algorithms for the selection of a main node and methods of placement of sensor nodes for the effective construction of wireless sensor networks (WSNs), therefore it is relevant.The analysis of the literature made it possible to establish that many scientists were engaged in the research of methods of building smart energy systems for a smart city management. However, the researchers did not analyze the potential and real threat of data transmission from energy consumption control sensors.The purpose of the research is to improve the accuracy of control and accounting of energy resources consumed by residents for the neighborhood using wireless sensor networks.The article analyzes the state of the art in the field of research on wireless sensor networks (WSNs), determines the most important characteristics and structure of WSNs in the organization of the automated system of control and accounting of energy resources (ASCOE). An analysis of the existing algorithms for routing, self-organization and selection of t a main node of the cluster in wireless sensor networks of sensors for controlling the consumption of energy parameters by the consumer was carried out. The duration of the network's life cycle, the period of stability and the bandwidth of the BSMwas evaluated based on the ratio between the radius of coverage and the radius of the communication range.Studies have shown that the applied algorithm for selecting the main node of the cluster based on fuzzy logic using Voronoi diagrams significantly increases the life cycle of the sensor network compared to the LEACH and Fuzzy C-Means algorithms., У статті вирішено актуальне науково-технічне завдання підвищення точності контролю та обліку витрат енергетичних ресурсів мешканцями багатоповерхового мікрорайону з використанням сенсорних мереж. Проведено аналіз сучасного стану в галузі досліджень бездротових сенсорних мереж (БСМ), визначення найбільш важливих характеристик і структури БСМ при організації автоматизованої системи контролю та обліку енергоресурсів (АСКОЕ). Здійснено аналіз існуючих алгоритмів маршрутизації, самоорганізації та вибору головного вузла кластерав бездротових сенсорних мережах датчиків контролю витрати енергетичних параметрів споживачем. Проведена оцінка тривалості життєвого циклу мережі, періоду стабільності та пропускної спроможності БСМ на основі відношення між радіусом покриття та радіусом дальності зв’язку.

Air quality, especially particulate matter pollution levels in urban areas, is an essential academic and social topic due to its association with health issues and climate change. In Romania, increasing awareness of urban communities and the availability of low-cost sensors has led to the development of an independent monitoring network currently distributed in over 194 cities and towns. The uRADMonitor® network consists of 630 sensors measuring PM10 and PM2.5 concentration levels. The spatial distribution of the sensors complements the national air quality network with sensors in residential areas, intense traffic zones, and industrial areas. The data are available through a user-friendly web-based platform from uRADMonitor®. Based on data collected in 2021, we present an analysis of PM10 pollution levels in Romania’s five most populated urban areas by employing five annual statistical indicators recommended by the European Environmental Agency. For the case of Timișoara, we also compare the data measured by independent sensors with those from the national monitoring network. The results highlight the usefulness of our community-based network as it complements the national one.

In the era of rapid technological growth, we are facing increased energy consumption. The question of using renewable energy sources is also essential for the sustainability of wireless sensor networks and the Industrial Internet of Things, especially in scenarios where there is a need to deploy an extensive number of sensor nodes and smart devices in industrial environments. Because of that, this paper targets the problem of monitoring the operations of solar-powered wireless sensor nodes applicable for a variety of Industrial IoT environments, considering their required locations in outdoor scenarios and the efficient solar power harvesting effects. This paper proposes a distributed wireless sensor network system architecture based on open-source hardware and open-source software technologies to achieve that. The proposed architecture is designed for acquiring solar radiation data and other ambient parameters (solar panel and ambient temperature, light intensity, etc.). These data are collected primarily to define estimation techniques using nonlinear regression for predicting solar panel voltage outputs that can be used to achieve energy-efficient operations of solar-powered sensor nodes in outdoor Industrial IoT systems. Additionally, data can be used to analyze and monitor the influence of multiple ambient data on the efficiency of solar panels and, thus, powering sensor nodes. The architecture proposal considers the variety of required data and the transmission and storage of harvested data for further processing. The proposed architecture is implemented in the small-scale variants for evaluation and testing. The platform is further evaluated with the prototype sensor node for collecting solar panel voltage generation data with open-source hardware and low-cost components for designing such data acquisition nodes. The sensor node is evaluated in different scenarios with solar and artificial light conditions for the feasibility of the proposed architecture and justification of its usage. As a result of this research, the platform and the method for implementing estimation techniques for sensor nodes in various sensor and IoT networks, which helps to achieve edge intelligence, is established.

L’objectiu d’aquest treball de fi de grau és posar solució al risc d’incendis forestals, a partir del disseny d’un dispositiu sensorial, que detecta diferents paràmetres que afecten directament a l’aparició del foc per poder detectar-lo el més aviat possible i així erradicar-lo abans que es faci massa gran. Els paràmetres que té en compte són la temperatura, la humitat relativa i la concentració de monòxid de carboni. Al llarg de la memòria s’explica com afecten aquests factors, com es mesuren i entre quins valors es poden moure perquè no suposin un risc d’aparició d’incendi. Aquest document conté un estudi detallat del funcionament de les xarxes inalàmbriques de sensors i de les estructures i les comunicacions d’aquests. A part de dur a terme la construcció i programació d’un node sensor, també es realitza un estudi econòmic i una prova d'implantació d’una xarxa inalàmbrica amb els sensors creats que es realitza en un territori del Parc de Collserola. Un cop finalitzat el treball, es presenta el prototip final de sensor i es presenten les conclusions del projecte El objetivo de este trabajo es dar solución al riesgo de incendios forestales, a partir del diseño de un dispositivo sensorial, que detecta diferentes parámetros que afectan directamente a la aparición del fuego para poder detectarlo lo antes posible y así erradicarlo antes de que se haga demasiado grande. Los parámetros que tiene en cuenta son la temperatura, la humedad relativa y la concentración de monóxido de carbono. A lo largo de la memoria se explica cómo afectan estos factores, cómo se miden y entre qué valores se pueden mover para que no supongan un riesgo de aparición de incendios. Este documento contiene un estudio detallado del funcionamiento de las redes inalámbricas de sensores y de las estructuras y las comunicaciones de estos. A parte de realizar la construcción y programación de un nodo sensor, también se realiza un estudio económico y una prueba de implantación de una red inalámbrica con los sensores creados que se realiza en un territorio del Parque de Collserola. Una vez finalizado el trabajo, se presenta el prototipo final del sensor y se presentan las conclusiones del proyecto The objective of this work is to provide a solution to the risk of forest fires, based on the design of a sensory device that detects different parameters that directly affect the appearance of the fire in order to detect it as soon as possible and thus eradicate it before it becomes too big. The parameters it takes into account are temperature, relative humidity and carbon monoxide concentration. Throughout the report it is explained how these factors are affected, how they are measured and between which values they can be moved so that they do not pose a risk of fire occurrence. This document contains a detailed study of the operation of wireless sensor networks and their structures and communications. In addition to the construction and programming of a sensor node, an economic study and a test of the implementation of a wireless network with the sensors created in a territory of the Collserola Park is also carried out. Once the work is finished, the final prototype of the sensor is presented and the conclusions as well