Wireless Sensor Networks (WSNs)

The growing demand for efficient management of residential resources has led to the development of remotely accessible control systems that enable users to monitor and regulate household utilities from a distance. These systems aim to improve safety, reduce resource wastage, and enhance user convenience by providing centralized control over utilities such as electricity, water, and gas. This paper presents a comprehensive review of remote residential utility control systems, focusing on their architectural designs, communication mechanisms, and operational workflows. Various existing approaches are analyzed based on their underlying technologies, control strategies, and system integration capabilities. The study further examines key challenges, including interoperability limitations, communication reliability, security concerns, and scalability issues, which hinder the effectiveness of current solutions. In addition, essential design considerations for developing robust and efficient systems are identified, emphasizing modularity, responsiveness, and user accessibility. Based on the critical analysis of existing literature, significant research gaps are highlighted, particularly in the areas of unified control, seamless integration of multiple utilities, and efficient coordination between system components.

One of the main challenges in wireless sensor networks is the limited energy and operational lifespan of nodes. In this paper, compressive sensing theory is employed to reduce the number of required transmissions, thereby extending the network's lifetime. Data in many wireless sensor networks exhibits both spatial and temporal correlation. The proposed method leverages both forms of correlation to minimize the mean squared error of the reconstructed signal. Furthermore, a sleep/wake scheduling mechanism is applied to decrease node energy consumption. Selecting the optimal set of awake nodes is an NP hard problem; thus, a genetic algorithm is utilized to determine which nodes should remain active. Once the active nodes are identified, an ant colony optimization algorithm constructs an efficient data aggregation tree. Simulation results demonstrate that the proposed approach-combining node selection and routing-reduces reconstruction error by more than 48% and increases network lifetime by over 18% compared to existing methods.

Intrusion Detection system protects modern cybersecurity networks through continuous traffic and system activity inspection for identifying potential security threats. Such systems provide organizations with the ability to find and respond instantly to unauthorized access and cyber threats and policy violations. The security of contemporary cybersecurity systems heavily depends on IDS for WSN and ad hoc Wireless Sensor Networks specifically require IDS because traditional security measures fail due to network decentralization. This research paper investigates current intrusion detection approaches designed for ad hoc WSNs while focusing on the distinct security threats DoS, Sybil, and black-hole attacks. The paper examines data gathering strategies and detection methods for IDS models alongside their performance evaluation in WSN environments with limited resources to establish ways for algorithm optimization between security needs and energy efficiency. The recent research should focus on enhancing IDS security while establishing Machine learning methods for behavioral analytics in wireless systems SVM shows best performance as compared to others.

Recently the research is about the life time maximization and the routing in wireless sensor network it is an important issue in today's WSN.To solve these types of problems some of the algorithms and techniques are addressed in this paper and also introduced network architecture. To save the life time in the network .This paper proposed sleep control for the sensor node in the network and connected dominating set based alpha minimum routing cost algorithm for the routing. Wsn is a node which is connected to several or thousands of the sensor in wireless area such as radio transceiver either internal antenna or as external antenna.

Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), support vector machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.

Wireless sensor network (WSN) can be used as a solution to find out the position of an object that cannot be reached by global positioning system (GPS), for example to find out the position of objects in a room known as Indoor Positioning. One method in indoor positioning that can be used is fingerprinting. Inside there are two main work phases, namely training and positioning. The training phase is the process of collecting received signal strength indication (RSSI) data levels from each sensor Node reference that will be used as a reference value for the positioning phase. The more sensor Nodes used, the longer the processing time needed in the training phase. This research focussed on the duration of the training phase, the implementation of which are used 4 sensor Nodes, namely Zigbee (IEEE 802.15.4 protocol) arranged according to mesh network topology, one as Node X (positioning target) and 3 as reference Nodes. There are two methods used in the training phase, namely fixed target parameter (FTP) and moving target parameter (MTP). MTP took 5 seconds faster than FTP in terms of the duration of RSSI data collection from each reference Node.

Due to extensive pipeline dissemination in the oil and gas refinery, the nodes need to be placed in a grid formation. As such, since most oil and gas industry applications require continuous data gathering, a heavy data stream will be introduced in the network traffic, mainly when the network density is high. As a result, performance degradation and poor energy consumption will occur. Ad hoc on-demand distance vector and optimized link state routing protocol have been simulated to investigate these issues further. Due to packet congestion, the network experiences a domino effect on the performance, such as packet loss, throughput degradation, and poor energy consumption. Thus, a tailored solution is required since oil and gas industry relies heavily on sensor data to keep track of pipelines condition to prevent anomalous events from happening. The proposed algorithm has been developed to optimize the network performance by dividing the traffic into two and by reducing the flooding during route discovery. The results have shown better network performance and energy consumption can be achieved using the proposed algorithm when compared to the others.

Advance clustering techniques have been widely used in Wireless Sensor Networks (WSNs) since they can potentially reduce latency, improve scheduling, decrease end-toend delay and optimise energy consumption within a dense network topology. In this paper, we present a novel clustering algorithm for high density IEEE 802.15.4-2015 Time-Slotted Channel Hopping (TSCH). In particular, the proposed methodology merges a variety of solutions into an integrated clustering design. Assuming an homogeneous network distribution, the proposed configuration deploys a hierarchical down-top approach of equally numbered sub-groups, in which the formation of the separate sub-groups is adapted to the network density and the node selection metric is based on the link quality indicator. The presented algorithm is implemented in Contiki Operating System (OS) and several test vectors have been designed in order to evaluate the performance of the proposed algorithm in a COOJA simulation environment. Performance results demonstrate the capability of the clustering structure since compared to the default scheme it significantly improves the energy efficiency up to 35%, packet drops more than 40% as well the packet retransmission rate. Last but not least, the outcome of this study indicates a major increase in the network lifetime, i.e., up to 50%.

Advance clustering techniques have been widely used in Wireless Sensor Networks (WSNs) since they can potentially reduce latency, improve scheduling, decrease end-toend delay and optimise energy consumption within a dense network topology. In this paper, we present a novel clustering algorithm for high density IEEE 802.15.4-2015 Time-Slotted Channel Hopping (TSCH). In particular, the proposed methodology merges a variety of solutions into an integrated clustering design. Assuming an homogeneous network distribution, the proposed configuration deploys a hierarchical down-top approach of equally numbered sub-groups, in which the formation of the separate sub-groups is adapted to the network density and the node selection metric is based on the link quality indicator. The presented algorithm is implemented in Contiki Operating System (OS) and several test vectors have been designed in order to evaluate the performance of the proposed algorithm in a COOJA simulation environment. Performance results demonstrate the capability of the clustering structure since compared to the default scheme it significantly improves the energy efficiency up to 35%, packet drops more than 40% as well the packet retransmission rate. Last but not least, the outcome of this study indicates a major increase in the network lifetime, i.e., up to 50%.

In this paper, we develop optimal scheduling mechanisms for packet forwarding in Wireless Sensor Network, where clusterheads are gathering information with a predefined Quality of Service. The objective is to ensure balanced energy consumption and to minimize the packet loss probability, subject to time constraints (i.e. different nodes must send all their packets within a given time interval). Novel solutions of scheduling are developed by combinatorial optimization, and by quadratic programming methods. In our approach, the scheduling of packet forwarding is broken down to a discrete quadratic optimization problem and the optimum is sought by a Hopfield Neural Network yielding the solution in polynomial time. The scheduling provided by the Hopfield Neural Network indeed guarantees uniform packet loss probabilities for all the nodes and saves the energy of the clusterheads. In this way, the longevity of the network can be increased.

Energy-efficiency of wireless sensor networks (WSN) becomes an essential issue in the research area. This is because of the energy constraints in WSN that depend on a battery, which is difficult to replace or recharge; therefore, multiple clustering algorithms were proposed to achieve efficiency in using the available energy as much as possible. This paper proposed energy efficient and fault-tolerance algorithms that enhance the low energy adaptive clustering hierarchy (LEACH) protocol by three algorithms. The first focuses on selecting the best cluster head, and the second focuses on minimizing the required nodes within the same cluster. Simultaneously, the third fault tolerance algorithm uses software engineering techniques like sleep schedules to increase network lifetime as much as possible. The testing results of the proposed algorithms prove the claim of enhancing the lifetime of WSN. In order to check improvement of lifetime of WSN we have compered the results of the proposed algorithms with standered algorthim. The results show prove the claim of enhancing the life-time of WSN, where the total lifetime of WSN rise from about 550 rounds to reach 4100 when utilized self-checking process and rised up to 5200 after enhance minimum distans.

A new algorithm is described for determining the optimal round-trip paths for two moving sinks in a wireless sensor network. The algorithm uses binary integer programming to select two non-overlapping shortest paths except having a common junction node to cover all the sensor nodes. The two paths are balanced as nearly equal as possible. That is the sensor nodes along each path are equal or differ by just one depending on whether the total number of sensor nodes excluding the junction node is even or odd. In this method, both the path lengths are made equal or very nearly equal while the total length is minimized. This integrated approach is a novel and unique solution to solve the dual moving sink path problem in a wireless sensor network.

Sensor nodes report the sensed data packets to the sink and depending on the application these packets may have diverse attributes: time-critical (TC) and non time-critical (NTC). In such a heterogeneous traffic environment, designing a data dissemination framework that can achieve both the reliability and delay guarantee while preserving the energy efficiency, namely multi-constraint QoS (MCQoS), is a challenging problem. This paper proposes a new aggregate routing model and a localized algorithm (DARA) that implements the model. DARA is designed for multi-sink multipath location aware network architecture. Delaydifferentiated multi-speed packet forwarding and in-node packet scheduling mechanisms are also incorporated with DARA. The simulation results demonstrate that DARA effectively improves the reliability, delay guarantee and energy efficiency.

Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.

Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.

Wireless Sensor Network (WSN) has an enormous prospective in hazardous areas such as underground coal mines. However, there is a need to ensure safety while installing WSN in underground coal mine as it is hazardous in nature and WSN radiates Radio Frequency (RF) signals which can be an eminent source of ignition. Henceforth when the underground coal mines are equipped with WSN there is a need to set the threshold limits of different physical parameters in order to eradicate such hazards for enabling safety. Therefore, in the present chapter, attempts have been made to assess the required safety for WSN while installing in underground coal mines. In addition, various types of hazards associated with underground coal mines and their consequences are elaborated in details with a glimpse to mitigate them with the use of WSN.

The current requirement in Internet of Things (IoT) applications is the use of Wireless Sensor Networks (WSN) which must be both energy-efficient and have secure data transmission. In this paper, a new hybrid system is presented, as well as Distributed Energy-Efficient Clustering (DEEC) protocol was set in conjunction with Elliptic Curve Cryptography (ECC) and Fuzzy Logic or what is called Fuzzy-DEEC Protocol. The protocol that is proposed focuses on improving the performance of WSN by intelligent cluster head selection and secure communications. ECC parameters are also optimized which minimizes computational demand and enhances performance by the usage of Flower Pollination Algorithm (FPA). As shown by our simulation, Fuzzy-DEEC Protocol is better than standard DEEC in several ways, regarding energy consumption, network lifetime, throughput, packet drop rate. In particular, the protocol attains 30-50% longer network life, 15-25% lower energy costs, 25-40% higher throughput, and 35-45% lower packet drop-rate. These benefits are attributed to the fact that selection of cluster heads has been optimised, energy aware routing and secure data transmission is implemented. The effectiveness of this protocol is confirmed with complete performance measures including quantity of active nodes surviving in time, network bandwidth, energy usage and packet drop percentage. This paper prepares the ground to more improved WSN safe and sustainable architecture and thus it is suited to WSN implementation in industrial monitoring, smart agriculture and remote sensing.

Wireless sensor networks (WSN) are increasingly finding their way in diverse applications be it the realization of IoT or real-time monitoring of the environment, health, defense, and even domestic ones. A wireless sensor network consists of a large number of small self-configurable sensor nodes that are capable of performing functions of sensing, processing, and communication of information. Sensor nodes are inherently constrained in terms of energy, computational and communicational capabilities. Most of the sensor nodes are battery-powered with a limited supply of energy. The battery of a sensor node is hard/ impossible to be replaced once deployed. Lifetime of a sensor node or sensor network is concerned with energy, and it is strictly constrained. Most of the energy of a sensor node is consumed during communication, therefore; energy efficiency should be the prime focus while designing the hardware and software of wireless sensor networks. Clustering is an energy optimization technique that can increase the energy efficiency and prolong the network lifetime. Various aspects of clustering presented in this paper include different types of cluster-based networks, clustering process and evaluation metrics. A brief insight into present trends on clustering is also included.

The Internet of Things (IoT) is a technological revolution that has changed everything we do and given us a new perspective on our daily lives, but despite the fact that numerous publications have focused on characterizing the many edges and technologies that make up an IoT system, the IoT ecosystem is still seen as too complex to be recognized as a stand-alone environment due to its significant diversity; hence, the objective of this research is to address such a complex environment in a way that highlights its components and distinguishes them both individually and in relation to their broader context. Therefore, the definition of IoT and its emergence were discussed and organized around the timeline of Internet development phases demonstrating that IoT has been a need that has accompanied the presence of the Internet since its early stages, and then its growth and impact were discussed and highlighted with estimates and numbers. On the technical side, each of the following groups, IoT components, protocols, and architectures, was defined, discussed, and grouped in such a way that their intergroup organization, as well as their placement and contribution to the overall ecosystem, was highlighted. This, in addition to the various examples mentioned throughout the discussion, will provide the reader with a better understanding of the Internet of Things and how deeply it has become entwined in our daily lives and routines as a result of its numerous applications.

Resumo Resumen Abstract Esta pesquisa visa introduzir novos avanços na área da tecnologia da informação e comunicação (TICs) e do seu potencial no desenvolvimento da gestão desportiva. Para este fim, os novos avanços tecnológicos neste domínio são apresentados, liderados por um novo paradigma tecnológico chamado Internet das coisas (IoT), que integra simultaneamente vários desenvolvimentos complementares, como a computação em nuvem (CC), a computação pervasiva, os sistemas de identificação por radiofrequência (RFID) e as redes de sensores sem fio (RSSF), a fim de formar um ecossistema tecnológico capaz de capturar o máximo de informações, simplificar a sua gestão e promover a melhoria de todas as áreas de uma organização desportiva. Além disso, esta pesquisa apresenta várias áreas de implementação, características e potencialidades destas tecnologias a partir de uma perspectiva prática, a fim de divulgar o seu alcance e incentivar a implementação destas novas ferramentas. PALAVRAS C...

It takes early detection and accurate classification of melanoma to lower death rates and improve patient outcomes. But because benign and malignant tumors look the same, misdiagnosis happens a lot, which emphasizes the need for advanced computer techniques that can distinguish between them. Currently available melanoma classification methods often struggle with robustness when dealing with complex and variable lesion patterns. Lack of labelled data complicates training models. This makes original ideas that may effectively address these problems and improve the accuracy of melanoma categorization extremely necessary. CAMLP method is proposed to combine the multilayer perceptrons (MLPs) with conditional adversarial learning to enhance the classification performance of melanoma lesion. While the MLPs serve as the primary classifier, obtaining high-level features from melanoma photos, the conditional adversarial learning approach provides further regularization and improves the ability to distinguish between benign and malignant lesions. The CAMLP approach is demonstrated to be effective in classifying melanoma in experimental data. We achieve 92.5% accuracy, far higher than cutting edge methods. CAMLP exhibits robustness over a range of datasets and in cross-validation tests.

Wireless Sensor Networks (WSNs) are increasingly deployed in mission-critical environments but remain highly vulnerable to Denial of Service (DoS) and unauthorized access attacks due to constrained computational resources. This study proposes a security-enhanced WSN architecture integrated with the ServiceNow cloud platform to provide centralized monitoring, automated incident management, and secure data orchestration. A simulation-based experimental evaluation was conducted using 100 IEEE 802.15.4 sensor nodes under DoS attack scenarios. Results demonstrate that the proposed approach reduces average latency by 18%, improves packet delivery ratio by 12%, and enhances DoS detection rate to 94% compared to traditional standalone WSN security mechanisms. The integration leverages TLS-secured communication and lightweight AES-128 encryption to balance security and energy efficiency. The findings validate that cloud-driven orchestration significantly improves WSN resilience without imposing excessive overhead on sensor nodes.

Energy saving can be achieved in mobility enabled wireless sensor networks that visit sensor nodes and collect data from them through short range communication. The problem that has been faced in WSNs is the increased latency in data collection due to the speed at which the data have been collected. So in order to collect the data efficientlya rendezvous point (RP) is used. Here data are collected by the base station while visiting the rendezvous points. The rendezvous points collect the data which are being buffered from various source nodes which aggregate at a particular pointknown as RP. This work proposes an efficient rendezvous design algorithm with provable performance bounds with mobility and fixed tracking.

Wireless sensor networks (WSNs) have emerged as a critical infrastructure for distributed sensing platforms in recent years. Their effective implementation requires self-organizing features that can adapt to rapidly changing ecological conditions. We have noticed in the comparative study that despite extensive research on individual self-organizing mechanisms, e.g., clustering, routing, and topology management. We believe there exists a significant analytical gap in systematically comparing these approaches across key performance metrics. Our study addresses this gap by conducting a comprehensive comparative analysis of four primary self-organization or autonomious mechanisms: clustering-based organization, dynamic routing protocols, topology adjustment strategies, and coverage reinforcement methods. In our work, using a simulation-based methodology with the NS-3 network simulator, we thoroughly tested these frameworks across networks with 50 to 500 nodes under varying traffic loads and mobility patterns. We assessed the performance using three key KPIs (key performance indicators). Reliability is measured by packet delivery ratio, scalability by convergence time, and energy efficiency by network lifetime parameters. Our results demonstrate that clustering approaches achieve 23% better energy efficiency in static deployments, whereas distributed routing protocols provide 34% better scalability in dynamic conditions. We also observed that topology adjustment mechanisms improve reliability by 18% under high node failure rates. These findings provide clear, evidence-based guidance for selecting the right selforganization technique for specific deployment scenarios and application requirements. We recommend that future research investigate hybrid mechanisms that combine multiple approaches and explore integrating machine learning to support adaptive strategy selection under heterogeneous network conditions.

The rapid growth and development observed in wireless communications has resulted in the overcrowding of the radio spectrum used as a communication medium in wireless networks. In many countries, governmental agencies regulate the use of the radio spectrum by assigning a portion of the spectrum in the form of a renewable license to any company or person who wishes to use the wireless medium for communication purposes; this is in order to allow efficient utilization of this scarce resource. The need to close the digital divide means that cost effective communication technologies should be deployed in rural areas for wireless broadband connectivity. Cognitive Radio (CR) is seen as a technology that will bring a new communication paradigm to address spectrum scarcity in the most energy efficient manner. In this paper we present the proposed work to be carried out as part of a doctoral thesis to address the spectrum scarcity and transmission power in energy constrained rural areas.

A wireless sensor network (WSN) is a self organizing wireless network system consists of number of energy limited sensor nodes. Since sensor nodes are battery powered devices, they have limited processing and transmission power and with limited communication ability. In order to transmit sensing data to receiver effectively, it is necessary to design an energy efficient routing protocol for WSNs. The nodes with greater signal strength will have more communication link and result in faster energy consumption as a whole network cannot always work under the same topology structure. A topology reconnecting mechanism of the cluster head rotation algorithm is needed. This paper presents an Enhanced Forward Aware Factor -Energy Balanced Routing Method (EFAF-EBRM) based on the selection of appropriate cluster head to improve the energy efficiency . In this technique the sensor node may prefer to select cluster head that makes the total energy consumption least.This proposed cluster formatio...

Localization is the basic problem in the wireless sensor networks. All the result data get wasted if the node is placed in the wrong position. Range based and Range free algorithms are used for locating the node in the wireless sensor network. But in the existing system they will use any one of the algorithm. The method CDL-combined and Differentiated Localization approach inherits the properties of both Range based and Range free algorithm. CDL provides the location accuracy efficiently but still it has location error in large scale networks. To overcome this problem the novel, geographic routing method named Conditioned Mean Square Error Ratio (CMSER) routing, intended to route packets when the localization error is occurred.

Objectives: To propose a novel bio-inspired self-healing Quantum Swarm Immunization (QSI) and intelligent optimization protocol for holistic security and trust-aware routing in multi-layered WSNs. The model also intends to improve energy efficiency, defense against malicious node behavior, minimizes packet loss and transmission delay. Methods: The proposed QSI-Fusion model integrates quantum superposition and Immune-Inspired Adaptive Selection (IISS) with swarm intelligence to achieve optimal and trust-aware routing. QSI-Fusion works with a three-layer hybrid architecture: i) Quantum Behavioral Layer (QBL) for anomaly detection, ii) Immunized Trust Layer (ITL) for adaptive selection, and iii) Secure Swarm Routing Layer (SSRL), which enables energyefficient and attack-resilient connections between source and destination. This multi-layer quantum swarm optimization process dynamically adjusts the learning parameters, including a) trust threshold, b) node selection, and c) energy-aware transmission weights, to enhance performance. NS-3 is used to evaluate the performance under variable node densities and attack scenarios. The results are compared with existing energy-efficient and trust-aware bioinspired models such as MC-CRITIC, Bio-Inspired Models (PSO, ACO, BO, WO, FCM, AC), WQALO, and QEBSO. NSL-KDD & DS2OS IoT datasets are used for validation to ensure generalization under heterogeneous WSN-IoT environments. Findings: The proposed QSI-Fusion achieves promising results compared to existing models, attaining a 98.4% Fault Detection Rate (FDR), 98.6% Accuracy (AC), 97.9% Energy Efficiency (EE), a reduced delay of 0.021 s, and 96.5% Intrusion Resistance (IR). The execution time is reduced to 27%. Novelty: A unified integration of quantum intelligence, immunological learning, and bio-inspired swarm forms a multi-layer security ecosystem that helps to secure data in complex WSN environments. The proposed model establishes an energy-efficient, trust-aware, and self-adaptive WSN system suitable for next-generation applications.

Solid waste management is one of the existing challenges in urban areas and it is becoming a critical issue due to rapid increase in population. Appropriate solid waste management systems are important for improving the environment and the well-being of residents. In this paper, an Internet of Things(IoT) architecture for real time waste monitoring and collection has been proposed; able to improve and optimize solid waste collection in a city. Netlogo Multi-agent platform has been used to simulate real time monitoring and smart decisions on waste management. Waste filling level in bins and truck collection process are abstracted to a multi-agent model and citizen are involved by paying the price for waste collection services. Furthermore, waste level data are updated and recorded continuously and are provided to decision algorithms to determine the vehicle optimal route for waste collection to the distributed bins in the city. Several simulation cases executed and results validated. The presented solution gives substantial benefits to all waste stakeholders by enabling the waste collection process to be more efficient

Solid waste management is one of the existing challenges in urban areas and it is becoming a critical issue due to rapid increase in population. Appropriate solid waste management systems are important for improving the environment and the well-being of residents. In this paper, an Internet of Things(IoT) architecture for real time waste monitoring and collection has been proposed; able to improve and optimize solid waste collection in a city. Netlogo Multi-agent platform has been used to simulate real time monitoring and smart decisions on waste management. Waste filling level in bins and truck collection process are abstracted to a multi-agent model and citizen are involved by paying the price for waste collection services. Furthermore, waste level data are updated and recorded continuously and are provided to decision algorithms to determine the vehicle optimal route for waste collection to the distributed bins in the city. Several simulation cases executed and results validated. The presented solution gives substantial benefits to all waste stakeholders by enabling the waste collection process to be more efficient

Recent advances in miniaturization and low power design have led to a flurry of activity in wireless sensor networks. Sensor networks have different constraints than traditional wired networks. A wireless sensor network is a special network with large numbers of nodes equipped with embedded processors, sensors, and radios. These nodes collaborate to accomplish a common task such as environment monitoring or asset tracking. In many applications, sensor nodes will be deployed in an ad-hoc fashion without careful planning. They must organize themselves to form a multihop, wireless communication network. The results contained in this dissertation have been published in the following forums (international journals and conferences).

Wireless sensor networks (WSN) are increasingly finding their way in diverse applications be it the realization of IoT or real-time monitoring of the environment, health, defense, and even domestic ones. A wireless sensor network consists of a large number of small self-configurable sensor nodes that are capable of performing functions of sensing, processing, and communication of information. Sensor nodes are inherently constrained in terms of energy, computational and communicational capabilities. Most of the sensor nodes are battery-powered with a limited supply of energy. The battery of a sensor node is hard/ impossible to be replaced once deployed. Lifetime of a sensor node or sensor network is concerned with energy, and it is strictly constrained. Most of the energy of a sensor node is consumed during communication, therefore; energy efficiency should be the prime focus while designing the hardware and software of wireless sensor networks. Clustering is an energy optimization technique that can increase the energy efficiency and prolong the network lifetime. Various aspects of clustering presented in this paper include different types of cluster-based networks, clustering process and evaluation metrics. A brief insight into present trends on clustering is also included.

The major challenge for wireless sensor networks is energy consumption minimization. Wireless transmission consumes much more of energy. In the c lustered network, a few nodes become cluster heads which causes the energetic heterogeneity. Therefore the behavior of the sensor network becomes very unstable. Hence, the need to apply the balancing of energy consumption across all nodes of the heterogeneous network is very important to prevent the death of those nodes and thereafter increase th e lifetime of the network. DEEC (Distributed Energy E fficient Clustering) is one of routing protocols designed to extend the stability time of the networ k by reducing energy consumption. A disadvantage of DEEC, which doesn’t takes into account the cluster ize and the density of nodes in this cluster to el ect the cluster heads. When multiple cluster heads are rand omly selected within a small area, a big extra ener gy loss occurs. The amount of lost energy is approxima tely proportional to the n...

Wireless Sensor Networks (WSN) consist of miniature sensor nodes that communicate among themselves via wireless channels, often in an unfriendly environment, and nodes can be carried and defeated. Thus, an enemy may also attack the clones by copying the nodes taken and broadening the breaching areas with the help of clones. Hence, to reduce the losses of clone nodes to the WSNs, it is crucial to detect them as soon as possible. Other types of clone detection systems have been proposed in the recent past for WSNs, bearing in mind the dissimilar types of network structures, such as deployment strategies and types of devices. The Deep Learning (DL) techniques, however, are used to identify and clone nodes in WSN. A Hybrid Optimization-Based Feature Learning is presented in this paper regarding Clone Detection Using Ensemble Learning Models (HOFLCD-ELM). The project seeks to create and assess an effective clone detection technique in wireless sensor networks to improve network security and integrity. The initial phase of data preprocessing is the min-max normalization approach, which transforms raw data into a usable format for modeling. In the feature subset selection procedure, the proposed HOFLCD-ELM model develops a hybrid optimization process in the form of Lyrebat Algorithm (LYBA) that integrates Lyrebird Optimization Algorithm (LOA) and Bat Algorithm (BA) in order to find the optimal features within a dataset. Subsequently, the system of Deep Belief Network (DBN) model, Convolutional Variational Autoencoder (CVAE) method, and Graph Convolutional Network (GCN) has been implemented to identify and classify clone attacks. Lastly, the optimization process of the Spider Wasp (SWO) model is used to acquire the parameter tuning process in enhancing the classification of the ensemble classifier. The experimental analysis of the HOFLCD-ELM model is done through a benchmark and a dataset. The results of the empirical study showed that the performance of the HOFLCD-ELM method was improved more than that of the current methods.