Narrow Results

Wake-up receivers (WuRxs) allow wireless sensor nodes to run on battery power while maintaining asynchronous, low-latency communication. This paper focuses on WuRxs based on low-frequency pattern matchers (LFPMs). Many recent studies either investigate physical WuRx implementations or simulate WuRx-based protocols. Our goal is to address the challenges that arise when realizing WuRx-based protocols in hardware. These challenges are, that a packet activates unwanted WuRxs, an unreliable address space, and missing cluster broadcast capabilities. The proposed separation sequences and run-length limited patterns ensure a reliable address space. WuRxs based on LFPMs use a fixed pattern matching. Cluster broadcasts are enabled by the proposed variable Manchester coding. Typically, LFPMs use Manchester coding with an efficiency of only 0.5 bit/symbol. We introduce two non-Manchester coding techniques with higher efficiency: lookup table-based coding with an efficiency of 0.71 and 3S2B coding with an efficiency of 0.67.

Hotels streamline operations, provide more personalized service, streamline communication, automate procedures, and gather feedback for ongoing development using modern digital technologies like customer care chatbots, mobile guest applications, and property management systems. Batch, sequential, and incremental learning all make heavy use of the Extreme Learning Machine (ELM) because of its efficient and rapid learning rate, rapid convergence, strong generalizability, and simplicity of implementation. By automating repetitive tasks, Artificial Intelligence (AI) improves the hospitality sector’s efficiency and quality on service it provides to customers and employees. Using AI-driven automated systems, hotels may improve inventory management, track and manage energy use, and simplify cleaning schedules. The challenging characteristics of smart flood detection and alert systems are that they lack expertise, streamline operations and provide personalized guest services. Hence, in the proposed method, the Artificial Intelligence Environment enabled Wireless Sensor Network (AIE-WSN), which integrates a hotel digitization effect in an ELM. In addition, case studies and expert interviews provide practical insights into how hotels have used and benefited from financial management systems driven by AI. The research shows that hotels may greatly benefit from AI tools, including personalized pricing strategies, spending monitoring systems, fraud detection algorithms, and predictive analytics when making financial decisions through WSN. The hotel industry is undergoing digitalization, adopting AI-WSN solutions to track products’ provenance and monitor their history, among the many benefits of the proposed approach. It can help with supply chain management and adopting more efficient practices.

In recent years, the axial radiation model has emerged as a pivotal framework for Wireless Sensor Networks (WSN), particularly in enhancing intelligent sensing platforms. This study delves into the WSN structured around the axial radiation model, encompassing critical aspects like model reconstruction, node deployment, and routing optimization. Our focus is on evaluating the performance metrics that influence the responsiveness of these intelligent platforms. We introduce the Multi-block Directed Radiation (MBDR) routing algorithm, designed to extend system operational time and boost data transmission efficiency. Comprehensive experimental analyses demonstrate the significant advancements of MBDR in survivability, data transmission rates, and regional balance within central axis radiation model environments.

Wireless sensor networks have been extensively studied for their potential applications in both civil and military domains. However, due to their inherent resource constraints, sensor nodes are highly susceptible to cyber threats, particularly worm attacks, which pose significant security challenges. This study explores the dynamics of potential worm attacks within wireless sensor networks using a compartmental epidemic model. The model analyzes the temporal evolution of worm propagation while effectively capturing both spatial and temporal aspects. We determine the reproduction number and equilibrium of the system, with the local stability assessed using the Jacobian matrix. The linear growth and Lipschitz conditions are used to establish the existence and uniqueness of the solution. Furthermore, the Hyers–Ulam stability of the proposed model is also evaluated within the context of the fractal-fractional operator. Finally, a numerical method is developed to investigate the dynamic behavior of the wireless sensor network model under fractal-fractional orders, providing valuable insights into its robustness and security against potential worm attacks.

Wireless sensor networks (WSNs) are a powerful support system for the fundamental infrastructure that is required to monitor physiological and activity parameters (WSN). Wearable devices, which are also referred to as wireless nodes in the scientific world, are what are used in order to measure one or more of the user’s vital signs. Each and every wireless node is a teeny-tiny device that is meant to be supplied with enough amounts of storage space, power, and transmission capability. The loss of data packets may occur during the transmission of data via a wireless medium for a number of reasons. These reasons include interferences, improper deployment circumstances, distance, and inadequate signal strength. The monitoring of a user’s physiological information and postural activity information in various applications, such as home care and hospital care, is the primary emphasis of this study. In this work, the WSN was shown thanks to the introduction of wireless sensor nodes that were created locally. These wireless sensor nodes are used in the process of analyzing many aspects of a network, such as the received signal strength, transmission offset, packet delivery ratio (PDR), and signal-to-noise interference. The work significantly improves the capabilities of conventional WSN by implementing a variety of alternative communication approaches, such as network-coded cooperative communication (NC-CC) and cooperative communication (CC). The system that is being shown makes it feasible to localize the user’s approximate position inside an indoor setting without making use of any camera network connections. This is made possible by the system’s ability to determine the user’s location via triangulation. This is one of the benefits that the system provides. A hospital sensor network, an example of which is being shown here, is capable of doing real-time monitoring of a patient’s postural activity as well as their general health. The method is being promoted in order to ensure that the patient will get assistance in a timely manner that is adequate to his/her needs. Involving NC-CC enables the effective sharing of real-time data among the group of privileged duty nurses while simultaneously minimizing the amount of network traffic, latency, and throughput. This is possible because of NC-CC protocol. The findings of the experiments showed that the proposed method of communication, which is known as dynamic retransmit/rebroadcast decision control, is a significant advancement in the network coding approach that is presently being utilized. This was demonstrated by the fact that the method was shown to be significantly more effective.

In wireless sensor networks (WSNs), barrier coverage is used to detect objects crossing a protected area or to monitor an area of interest. It is a critical application within WSNs. Due to cost considerations, sensor nodes are often randomly deployed along the boundary of the monitoring area, constructing multiple barriers to maximize network lifetime. These barriers operate based on a sleep-wakeup schedule. However, a new security issue, known as the barrier-breach problem, can arise during this schedule. In this paper, we propose a novel barrier coverage construction algorithm (BCC_VL) that addresses the barrier-breach problem using Virtual Lines (VLs), a concept that has not been explored before. By employing VLs, our algorithm can construct a barrier coverage without any breaches. We conducted simulations to validate the proposed method, and the results were compared and verified against the MaxFlow model. In the MaxFlow_RM method, our approach demonstrated superior performance in identifying barrier coverage.

The main idea of this framework is that it is capable of overcoming the drawbacks that are always linked with conventional cloud-based methods. Computation and storage resources in Internet of Things (IoT) networks are distributed closer to the network’s edge; therefore, the amount of data processed in real time is reduced. By decreasing the distance of the data transfers, less bandwidth is used. Structural problems, data safety, interoperability, and resource allocation-related matters denote challenges preventing the successful implementation of those ideas. The proposed work is the cloud-enabled fog computing framework (C-FCF) in data center systems based on the IoT platform. It brings cloud computing to a new level of scalability by compounding the following: Scalable architecture, uniform communication interfaces, the dynamic algorithms that allocate resources, and the data-centered approach, on the one hand, and strong security protocols, on the other. The wireless sensor network (WSN) approach to this technology represents a greater versatility of this system as it can be applied to perform different tasks in various industries like smart cities, healthcare, transportation, and industrial automation services. The application of the given services illustrates C-FCF’s capability of creating innovation, modeling effectiveness, and uncovering potential for integration within the IoT network. The virtual simulation analysis is necessary to validate C-FCF’s effectiveness in real-life scenarios. The simulations provide evidence of features, including low latency, efficient resource utilization, and overall system performance, which underlines the practical aspects of applying C-FCF in different IoT settings. Developing this advanced computing architecture, which can surpass the limitations of conventional technology and the ability to entail many different use cases, will potentially change the data processing and management paradigm in IoT-enabled settings.

Directional Wireless Sensor Networks (DWSNs) have been a very active research topic as they have a wide range of applications in both military and civilian areas. In this paper, we study the Antenna Orientation (AO) and Antenna Orientation and Power Assignment (AOPA) problems, two important issues in DWSNs. In the AO problem, given a set $V$ of sensors equipped with directional antennas with beam-width $𝜃=\pi /3$, the objective is to determine an orientation of the antennas and a minimum transmission range $r=O(1)$ such that the induced symmetric communication graph is connected. We prove that the AO problem is NP-hard. We then extend our proof technique and show that the AOPA problem for DWSNs equipped with $\pi /3$ directional antennas is also NP-hard where we have to determine an orientation of the antennas as well as a power assignment $r$ to the nodes such that the resulting symmetric communication graph is connected, and (2) ${\mathrm{\Sigma }}_{u\in S}r{(u)}^{\beta }$ is minimized ($\beta$ is the distance-power gradient, $2\le \beta \le 6$). We propose an $O(n^2)$ heuristic algorithm and demonstrate by simulation that our method is better than the Ice-cream method.

In order to effectively solve the problem of routing efficiency in wireless sensor networks, a new chained routing algorithm based on group perception is proposed in this paper. First, the performance index parameters such as energy consumption and area coverage are given. Meanwhile, a chained routing strategy based on node utility value is established, which combines the “storage-carrying-forwarding” opportunistic transmission mode and decides whether a message needs to be forwarded to an encounter node according to a message forwarding strategy. Finally, the key factors affecting the chained routing algorithm are studied through simulation experiments. The results show that compared with random routing, the chained routing algorithm has great advantages in message delivery rate, collision probability, accuracy, etc.

In wireless sensor network (WSN), most of the devices function on batteries. These nodes or devices have inadequate amount of initial energy which are consumed at diverse rates, based on the power level and intended receiver. In sleep scheduling algorithms, most of the sensor nodes are turned to sleep state to preserve energy and improve the network lifetime (NL). In this paper, an energy-efficient dynamic cluster-based protocol is proposed for WSN especially for physics-based applications. Initially, the network is divided into small clusters using adaptive clustering. The clusters are managed by the cluster heads. The cluster heads are elected based on the novel dynamic threshold. Afterwards, general variable neighborhood search is used to obtain the energy-efficient paths for inter-cluster data aggregation which is used to communicate with the sink. The performance of the proposed method is compared with competitive energy-efficient routing protocols in terms of various factors such as stable period, NL, packets sent to base station and packets sent to cluster head. Extensive experiments prove that the proposed protocol provides higher NL than the existing protocols.

Aiming at the problem of large location errors of traditional ranging-free algorithms in Wireless Sensor Network (WSN), a novel node location algorithm based on proximity-distance mapping (PDM) and Jaya optimization was proposed. In this algorithm, proximity and Euclidean distance are extracted from the relationship of anchor nodes to construct a mapping matrix by using the idea of PDM. It is calculated by using the mapping matrix that the estimated distance from the unknown node to the anchor node can be used for the subsequent calculations. After the estimated distance is obtained, the Jaya optimization algorithm is imported to calculate the location of the unknown one. To accelerate the convergence and enhance the accuracy of the algorithm, the idea of a boundary box is used to limit the initial feasible region of unknown nodes. The experiment results show that the PDM–Jaya algorithm has better positioning accuracy than the original PDM in the same condition.

Energy restrictions are a major challenge for wireless sensor networks, since after the deployment of network nodes in the environment, they can hardly be accessed or even recharged. Since the nodes are placed in harsh and unfriendly areas, they rapidly become prone to defects. Therefore, the management of these networks towards increasing their fault-tolerance capacity in the face of limited resources is extremely important. In this paper, a novel protocol is presented here, where in each node as a cluster member must send its last residing energy level to its upstream head cluster. Also, to prevent rework and energy loss, the node will see to it that only when the current sensed data is different than that of the previous interval, it sends the sensed data to the cluster head (CH). So, on the one hand the proposed protocol distinguishes between live and faulty nodes and therefore increases the network's fault-tolerance capacity, and on the other hand it prevents energy loss by eliminating rework. To implement Leach algorithm plus the proposed FT-Leach protocol, we used MATLAB software to simulate node energy consumption in each interval along with the residing energy of the nodes. The results and comparisons showed that our proposed protocol is visibly better than Leach in terms of fault-tolerance capacity and also energy consumption which ultimately extends network's lifetime.

Sensor nodes are low-power and low-cost devices with the requirement of a long autonomous lifetime. Therefore, the nodes have to use the available power carefully and avoid expensive computations or radio transmissions. In addition, as some wireless sensor networks (WSNs) process sensitive data, selecting a security protocol is vital. Cryptographic methods used in WSNs should fulfill the constraints of sensor nodes and should be evaluated for their security and power consumption. WSN engineers use several metrics to obtain estimations prior to network deployment. These metrics are usually related to power and execution time estimation. However, security is a feature that cannot be estimated and it is either “active” or “inactive”, with no possibility of introducing intermediate security levels. This lack of flexibility is a disadvantage in real deployments where different operation modes with different security and power specifications are often needed. This paper proposes including a new security estimation metric in a previously proposed framework for WSN simulation and embedded software (SW) performance analysis. This metric is called Security Estimation Metric (SEM) and it provides information about the security encryption used in WSN transmissions. Results show that the metric improves flexibility, granularity and execution time compared to other cryptographic tests.

The energy efficiency in wireless sensor networks (WSNs) is a fundamental challenge. Cluster-based routing is an energy saving method in this type of networks. This paper presents an energy-efficient clustering algorithm based on fuzzy c-means algorithm and genetic fuzzy system (ECAFG). By using FCM algorithm, the clusters are formed, and then cluster heads (CHs) are selected utilizing GFS. The formed clusters will be remaining static but CHs are selected at the beginning of each round. FCM algorithm forms balanced clusters and distributes the consumed energy among them. Using static clusters also reduces the data overhead and consequently the energy consumption. In GFS, nodes energy, the distance from nodes to the base station and the distance from each node to its corresponding cluster center are considered as determining factors in CHs selection. Then, genetic algorithm is also used to obtain fuzzy if–then rules of GFS. Consequently, the system performance is improved and appropriate CHs can be selected, hence energy dissipation is reduced. The simulation results show that ECAFG, compared with the existing methods, significantly reduces the energy consumption of the sensor nodes, and prolongs the network lifetime.

Wireless sensor networks (WSNs) represent a promising solution in the fields of the Internet of Things (IoT) and machine-to-machine networks for smart home applications. However, to feasibly deploy wireless sensor devices in a smart home environment, four key requirements must be satisfied: stability, compatibility, reliability routing, and performance and power balance. In this study, we focus on the unreliability problem of the IEEE 802.15.4 WSN medium access control (MAC), which is caused by the contention-based MAC protocol used for channel access. This problem results in a low packet delivery ratio, particularly in a smart home network with only a few sensor nodes. In this paper, we first propose a lightweight WSN protocol for a smart home or an intelligent building, thus replacing the IEEE 802.15.4 protocol, which is highly complex and has a low packet delivery ratio. Subsequently, we describe the development of a discrete event system model for the WSN by using a GRAFCET and propose a development platform based on a reconfigurable FPGA for reducing fabrication cost and time. Finally, a prototype WSN controller ASIC chip without an extra CPU and with our proposed lightweight MAC was developed and tested. It enhanced the packet delivery ratio by up to 100%.

Wireless sensor networks (WSNs) provide acceptable low cost and efficient deployable solutions to execute the target tracking, checking and identification of substantial measures. The primary step necessary for WSN is to organize all the sensor nodes in their positions to build up an effective network. In the sensor deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have obtained significant consideration in sensor deployment. This paper intends to develop an intelligent context awareness algorithm for sensor deployment process in WSN. Accordingly, the process is divided into two phases. In the first phase, the TCOV process is performed, whereas the second phase of the algorithm establishes NCON among the sensors. An objective model to meet both TCOV and NCON is formulated as a minimization problem. The problem is solved using FireFly (FF) optimization to determine the optimal locations for sensors. It leads to an intelligent sensor deployment model that can determine the optimal locations for the sensors in the WSN. Further, the proposed FF-TCOV and FF-NCON models are compared against the conventional algorithms, namely, genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution and evolutionary algorithm-based TCOV and NCON models. The results achieved from the simulation show the improved performance of the proposed technique.

Wireless sensor network (WSN) consists of a large amount of limited battery-powered sensor nodes. In general, energy consumption will be a significant concern for WSN owing to irreplaceable battery constraints of sensor nodes. The zone formation approach could be an adequate data aggregation technique which efficiently minimizes the energy consumption by categorizing sensor nodes into zones. However, the main constraints like zone head (ZH) selection, frequent change of ZH, and multi-hop communication from ZH to the sink have a direct impact on the network consistency of WSN. In this paper, a novel efficient intra- and inter-zone routing scheme has been proposed in order to prolong the network consistency of WSN. In the proposed scheme, the hybrid algorithm is established in which harmony search algorithm incorporates with modified moth flame optimization algorithm. This hybrid algorithm provides the appropriate ZH selection for intra-zone routing that reduces the frequent change of ZH in the network. Furthermore, the path balancing in inter-zone routing is acquired through multi-criteria-based optimal path routing algorithm. The performance results confirm that the proposed scheme enhances the network consistency compared with an existing scheme.

In this work, energy-efficient adaptive duty cycle guaranteed time slot algorithm is proposed for beacon-enabled standard medium access control protocol to efficiently handle low, medium and high data traffic loads. The proposed protocol can efficiently handle high traffic load by effective utilization of slots. The protocol can dynamically update the number of slots for contention access period and contention free period in each beacon interval. Therefore, it can capably handle high network load condition. The protocol continuously monitors the utilization capacity of each of the sensor node and allots the contention free period slots to the needy nodes based on their utilization capacity. Simulation analysis is done for three different scenarios. The performance of the proposed protocol is compared with the other existing protocols. Simulation results show the overall superiority of our proposed algorithm in terms of packet delivery and energy consumption.

In this paper, a secure and low energy dynamic slicing algorithm, namely, Improved D-SMART (IM-D-SMART) based on the Data Aggregation Protocol on Slice Mix Aggregate (D-SMART) is proposed to improve the security and confidentiality of wireless sensor networks and reduce the energy consumption of nodes in data collection and transmission in wireless sensor networks. According to the importance of data, the residual energy of nodes and the relative density of nodes, the data are dynamically partitioned to improve the D-SMART algorithm. Simultaneously, sending negative number splicing is used to compensate for the loss caused by the collision of data transmission between nodes. The simulation results show that IM-D-SMART outperforms D-SMART in terms of computation, privacy and communication cost.

Wireless sensor networks congestion occurs easily due to its centralized traffic pattern. Normally, mono-sync wireless sensor network experiences multiple traffic flow congestion in the dense environment of the network, which leads to excess energy consumption and severe packet loss. To overcome these issues, a congestion detection and alleviation mechanism using cluster based heuristic optimized hierarchical routing protocol is proposed in this paper. Here, congestion detection and alleviation utilize the features of sensor nodes. The congestion is categorized into two types: (i) node level congestion and (ii) link level congestion. The node level congestion is detected by assessing the buffer utilization and the interval amid the consecutive data packets. The link level congestion is considered with computing link usage utilizing back-off step of round robin carrier sense multi-access with collision avoidance. Congestion detection and alleviation reactively affected node/link through cuckoo hosted rider search multi-hop routing algorithm. It has two phases: the cluster head selection and multi-path routing. Cluster head selection is performed through Taylor multiple random forest kernel fuzzy C-means clustering algorithm and multi-path routing is performed through cuckoo hosted rider search multi-hop routing algorithm. The simulation of the proposed method is done in network simulator tool. Here, the performance metrics, like packet delivery ratio, delay, energy consumption, packet drop, overhead, network lifetime and throughput are calculated. The experimental outcomes of the proposed technique shows 11.6%, 18.4% and 28.1% lower delay, 78.2%, 65.4% and 52.6% higher packet delivery ratio, and 29.2%, 37.4% and 40.8% lower packet drop compared with the existing methods, like congestion detection and alleviation using multi-attribute decision-making in optimization-based hybrid congestion alleviation routing protocol in wireless sensor networks, congestion detection and alleviation using hybrid K-means with greedy best first search algorithms in packet rate reduction utilizing adaptive weight firefly algorithm ant colony optimization based routing protocol in wireless sensor networks and congestion detection and alleviation using multi-input time on task optimization algorithm for altered gravitational search algorithm routing protocol in wireless sensor networks.