Number Of Active Nodes Research Articles

Research on the Influence of Improved K-shell Algorithm on Commodity Profit

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.

A fire detection model based on power-aware scheduling for IoT-sensors in smart cities with partial coverage

Fire detection techniques have received considerable critical attention over the past ten years. Regardless of the progress in the area of fire detection, questions have been raised about the cost, complexity, consumed power from a large number of sensors to analyze sensors’ data. Debate continues about the best strategies for the management of consumed power and how to accelerate the processing of real-time data in fire detection through the internet of things. This paper presents a partial coverage and a power-aware IoT-based fire detection model with different multi-functional sensors in smart cities. In the proposed model, the sleep scheduling approach used for saving sensors energy. This approach will significantly help in saving consumed energy and thus the need for any extra number of nodes required for continuously covering the target area. Moreover, fog computing is applied to process real-time data aggregated from a large number of sensors for running systems more efficiently. Validation of the proposed model was carried out via simulation and experimental testbed implementation with Arduino, sensors, and Raspberry pi. The results obtained indicate how the proposed technique can efficiently determine the sensors to meet the constraints imposed. The most striking finding to emerge from this experimental and simulation study is that the proposed technique helps in ensuring excellent performance in terms of the number of active nodes and the network lifetime, over other state-of-the-art techniques. The most striking finding of this work compared to MWSAC and PCLA, while covering the same area, is the minimization of the number of active nodes by 64.33% and 15%. It raises the network’s lifetime by 91.32% compared to MWSAC and 12% compared to PCLA, respectively.

How to be influential being weakly connected

A new algorithm is formulated to maximize actor’s eigencentrality by rewiring links as to connect actors with the largest eigencentrality. The algorithm is used to model the process of formation of oligarchy in a social network. During the time evolution, activity of actors can be switched on or off. A node can be active only if its degree fits to a prescribed range. Three stages of time evolution are distinguished. During the first stage the number of active nodes evolves to some stationary value. During the second stage a selected group of actors are active and they modify their connections to other actors. At a third stage the network is frozen in an absorbing state, where either any activity dies out, or an ‘oligarchic’ group of active actors emerges, represented by a complete graph. The size of this group is determined by the maximal allowed degree and it does not depend on the system size. Therefore, the concentration of power increases with the system size. This result is consistent with sociological theory.

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

Robust network structures for conserving total activity in Boolean networks

One of the typical properties of biological systems is the law of conservation of mass, that is, the property that the mass must remain constant over time in a closed chemical reaction system. However, it is known that Boolean networks, which are a promising model of biological networks, do not always represent the conservation law. This paper thus addresses a kind of conservation law as a generic property of Boolean networks. In particular, we consider the problem of finding network structures on which, for any Boolean operation on nodes, the number of active nodes, i.e., nodes whose state is one, is constant over time. As a solution to the problem, we focus on the strongly-connected network structures and present a necessary and sufficient condition.

Fast node cardinality estimation and cognitive MAC protocol design for heterogeneous machine-to-machine networks

Machine-to-Machine (M2M) networks are an emerging technology with applications in numerous areas including smart grids, smart cities, vehicular telematics, and healthcare. In this paper, we design two estimation protocols for rapidly obtaining separate estimates of the number of active nodes of each traffic type in a heterogeneous M2M network with $T$ types of M2M nodes (e.g., those that send emergency, periodic, normal type data etc), where $T \geq 2$ is an arbitrary integer. One of these protocols, Method I, is a simple scheme, and the other, Method II, is more sophisticated and performs better than Method I. Also, we design a medium access control (MAC) protocol that supports multi-channel operation for a heterogeneous M2M network with an arbitrary number of types of M2M nodes, operating as a secondary network using Cognitive Radio technology. Our Cognitive MAC protocol uses the proposed node cardinality estimation protocols to rapidly estimate the number of active nodes of each type in every time frame; these estimates are used to find the optimal contention probabilities to be used in the MAC protocol. We compute a closed form expression for the expected number of time slots required by Method I to execute as well as a simple upper bound on it. Also, we mathematically analyze the performance of the Cognitive MAC protocol and obtain expressions for the expected number of successful contentions per frame and the expected amount of energy consumed. Finally, we evaluate the performances of our proposed estimation protocols and Cognitive MAC protocol using simulations.

Energy Efficient Communication Framework for Target Coverage using Trust Concepts

Target coverage is a challenging task in the field of wireless sensor networks aiming to observe a set of critical targets while considering the limited resources and the network lifetime is improved. The paper proposes an approach to : i) determine the strategy according to which the critical targets can be monitored while satisfying a certain confidence level. ii) determine the maximum and minimum number of nodes which can guarantee the coverage, iii) determine the optimal number of active nodes for various deployment strategies, iv) to determine a routing mechanism using either single hop/multi hop communication based on the reachability of the node to the base station and iv) to develop an aggregation protocol which can reduce the redundancy and number of packet transmissions. The proposed protocol is based on the two level aggregation at the set cover level and at the cluster level using the concept of Locality Sensitive Hashing (LSH) and Jaccard Similarity measure. The efficiency of the proposed aggregation mechanism is determined for various data sets of multiple dimensions. The results obtained through the simulations show the improvement in the network performance with respect to the network longevity, coverage, reliability and of the data transmission as compared to the Boolean coverage model

Research on Wake-up Mechanism of Wireless Sensor Network Nodes

The traditional node wake-up mechanism usually takes the huge cost of network energy and fails to consider the expensive price of the sensor node. In order to improve this shortcoming, this paper studies the wireless sensor network monitoring area coverage rate of 100% in the existing an improved node wake-up mechanism problem is proposed in the node wake-up mode. Firstly, the problem is transformed into SAT (the satisfiability of Boolean equation), and then the MiniSAT solver is used to solve the answer. The wireless sensor network is reached with as few working nodes as possible. The complete coverage of the monitoring area and the reduction of network energy consumption show that the new node wake-up mechanism can reduce the number of active nodes in the wireless sensor network monitoring area, improve network energy utilization, and reduce the application of WSN (wireless sensor network). Cost, driving the application of wireless sensor networks.

Binary grey wolf optimisation‐based topology control for WSNs

Wireless sensor networks (WSNs) are composed of a large number of sensor nodes that are deployed at target locations. Topology control (TC) is one of the significant fundamental challenges in WSNs because of node energy and computing power constraints. TC algorithms try to produce reduced topology by preserving network connectivity. This study presents a novel TC algorithm based on binary Grey wolf optimisation. It uses the active and inactive schedules of sensor nodes in binary format as well as introduces fitness function to minimise the number of active nodes (ANs) for achieving the target of lifetime expansion of the nodes and network. The proposed algorithm is compared with other TC algorithms. The result reduces a minimum of 10% of ANs and energy consumption by 6.84%. The proposed approach also gives maximum coverage and connectivity. The designed fitness function also benefits in the process of selecting a node with low residual energy to join the active topology. The standard deviation in the remaining energy for the proposed algorithms is lower than the other TC schemes.

Optimizing topologies in wireless sensor networks: A comparative analysis between the Grey Wolves and the Chicken Swarm Optimization algorithms

Finding the suitable solution in constrained environments, such as wireless sensor networks (WSNs), is in the application area of meta-heuristic algorithms. The constrained resources of such networks include: low surplus energy, limited computational power, and small communication bandwidth. Despite this fact, not all meta-heuristic algorithms guarantee suitable energy-efficient routing due to their complexity and the need for specific parameters’ tuning. This had motivated our attempts to benchmark the efficacy of topology control protocols through the application of two meta-heuristic algorithms; the Grey Wolves Optimizer (GWO) and the Chicken Swarm Optimization (CSO) algorithms. In addition to the performance analysis that has been proven through the simulations, the assessment had covered a comparative analysis of the behavior of each algorithms’ operators. The performance indicator was finding the smallest number of active nodes for the network’s operation. These nodes should have high residual energy for the purpose of extending the network’s operation time. The proposed solution penalizes any topology that trades-off the coverage characteristic of the network. Through a number of quantitative experiments, the results showed the dominance of CSO based algorithm as active nodes’ reduction had reached up to 12% of the total numbers of network’s nodes.

Improving Lifetime of Wireless Sensor Network Based on Sinks Mobility and Clustering Routing

Previous studies on wireless sensor networks indicate one of the most important research topics is to consider both significance of the reduction in sensor nodes energy consumption and improving network lifetime. Regarding results of some researchers using sinks, mobility has a substantial impact on the performance of sensor networks concerning reducing total energy consumption, increasing the number of active nodes and solving the energy-hole problem. In this study, we consider hexagons beehives feature where sensor nodes are distributed across a hexagon randomly. This hexagonal is divided into equal clusters based on the radius of hexagonal. Also, the need for cluster heads to be close to their centre of clusters in the previous similar model is solved by adding mobility to the sinks, providing a pre-determined path for mobile sinks and covering each cluster by sinks from two different directions. Therefore, the proposed model simulation results show the increase in the average of residual energy between sensor nodes, reducing total energy consumption, increasing the number of active nodes and ultimately improving the lifetime of wireless sensor networks related to previous models.

A Cross-Layer Approach to Data-Aided Sensing Using Compressive Random Access

In this paper, data-aided sensing as a cross-layer approach in Internet-of-Things (IoT) applications is studied, where multiple IoT nodes collect measurements and transmit them to an access point (AP). It is assumed that measurements have a sparse representation (due to spatial correlation) and the notion of compressive sensing (CS) can be exploited for efficient data collection. For data-aided sensing, a node selection criterion is proposed to efficiently reconstruct a target signal through iterations with a small number of measurements from selected nodes. Together with compressive random access (CRA) to collect measurements from nodes, compressive transmission request is proposed to efficiently send a request signal to a group of selected nodes. Error analysis on compressive transmission request is carried out and the impact of errors on the performance of data-aided sensing is studied. Simulation results show that data-aided sensing allows to reconstruct the target information with a small number of active nodes and is robust to nodes’ decision errors on compressive transmission request.

A Multi-Layer Self-Healing Algorithm for WSNs

The implementation of Wireless Sensor Networks (WSNs) is a challenging task due to their intrinsic characteristics, e.g., energy limitations and unreliable wireless links. Considering this, we have developed the Disjoint path And Clustering Algorithm (DACA) that combines topology control and self-healing mechanisms to increase the network lifetime with minimum loss of coverage. Initially, DACA constructs a tree that includes all nodes of the network by using the Collection Tree Protocol (CTP). This tree is an initial communication backbone through which DACA centralizes the information. Then, DACA builds a set of spatial clusters using Kmeans and selects the Cluster Heads (CHs) using Particle Swarm Optimization (PSO) and a multi-objective optimization (MOO) function. Subsequently, DACA reconstructs the tree using only the CHs. In this way, DACA reduces the number of active nodes in the network and saves energy. Finally, DACA finds disjoint paths on the reconstructed tree by executing the N-to-1 multipath discovery protocol. By doing so, the network can overcome communications failures with a low control message overhead. The simulations on Castalia show that DACA considerably extends the network lifetime by having a set of inactive nodes and disjoint paths that support the communication when active nodes die. Besides, DACA still maintains a good coverage of the area of interest despite the inactive nodes. Additionally, we evaluate the shape of the tree (i.e., the average number of hops) and the risk of connection loss of the network.

Energy Efficient Clustering using Modified Multi-Hop Clustering

In recent years, WSN has been widely used for building the decision support system (DSS) for solving the real-world problem. Moreover, out of several fields, one of the interesting fields that requires DSS is the monitoring of agriculture environment. Nowadays, monitoring an agricultural environment has become one of the essential fields. IoT has been one of the eminent technologies in recent years and WSN model has played the parallel role into it. These sensors have a limited power supply and this affects the efficiency of the algorithm. In the past, several methods have been proposed for efficient clustering, however, these methods fail to provide satisfactory accuracy. Hence, this article proposes an energy efficient methodology named modified multi-hop clustering (MMHC). This methodology takes three steps. The first step is assembling the nodes where the nodes are assembled, the second step couples the nodes, and the third step detects the redundant nodes and discard them. The main advantage of MMHC is that it can select multiple redundant nodes at one time and discard, and this makes the algorithm more fast and efficient. Moreover, in order to evaluate the algorithm several parameters have been considered, such as energy consumption, number of failed nodes, number of active nodes, and communication overhead.

Reduction of energy consumption in wireless sensor networks based on predictable routes for multi-mobile sink

According to recent studies, we can benefit from mobile sinks and hierarchical algorithms to improve energy efficiency and load efficiency in wireless sensor networks. To improve energy conservation and to increase load balancing, we proposed a cluster-based routing algorithm based on several predictable movement paths for mobile sinks. The basic idea of the proposed scheme is to prolong the network longevity and to reduce the average distance between cluster heads and mobile sinks in the data transmission process. To this end, and inspiring by the hexagonal shape of beehives, we let the network area to be a hexagonal shape in which sensor nodes are distributed randomly. This hexagon is composed of three rhombuses, where each rhombus is considered as a cluster. The mobile sinks are forced to move along an internal triangle formed by connecting the large diagonals of these three rhombuses. Since the sinks move along a pre-determined path and accurately covers the area of all clusters, this movement strategy eliminates the problem of sink off cluster heads, seen in a previous similar approach. In addition, we provide the ability for the sensing nodes to send the data to the closet sinks based on different movement paths and with minimal energy consumption. Therefore, the distance between the cluster heads and mobile sinks is reduced, which in turn leads to reduced energy consumption among the sensing nodes and increases load balancing. According to the simulation results, the proposed scheme has better performance in terms of the number of active nodes, the total energy consumption, the average of residual energy, and the network lifetime compared to previous movement strategies.

Mobile target tracking algorithm for wireless camera sensor networks with adjustable monitoring direction of nodes

SummaryWireless camera sensor networks (WCSNs) possess a powerful physical environment monitoring capability. Camera nodes with adjustable monitoring directions further improve their flexibility. This study focuses on tracking multiple mobile targets to investigate the node scheduling and target location evaluation strategy of WCSNs on the basis of rotating nodes. By referring to existing research, this study improves the camera node monitoring and rotation model and proposes three network performance evaluation indicators. The proposed algorithm schedules nodes and their monitoring directions by using the unutilized energy of the nodes and the number of monitored targets. It also predicts the moving trends of the targets and selects active nodes by using the locations and linear speeds of the targets. Experimental results show that the proposed algorithm has a high target tracking accuracy. Compared with traditional target tracking algorithms, the proposed algorithm can effectively reduce the number of active nodes, balance the energy consumption between nodes, and prolong network lifetime.

Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes

Abstract The Internet of Medical Things (IoMT) is an essential paradigm for ubiquitous monitoring in healthcare environments. The IoMT system collects data (e.g. temperature, hazardous contamination, light intensity, room and patient status, etc.) from connected medical devices and sensor nodes to a central or distributed computer network. In order for these devices and sensor nodes to continue operating, they must be charged with sufficient energy at all times. In this paper, we propose an IoMT system that employs autonomous mobile chargers, which is equipped with wireless energy transfer technology, to support sensor nodes recharging requests. In this model, the mobile charger must distribute the energy among the sensor nodes so that their operation is not interrupted. This is challenging because the mobile charger carries limited amount of energy that may not be sufficient to satisfy all recharging requests. In this paper, we propose a reward-based energy charging decision mechanism that allows mobile chargers and sensor nodes to coordinate the charging process. The proposed reward-based mechanism utilizes the Analytical Hierarchy Process (AHP) for fair distribution of energy among the nodes. This paper presents the theoretical analysis of the model and the simulation experiments. Our results show that the proposed model can support a larger number of active nodes with less energy compared to conventional first come first served methods. Also the coverage utility of sensor nodes is much higher using our method compared to the on-demand recharging request schemes found in existing studies.

Exploiting Layered Multi-Path Routing Protocols to Avoid Void Hole Regions for Reliable Data Delivery and Efficient Energy Management for IoT-Enabled Underwater WSNs.

The key concerns to enhance the lifetime of IoT-enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are energy-efficiency and reliable data delivery under constrained resource. Traditional transmission approaches increase the communication overhead, which results in congestion and affect the reliable data delivery. Currently, many routing protocols have been proposed for UWSNs to ensure reliable data delivery and to conserve the node’s battery with minimum communication overhead (by avoiding void holes in the network). In this paper, adaptive energy-efficient routing protocols are proposed to tackle the aforementioned problems using the Shortest Path First (SPF) with least number of active nodes strategy. These novel protocols have been developed by integrating the prominent features of Forward Layered Multi-path Power Control One (FLMPC-One) routing protocol, which uses 2-hop neighbor information, Forward Layered Multi-path Power Control Two (FLMPC-Two) routing protocol, which uses 3-hop neighbor information and ’Dijkstra’ algorithm (for shortest path selection). Different Packet Sizes (PSs) with different Data Rates (DRs) are also taken into consideration to check the dynamicity of the proposed protocols. The achieved outcomes clearly validate the proposed protocols, namely: Shortest Path First using 3-hop neighbors information (SPF-Three) and Breadth First Search with Shortest Path First using 3-hop neighbors information (BFS-SPF-Three). Simulation results show the effectiveness of the proposed protocols in terms of minimum Energy Consumption (EC) and Required Packet Error Rate (RPER) with a minimum number of active nodes at the cost of affordable delay.

Adaptive Centralized Random Access for Collision Free Wireless Local Area Networks

Packet collisions caused by the standard Distributed Coordination Function (DCF) protocol in wireless local area networks (WLANs) have been a challenging problem leading to congestion and reduced throughput. To solve this problem, we propose a novel backoff state generation algorithm, called adaptive virtual backoff algorithm (AVBA), operating in an access point. The virtual contention window size in the algorithm is automatically adjusted based on the number of synchronized active nodes in a cell. Backoff counts generated by AVBA operating in the access point are sent by the access point to each active node in the cell to try to synchronize transmissions and minimize packet collisions. The evaluation results show that the AVBA improves the throughput performance compared to the existing collision resolution techniques, such as the deterministic backoff and centralized random backoff (CRB), when the number of active nodes is large. Moreover, the results show that it maintains a high level of fairness in terms of channel access opportunities between nodes using AVBA and nodes using the DCF protocol when they coexist in a mixed wireless network scenario.

Data-Driven Dynamic Active Node Selection for Event Localization in IoT Applications - A Case Study of Radiation Localization

In this paper, the problem of active node selection for localization tasks, on the Internet of Things (IoT) sensing applications, is addressed. IoT plays a significant role in realizing the concept of smart environments, such as in environmental, infrastructural, industrial, disaster, or threat monitoring. Several IoT sensing nodes can be deployed within an area to collect regional information for the purpose of achieving a common contextual goal. Active node selection proves useful in mitigating common IoT-related issues like resource allocation, network lifetime, and the confidence in the collected data, by having the right sensors active at a given time. Current active node selection schemes prove inefficient when adapted to localization tasks, as they- 1) are usually designed for general monitoring, not localization, 2) do not dynamically exploit data readings in the selection process, and 3) are mostly designed for systems with nodes having sensing ranges. To address these challenges, we propose a novel Data-driven active node selection approach that- 1) dynamically uses data readings from current active nodes to select future ones, 2) assesses the area coverage achieved by a group of nodes while considering range-free sensors, 3) considers parameters like residual energy, power cost, and data confidence levels in the selection process, and 4) combines group-based and individual-based selection mechanisms to enhance the localization process in terms of time and power consumption. These considerations are integrated into a two-phase active node selection mechanism that uses genetic and greedy algorithms to select optimum groups for localization tasks. The efficacy of the proposed approach is validated through an example of radioactive source localization by using real-life and synthetic datasets, and by comparing the proposed approach to existing benchmarks. The results demonstrate the ability of the proposed approach to performing faster localization at low energy cost, even with a smaller number of active nodes.