Wireless Sensor Networks Domain Research Articles

GREPHRO: Nature-inspired optimization duo for Internet-of-Things

The optimization techniques usually work with the maximization or minimization of the problem to obtain the local loci or cumulative global loci. Two-dimensional bio-inspired optimization techniques face convexing problems towards a global solution and use an increased number of iterations. Besides, the duality principle considers the dual optimization aspects of the problems leading to a large duality gap of uncertain deviations and optimization errors between any prime solution and its dual solution. Moreover, several problems exist where one objective function requires maximization and another objective function requires minimization using the same set of parameters and some chaining of the feedback process. In such cases, we generally use two different optimization problems as per the best suit to the problem environment and obtain the different sub-solutions of the individual problems. This increases the complexity of the system and often deviates from the original optimal solution. We address these problems of dual optimization in our present work.In this paper, we introduce the first optimization duo model for computing services. To be specific, our proposed model is the first optimization model that works in a dual combined mode with maximization and minimization simultaneously to obtain a global optimum value or loci. We call our model GREen PHotosynthesis and Respiratory-based Optimization (GREPHRO). GREPHRO is primarily motivated by the observation of plants’ photosynthesis and respiratory processes, which work on the same set of environmental variables and have a chaining process. Further, our proposed nature-inspired GREPHRO can value the global optima or infimum point considering a single objective function serving as maximization and minimization combined. GREPHRO uses the Lagrange dual principle and non-linear parameters to obtain a linear solution for the infimum optima. We use a set of experiments on the GREPHRO model in the domain of Wireless Sensor Network (WSN)-based Internet of Thing (IoT) to derive the use case of our proposed work. The experimental results and the comparative analysis with the two of our previous works show that GREPHRO takes fewer iterations with more stability of the optimum solution. Moreover, the computational and memory complexities are also less. Therefore, GREPHRO is efficient and suitable for two-dimensional optimization problems in resource-constrained environments for IoTs.

Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study

Energy prediction plays a significant role in energy-harvesting wireless sensors (EHWS), as it helps wireless sensors regulate their duty cycles, achieve energy neutrality, and extend their lifespan. To explore and analyze advanced technologies and methods regarding energy prediction for EHWS, this study identifies future research directions and addresses the challenges faced based on the current research status, assisting with future literature research. This scholarly inquiry delineates future research prospects and addresses prevailing challenges within the context of the extant research landscape, thereby facilitating prospective scholarly endeavors. This study employed the systematic mapping study (SMS) approach to screen and further investigate the relevant literature. After searching and screening for papers from the ACM, IEEE Xplore, and Web of Science (WOS) databases from January 2007 to December 2022, 98 papers met the requirements of this study. Subsequently, the SMS was conducted for five research questions. The results showed that the solution proposal type category had the largest proportion among all research types, accounting for 58% of the total number, indicating that the research focusing on this field is placed on improving the existing methods or proposing new ones. Additionally, based on the SMS analysis, this study provides a systematic review of the technical utilization and improvement approaches, as well as the strengths and limitations of the selected prediction methods. Furthermore, by considering the current research landscape, this paper identifies the existing challenges and suggests future research directions, thereby offering valuable insights to researchers for making informed decisions regarding their chosen paths. The significance of this study lies in its contribution to driving advancements in the field of energy-harvesting wireless sensor networks. The importance of this study is underscored by its contribution to advancing the domain of energy-harvesting wireless sensor networks, thereby serving as a touchstone for forthcoming researchers in this specialized field.

Accurate Range Free Location based Partial Derivative and Stochastic Feedforward Neural Network Hyperbolic-based Agriculture Sensor Network Formation

Wireless sensor networks (WSN) authorize the control of different source environmental aspects and crop states as far as precision agriculture is concerned. Nevertheless, the complicated agricultural environment brings about the WSN topology to change often and hence link association likelihood is laborious to predict. Information pertaining to agriculture is an essential distress for localization-based service in the domain of WSNs. Smooth planning and control as the most typical range-free localization method localization performance is said to be good in even distributed networks. Nevertheless, it demonstrated extremely poor accuracy under that in an urgent issue that required to be addressed. In this work a novel topology construction method called, Partial Derivative Laurent Approximation and Stochastic Feedforward Hyperbolic (PDLA-SFH) based Agriculture Sensor Network Formation is proposed. The proposed method is split into three steps. They are agriculture sensor topology construction, average hop size distance validation and position estimation. First topology construction is performed by employing Game Theory Partial Derivative Regression Coefficient-based Topology model. Second, Laurent Approximation-based Hop Size Distance validation model is designed for optimal topology formation. Finally the Stochastic Feedforward Hyperbolic-based Position Estimation is modeled. The simulation results performed in NS3 showed that the proposed localization algorithms can attain better localization performance in terms of accuracy, time and error rate in comparison with other existing methods such as basic Digital Twins and Autonomous Groups Particles Swarm Optimization (AGPSO) under distinct arbitrary network topologies.

Design and Analysis of a Multiple and Wide Nulling Collaborative Beamforming Scheme in the Domain of 3-Dimensional Wireless Sensor Networks

Null steering is essential in collaborative beamforming (CB) in wireless sensor networks (WSNs) to ensure minimal radiation power and interference in the direction of unintended receivers. Current research in null steering in CB in WSNs is mainly from the perspective of planar arrangements of sensor nodes and sink(s). Furthermore, there is no research dedicated to the formation of multiple wide nulls during CB in 3-dimension WSNs. Wide nulls are ideal in scenarios featuring mobile unintended sink(s). A new multiple and wide null steering scheme applicable to CB in WSNs is presented in this work (from the perspective of a 3-dimensional random arrangement of static sensor nodes). It is assumed that desired nulling directions are implicitly known at a CB cluster head. A particle swarm optimization (PSO) algorithm variant is applied in concurrent node transmit amplitude and phase perturbation with an aim of achieving beam steering alongside multiple and wide null steering. The performance of the proposed null steering scheme is validated against a basic null steering approach (with reference to current literature). Furthermore, a comparative null depth, width, and nulling accuracy analysis are done upon varying the count of collaborating nodes and the collaborating cluster radius. An increase in the number of collaborating nodes is found to increase nulling depth at an exponentially decaying rate. An increase in the collaborating nodes’ cluster radius yields a reduction in null width. The contributions of this work to the existing literature are as follows: (i) the design and investigation of a null steering scheme from the perspective of a 3-dimension random arrangement of sensor nodes; (ii) the design of a concurrent beam steering and multiple wide null steering scheme on the basis of concurrent node transmit amplitude and phase perturbation whilst ensuring null depth uniformity; (iii) a statistical analysis of the impact of a count of collaborating nodes and collaborating cluster radius on nulling performance; (iv) investigation of capacity improvement at unintended receivers upon null steering.

A novel energy efficient routing scheme for wireless sensor networks

In the current rapid developing and smart world, the wireless sensor networks domain is among the most emergent fields with plenty of applications such as healthcare, wildlife, environmental monitoring, defense, and landslide detection. Distribution of cluster heads along with the selection of cluster heads is essentially vital for optimizing various network performance parameters. In this paper, a novel energy efficient routing scheme based on probabilistic sector wise clustering has been proposed, which results in energy saving in conjunction with superior network lifetime for wireless sensor networks. In the proposed routing technique, the sensing field is divided into multiple sectors and clusters in five different types. Nodes in the sector can communicate directly with the base station in one-hop or through the gateway node in two-hops. Nodes in the cluster communicate to the base station through the cluster head in three-hops. The simulation results of all five types are compared in terms of their life span. Life span of the network is analyzed by considering the round number in which the first node dies, the round number till which 80% of the nodes are alive, 50% of nodes are alive and 20% of the nodes are alive.

Intelligent Chimp Metaheuristics Optimization with Data Encryption Protocol for WSN

Recent developments in low power electronic devices integrated into wireless communication technologies resulted in the domain of wireless sensor networks (WSN), which finds in applications in diverse data gathering and tracking applications. Since WSN is mostly deployed in harsh and inaccessible environments, it is necessary to design energy efficient and security solutions. The clustering technique is an effective way to lengthen the lifetime of WSN. But most of the clustering techniques elect cluster heads (CHs) irrespective of clusters. To resolve this issue, this paper presents a new intelligent metaheuristics based energy aware clustering with data encryption protocol (IMEAC-DEP) for WSN. The goal of the IMEAC-DEP technique is to elect an appropriate set of CHs and CHs to encrypt the data prior to intercluster communication. The proposed IMEAC-DEP model involves two major phases namely clustering and data encryption. At the first stage, a new chimp optimization algorithm based clustering (COA-C) technique is derived with four fitness parameters. Next, in the second stage, signcryption with artificial fish swarm optimization algorithm (SC-AFSA) based data encryption technique is derived. In order to validate the performance of the proposed IMEAC-DEP model, a series of experiments were performed and the results are investigated under different aspects. The resultant experimental values highlighted the superior performance of the IMEAC-DEP model over the recent stage of art methods interms of energy efficiency as well as security.

Data Collection in Studies on Internet of Things (IoT), Wireless Sensor Networks (WSNs), and Sensor Cloud (SC): Similarities and Differences

Data collection is an essential part of sensor devices, particularly in such technologies like Internet of Things (IoT), wireless sensor networks (WSN), and sensor cloud (SC). In recent years, various literature had been published in these research areas to propose different models, architectures, and contributions in the domains. Due to the importance of efficient data collection regarding reducing energy consumption, latency, network lifetime, and general cost, a momentous literature volume has been published to facilitate data collection. Hence, review studies have been conducted on data collection in these domains in isolation. However, a lack of comprehensive review collectively identifies and analyzes the differences and similarities among the data collection proposals in IoT, WSN, and SC. The main objective of this research is to conduct a comprehensive survey to explore the current state, use cases, contributions, performance measures, evaluation measures, and architecture in the IoT, WSN, and SC research domains. The findings indicate that studies on data collection in IoT, WSN, and SC are relatively consistent with stable output in the last five years. Nine novel contributions are found with models, algorithms, and frameworks being the most utilized by the selected studies. In conclusion, key research challenges and future research directions have been identified and discussed.

An multilevel efficient energy clustering protocol with secure routing (MEECSR) in WSNs

ABSTRACT Nowadays the interest of research by the researchers is dominating in various areas of wireless sensor networks (WSNs) domain. The task of reducing energy consumption and spreading WSN lifetime are very impressive. Increase of energy stability and developing an advanced routing mechanism is one of the main issues in WSNs. There is a rising concern about protection of data from sensors and these low power devices are not suitable to complex cryptographic algorithms. Hence, the lifetime optimization and security are two conflicting design issues that are not filled to multi-hop wireless sensor networks. This paper presents a multilevel efficient energy clustering protocol with secure routing (MEECSR) protocol with two adjustable parameters: energy balance mastery and probabilistic random walking to avoid the above conflicts. The protocol for energy consumption of a given network topology is severely displaced, which greatly reduces the lifetime of the sensor networks. Also a quantifiable security method is proposed to address the source location privacy. The protocol provides an admirable trade-off between routing efficiency and energy balance can significantly extend the lifetime of the sensor networks in all scenarios. Further, the non-uniform energy deployment shows an increase in the lifetime of the network and the total number of packets delivered. This paper shows the simulation results of security and energy efficiency with different parameters.

An multilevel efficient energy clustering protocol with secure routing (MEECSR) in WSNs

ABSTRACT Nowadays the interest of research by the researchers is dominating in various areas of wireless sensor networks (WSNs) domain. The task of reducing energy consumption and spreading WSN lifetime are very impressive. Increase of energy stability and developing an advanced routing mechanism is one of the main issues in WSNs. There is a rising concern about protection of data from sensors and these low power devices are not suitable to complex cryptographic algorithms. Hence, the lifetime optimization and security are two conflicting design issues that are not filled to multi-hop wireless sensor networks. This paper presents a multilevel efficient energy clustering protocol with secure routing (MEECSR) protocol with two adjustable parameters: energy balance mastery and probabilistic random walking to avoid the above conflicts. The protocol for energy consumption of a given network topology is severely displaced, which greatly reduces the lifetime of the sensor networks. Also a quantifiable security method is proposed to address the source location privacy. The protocol provides an admirable trade-off between routing efficiency and energy balance can significantly extend the lifetime of the sensor networks in all scenarios. Further, the non-uniform energy deployment shows an increase in the lifetime of the network and the total number of packets delivered. This paper shows the simulation results of security and energy efficiency with different parameters.

Q-Learning based Routing Protocol to Enhance Network Lifetime in WSNs

In resource constraint Wireless Sensor Networks (WSNs), enhancement of network lifetime has been one of the significantly challenging issues for the researchers. Researchers have been exploiting machine learning techniques, in particular reinforcement learning, to achieve efficient solutions in the domain of WSN. The objective of this paper is to apply Q-learning, a reinforcement learning technique, to enhance the lifetime of the network, by developing distributed routing protocols. Q-learning is an attractive choice for routing due to its low computational requirements and additional memory demands. To facilitate an agent running at each node to take an optimal action, the approach considers node’s residual energy, hop length to sink and transmission power. The parameters, residual energy and hop length, are used to calculate the Q-value, which in turn is used to decide the optimal next-hop for routing. The proposed protocols’ performance is evaluated through NS3 simulations, and compared with AODV protocol in terms of network lifetime, throughput and end-to-end delay.

An active model for ranging by deep convolutional neural network and elephant herding optimization algorithm (DCNN-EHOA) in WSNs

PurposeIn modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.Design/methodology/approachThis paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem.FindingsIn this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services.Originality/valueThe proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.

A behavior‐driven reliability modeling method for complex smart systems

AbstractWith recent advancements in Internet technologies and wireless communications, wireless sensor network (WSN)‐based smart systems are gaining an enormous increase in use in various applications (eg, healthcare, smart home, smart manufacturing, smart power grids, and smart transportation). Due to the mission‐critical or safety‐critical nature of smart system applications, it is imperative that a smart system be reliable during its mission time. However, reliability analysis and design of smart systems are still open challenging research problems due to complicated dependencies existing in the WSN domain or the physical domain monitored by the WSN, and due to dependencies crossing the two domains. This paper proposes a new behavior‐driven reliability modeling methodology for accurate and efficient reliability analysis of complex WSN‐based smart systems, contributing toward their robust designs and operations. The suggested method can address effects of dependent behaviors affecting different domains of the smart system in a combinatorial manner. It also enables the use of efficient single‐domain reliability methods to retain their efficiency. A case study of a smart home system is performed to demonstrate the application of the proposed method as well as its advantages in handling nonexponential time‐to‐failure distributions and in analyzing smart systems with complicated intradomain and cross‐domain dependencies.

Metaheuristic Clustering Protocol for Healthcare Data Collection in Mobile Wireless Multimedia Sensor Networks

Nowadays, healthcare applications necessitate maximum volume of medical data to be fed to help the physicians, academicians, pathologists, doctors and other healthcare professionals. Advancements in the domain of Wireless Sensor Networks (WSN) and Multimedia Wireless Sensor Networks (MWSN) are tremendous. M-WMSN is an advanced form of conventional Wireless Sensor Networks (WSN) to networks that use multimedia devices. When compared with traditional WSN, the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content. Hence, clustering techniques are deployed to achieve low amount of energy utilization. The current research work aims at introducing a new Density Based Clustering (DBC) technique to achieve energy efficiency in WMSN. The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level, distance, and node centrality. In addition, two static data collector points called Super Cluster Head (SCH) are placed, which collects the data from normal CHs and forwards it to the Base Station (BS) directly. SCH supports multi-hop data transmission that assists in effectively balancing the available energy. A detailed simulation analysis was conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects. The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds, which is significantly higher compared to existing methods.

A Survey on Underwater Wireless Sensor Networks: Requirements, Taxonomy, Recent Advances, and Open Research Challenges.

The domain of underwater wireless sensor networks (UWSNs) had received a lot of attention recently due to its significant advanced capabilities in the ocean surveillance, marine monitoring and application deployment for detecting underwater targets. However, the literature have not compiled the state-of-the-art along its direction to discover the recent advancements which were fuelled by the underwater sensor technologies. Hence, this paper offers the newest analysis on the available evidences by reviewing studies in the past five years on various aspects that support network activities and applications in UWSN environments. This work was motivated by the need for robust and flexible solutions that can satisfy the requirements for the rapid development of the underwater wireless sensor networks. This paper identifies the key requirements for achieving essential services as well as common platforms for UWSN. It also contributes a taxonomy of the critical elements in UWSNs by devising a classification on architectural elements, communications, routing protocol and standards, security, and applications of UWSNs. Finally, the major challenges that remain open are presented as a guide for future research directions.

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.

Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling.

Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E2S-DRL) algorithm in WSN. E2S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. E2S-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, E2S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our E2S-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods.

Collaborative Functioning for Underwater Acoustic Sensor Communication using AURP (AUV-Assisted Underwater Routing Protocol)

Security has become one of the most essential requirements in any application domain of wireless sensor networks. It is highly required so that we can send data to an authenticated person on a secure channel ensuring all the aspects of security features like confidentiality, integrity, authentication, etc. are provided. But it is more essential in a domain like underwater acoustic sensor networks where the bandwidth is severely limited, the energy of sensors is limited and the propagation delay is very high and hence we have limited resources that must be utilized efficiently. In order to address these challenges, the proposed work focuses on a mobile agents based architecture consisting of secure co-operation of AURP (An AUV-aided underwater routing protocol) with the objective of providing maximum data delivery and minimum energy consumption. Further these mobile agents are encrypted and embedded in the AUV’s in their buffer for the data capturing process as these mobile agents can travel throughout the network via controlled AUV's. This work uniquely contributes a mobile agent based periodic approach for collecting data from various gateway nodes (cluster heads) of different clusters as specified in its itinerary using travelling sales person so that it covers all of them using an optimal amount of energy & processing them one by one and completing its itinerary and depositing the data to a sink and then repeating the same process again periodically when these AUV’s gets charged exploiting a solar energy. Each cluster has some members within one hop distance that have sensed some data which needs to be sent to a gateway node using encrypted mobile agents and only when some AUV's services are provided to that particular gateway node.

Secure Routing Protocols for Source Node Privacy Protection in Multi-Hop Communication Wireless Networks

Traffic analysis attacks are common in monitoring wireless sensor networks (WSNs). In the attacks, adversaries analyze the traffic pattern to obtain critical information such as the location information of a source node. Fake source packet routing protocols are often used to ensure source location privacy (SLP) protection. The protocols rely on broadcasting fake packets from fake sources concurrently with the transmission of real packets from the real source nodes to obfuscate the adversaries. However, fake source packet routing protocols have demonstrated some performance limitations including high energy consumption, low packet delivery ratio (PDR), and long end-to-end delay (EED). In this study, two existing fake source packet routing protocols are considered. Then two new phantom-based SLP routing protocols are proposed to address the limitations. Each proposed protocol introduces a two-level phantom routing strategy to ensure two adversary confusion phases. When the adversaries perform traffic analysis attacks on the packet routes, they encounter two levels of obfuscation. Simulation results establish that the proposed protocols have superior performance features. The protocols guarantee strong SLP protection throughout the WSN domain with controlled energy consumption, PDR, and EED. Furthermore, the proposed protocols achieve more practical results under varied network configurations.

Energy Efficient MAC Protocol for Heterogeneous Wireless Sensor Network using Cross-Layer Design

Over the last decade, Wireless Sensor Network (WSN) has gained considerable attention in various real-time applications. Since WSN works with battery operated nodes, utilization and optimization of node energy is one the primary challenge. For effectively handling and controlling the energy consumption problem in WSN, cross-layer optimization is one of the important methods. For a researcher working in the domain of WSN, energy constraint is a huge challenge to deal with. MAC layer is one of the major source of energy consumption so, an innovative energy efficient MAC protocol using a cross-layer approach in the heterogeneous wireless sensor network is proposed. In this protocol, first, packet retransmission by reducing packet loss is addressed by considering buffer space, channel state and remaining energy. Second, the synchronization scheme for a global schedule is implemented by deliberating adaptive listening using the length of the transmission queue. Finally, sleep time issue is worked out to reduce energy consumption. In this scenario, the nodes will be in sleep mode unless it has some packets to send or receive. The proposed protocol is implemented in Network Simulation (NS2). The simulation results show that the heterogeneous wireless network performs better in terms of energy consumption, packet data rate and energy buffer state while implementing through the proposed protocol.

The Amalgamative Sharp Wireless Sensor Networks Routing and with Enhanced Machine Learning

Wireless sensor networks (WSN) are most prominent domain in present networking world. Sensors are the devices used to sense the information transfer, routing, processing time, energy calculation and also to know the physical and environmental conditions. Many researchers worked on WSN to improve the performance of the sensors. Various algorithms are discussed about routing in WSN, information transfer and many sub domains. Machine Learning is used to control the process without any human interaction. In this paper, the amalgamative sharp WSN algorithm is introduced to show the performance based on information transfer, routing, processing time, and energy calculation. For the better results the ML algorithms are adopted with proposed algorithm. Results show the performance of the ASWSN algorithm.