Static Nodes Research Articles

Cooperation Between Mobile and Static Nodes in Mobile Wireless Sensor Networks

Mobile wireless sensor networks (MWSNs), a special class of WSN in which one or more component of the network is mobile, have recently grown popularity. In MWSNs, mobility plays a key role in the operation of the network. Cooperation between mobile and static nodes in MWSNs has gained significant importance in recent years. These nodes can cooperate in a number of ways to increase the efficiency and performance of the network. Mobile nodes, in cooperation with other static nodes in MWSN can provide important benefits in sensor deployment, localization, route planning and navigation, connectivity prediction and repair, routing and data collection. The aim of this paper is to characterize different cooperation techniques in different stages of MWSNs operation. First, we provide a general introduction followed by an overview to the roles and types of mobile nodes in MWSNs. Then we present basic taxonomy and brief review on cooperation between mobile and static nodes in different phases of MWSNs operation. Finally, the paper is concluded with hints to open problems.

Towards an energy balancing solution for wireless sensor network with mobile sink node

The issue of energy holes, or hotspots, in wireless sensor networks is well referenced. As is the proposed mobilisation of the sink node in order to combat this. However, as the mobile sink node may communicate with some nodes more than others, issues remain, such as energy spikes. In this study we propose a lightweight MAC layer solution — Dynamic Mobility and Energy Aware Algorithm (DMEAAL). Building on existing solutions utilising a communication threshold between static nodes and a sink node using a predictable mobility pattern, DMEAAL takes knowledge of optimum energy consumption levels and implements a cross-layer approach, utilising current energy consumption and dynamically adjusting communication threshold size based on target energy consumption. This approach is shown to balance energy consumption across individual nodes without increasing overall energy consumption compared to previous solutions. This without detrimentally affecting frame delivery to the sink. As such, network lifetime is improved. In addition we propose Mobile Edge Computing (MEC) applications for this solution, removing certain functionality from static nodes and instead deploying this within the mobile sink at the network edge.

A routing-benefited deployment approach combining static and dynamic layouts for underwater optical wireless networks

A routing-benefited deployment algorithm combining static and dynamic layouts is proposed, and its comprehensive performance evaluation is given in this article. The proposed routing-benefited deployment algorithm is intended to provide a suitable network deployment and subsequent data transmission approach for underwater optical networking and communication. Static nodes are anchored for long-term monitoring, and movable nodes can adjust their depths based on the virtual force and move with the variation of area-of-interest changing. Then, nodes begin to collect data that they can monitor and transmit to sink nodes. Here, the underwater wireless optical communication model is described to actualize the real environment, and the vector-based forwarding protocol is particularly considered to compare the impact of different deployment algorithms on routing. It is shown by simulation experiment results that routing-benefited deployment algorithm outperforms several existing traditional virtual force deployment algorithms in terms of coverage, lifetime, energy consumption balance, packet-loss rate, and time-delay.

A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks

Sensors in a Wireless Sensor Network (WSN) sense, process, and transmit information simultaneously. They mainly find applications in agriculture monitoring, environment monitoring, smart city development and defence. These applications demand high-end performance from the WSN. However, the performance of a WSN is highly vulnerable to various types of security threats. Any intrusion may reduce the performance of the WSN and result in fatal problems. Hence, fast intrusion detection and prevention is of great use. This paper aims towards fast detection and prevention of any intrusion using a machine learning approach based on Gaussian Process Regression (GPR) model. We have proposed three methods (S-GPR, C-GPR and GPR) based on feature scaling for accurate prediction of k-barrier coverage probability. We have selected the number of nodes, sensing range, Sensor to Intruder Velocity Ratio (SIVR), Mobile to Static Node Ratio (MSNR), angle of the intrusion path, and required k as the potential features. These features are extracted using an analytical approach. Simulation results demonstrate that the proposed method III accurately predicts the k-barrier coverage probability and outperforms the other two methods (I and II) with a correlation coefficient (R = 0.85) and Root Mean Square Error (RMSE = 0.095). Further, the proposed methods achieve a higher accuracy as compared to other benchmark schemes.

Energy Efficient 100+ GHz Transceivers Enabling Beyond-5G Wireless Communications

Portable computing devices handling multi gigabit-per-second (Gb/s) data rates are anticipated to enter the wireless market with the rise of the 5G protocol in the future. The recent allocation of the above-100-GHz band by the Federal Communications Commission creates a great opportunity for developing wireless networks with distributed base station nodes targeting high data rates. This ever-in-creasing need for higher data rates calls for novel transceiver architectures that address fundamental shortcomings of the conventional designs and can achieve tens of Gb/s speed with high spectral efficiency. This article presents an overview of design challenges and opportunities in regard to implementation of energy efficient 100+ GHz wireless transceivers and opportunities for research and development in this emerging area.

Online force-directed algorithms for visualization of dynamic graphs

Force-directed (FD) algorithms can be used to explore relationships in social networks, visualize money markets, and analyze transaction networks. However, FD algorithms are mainly designed for visualizing static graphs in which the topology of the networks remains constant throughout the calculation. In contrast to static graphs, nodes and edges in dynamic graphs can be added or removed as time progresses. In these situations, existing FD algorithms do not scale well, since any changes in the topology will trigger these algorithms to completely restart the entire computation. To alleviate this problem, we propose a design and implementation of five online FD algorithms to visualize dynamic graphs while maintaining their native force models. The online FD algorithms developed in this paper are able to reuse the force models of existing FD algorithms without significant modifications. To evaluate the effectiveness of the proposed approach, online FD algorithms are compared against static FD algorithms for visualizing dynamic graphs. Experimental results show that among the five algorithms evaluated, the online FD algorithm achieves the best number of edge crossings and the standard deviation of edge lengths for visualizing dynamic graphs.

Experimental Exploration of Unlicensed Sub-GHz Massive MIMO for Massive Internet-of-Things

IoT networks are getting overcrowded following the vast increase in number of Internet-of-Things (IoT) devices and connections. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections and moreover lower the energy expenditure of these devices. We present a study of the channel characteristics of massive MIMO in the narrowband unlicensed sub-GHz band. The goal is to support IoT applications with strict requirements in terms of number of devices, power consumption, and reliability. The assessment is based on experimental measurements using both a uniform linear and a rectangular array. Our study demonstrates and validates the advantages of deploying massive MIMO gateways to serve IoT nodes. While the results are general, here we specifically focus on static nodes. The array gain and channel hardening effect yield opportunities to lower the transmit power of IoT nodes while also increasing reliability. The exploration confirms that exploiting large arrays brings great opportunities to connect a massive number of IoT devices by separating the nodes in the spatial domain. In addition, we give an outlook on how static IoT nodes could be scheduled based on partial channel state information.

Quantitative Study on Impact of Static/Dynamic Selfishness on Network Performance in VANETs

Vehicular ad hoc network is a kind of mobile ad hoc networks which provides wireless communication between vehicles. In most cases, multi-hop communication is needed, because of the limited range of wireless transmission. The multi-hop communication among nodes strictly relies on the forwarding functionality of intermediate nodes. Due to resource limitation, the intermediate nodes may exhibit selfishness and refuse to bear forwarding tasks for others. In this article, we defined two types of selfish nodes, namely static selfish nodes and dynamic selfish nodes. The impact of the two types of selfish nodes are quantitatively investigated from various aspects including mobilities, proportions, densities, and combinations. We conducted exhaustive simulations on an integrated simulation platform which consists of OMNeT++, SUMO, INET, and Veins. The experimental results indicate that the static selfish nodes have more harmful impacts on the performance of vehicular ad hoc networks in terms of average packet delivery ratios and end-to-end delays. Moreover, the results also imply that the impact of node selfishness should be evaluated by a comprehensive consideration of mobilities, proportions, densities, and combinations of selfish nodes.

New path trajectory followed by Single Anchor by using Salp Swarm Algorithm in Wireless Sensor Network

In the various applications of wireless sensor networks (WSN), the event occurrence information or data sensed by the sensor node has no significance until the location of an event/sensor node is not known. Therefore, localization is an important parameter in WSN for tracking these deployed sensor nodes in the sensing area. To localize these sensor nodes, there are various existing localization models in available detailed literature. One solution to this problem is to deploy a set of static anchor nodes (GPS equipped) in the sensing area, that broadcast their coordination information to help unknown nodes to localize themselves. Another way is to use a single mobile anchor node that will follow a specific path and localize unknown nodes. But a mobile anchor node must follow an optimal path that helps to achieve high accuracy and able to localize a large number of unknown nodes in less computational time. In this paper, the author design and developed a localization algorithm in which a GPS equipped single mobile anchor node follows a new path trajectory i.e. spiral pentagon. Once the target node falls under the communication diameter of the virtual node, six virtual anchor nodes are projected in the shape of a ring around the anchor node and localize all the unknown nodes in the sensing area by using the application of Salp Swarm Algorithm (SSA) for optimization. The hypothesis of six virtual anchor nodes is to avoid the problem of Line of Sight. This proposed method assured that each target node can estimate their node coordinates at least once with a low localization error.

Link Budget Analysis for Reconfigurable Smart Surfaces in Aerial Platforms

Non-terrestrial networks, including Unmanned Aerial Vehicles (UAVs), High Altitude Platform Station (HAPS) nodes and Low Earth Orbiting (LEO) satellites, are expected to have a pivotal role in sixth-generation wireless networks. With inherent features such as flexible placement, wide footprints, and preferred channel conditions, they can tackle several challenges faced by current terrestrial networks. However, their successful and widespread adoption relies on energy-efficient on-board communication systems. In this context, the integration of Reconfigurable Smart Surfaces (RSS) into aerial platforms is envisioned as a key enabler of energy-efficient and cost-effective aerial platform deployments. RSS consist of low-cost reflectors capable of smartly directing signals in a nearly passive way. In this paper, we investigate the link budget of RSS-assisted communications for two RSS reflection paradigms discussed in the literature, namely “specular” and “scattering” paradigms. Specifically, we analyze the characteristics of RSS-equipped aerial platforms and compare their communication performance with that of RSS-assisted terrestrial networks using standardized channel models. In addition, we derive the optimal aerial platform placements for both reflection paradigms. Our results provide important insights for the design of RSS-assisted communications. For instance, given that a HAPS has a large area for RSS, it provides superior link budget performance in most studied scenarios. In contrast, the limited RSS area on UAVs and the large propagation loss in LEO satellite communications make them unfavorable candidates for supporting terrestrial users. Finally, the optimal location of an RSS-equipped platform may depend on the platform’s altitude, coverage footprint, and type of environment

On the Capacity of Full‐Duplex AF/DF Relay System with Energy Harvesting for Vehicle‐to‐Vehicle Communications

This paper studies the ergodic capacity (EC) of full‐duplex (FD) amplify‐and‐forward (AF) and decode‐and‐forward (DF) relay system with energy harvesting (EH) for vehicle‐to‐vehicle (V2V) communications. Unlike previous works on FD‐EH systems, we consider the case that both relay and destination are mobile vehicles while the source is a static base station. We mathematically derive the exact closed‐form expressions of ECs of both AF and DF protocols of the considered FD‐EH‐V2V relay system over cascade (double) Rayleigh fading. Our numerical results show that the ECs in the case of the V2V communication system are reduced compared to those in the case of stationary nodes. Also, with a specific value of residual self‐interference (RSI), the ECs of the considered FD‐EH‐V2V relay system can be higher or lower than those of half‐duplex‐ (HD‐) EH‐V2V system, depending on the average transmission power of the source. Furthermore, when the transmission power of the source and RSI are fixed, the ECs of the considered system can achieve peak values by using optimal EH time duration. On the other hand, the ECs of both AF and DF protocols reach the capacity floors in the high signal‐to‐noise ratio (SNR) regime due to the RSI impact. Also, the effect of RSI dominates the impact of cascade Rayleigh fading in the high SNR regime. Finally, we validate our analysis approach through Monte‐Carlo simulations.

A node localisation method for wireless sensor networks based on an improved bee colony algorithm

To overcome the problems of inaccurate positioning and large time consumption for traditional node localisation in wireless sensor networks, a node localisation method based on an improved bee colony algorithm is proposed in this paper. This paper adopts a traditional method combined with a classical multidimensional expansion method to construct a static distance matrix, uses the improved bee colony algorithm to obtain the original coordinates of the static nodes in a wireless sensor network, and determines the actual positions of these nodes. And use the offset speed and a time sequence method to construct an offset compensation model of dynamic sensor nodes. Combined with the uniform measurement method of node target positioning, the optimal positioning of the dynamic sensor nodes is determined. The experimental results show that the accuracy of the proposed method is approximately 98%, and the shortest time is approximately 2.2 s.

Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

In wireless sensor networks, due to the restricted battery capabilities of sensor nodes, the energy issue plays a critical role in network efficiency and lifespan. In our work, an upgraded long short‐term memory is executed by the base station to frequently predict the forecast positions of the node with the help of load‐adaptive beaconing scheduling algorithm. In recent years, new technologies for wireless charging have offered a feasible technique in overcoming the WSN energy dilemma. Researchers are deploying rechargeable wireless sensor networks that introduce high‐capacity smartphone chargers for sensor nodes for charging. Nearly all R‐WSN research has focused on charging static nodes with relativistic routes or mobile nodes. In this work, it is analysed how to charge nondeterministic mobility nodes in this work. In this scenario, a new mechanism is recommended, called predicting‐based scheduling algorithm, to implement charging activities. In the suggested technique, it directs them to pursue the mobile charger and recharge the sensor, which is unique for the present work. The mobile charger will then choose a suitable node, utilizing a scheduling algorithm, as the charging object. A tracking algorithm based on the Kalman filter is preferred during energy transfer to determine the distance needed for charging between the destination node & mobile charger. Here, the collecting & processing of data are performed through the big data collection in WSNs. The R‐WSN charging operations of nondeterministic mobility nodes will be accomplished using the proposed charging strategy.

Federated Learning With a Drone Orchestrator: Path Planning for Minimized Staleness

In this paper, we investigate the problem of scheduling transmissions for spatially scattered nodes that contribute to a collaborative federated learning (FL) algorithm via wireless links provided by a drone. In the considered system, the drone acts as an orchestrator, coordinating the transmissions and the learning schedule within a predefined deadline. The actual schedule is reflected in a planned path: as the drone traverses it, it controls the distance and thereby the data rate to each node. Hence, the model is structured such that the drone orchestrator uses the path (trajectory) as its only tool to achieve fairness in terms of learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staleness</i> , which reflects the learning time discrepancy among the nodes. Using the number of learning epochs performed at each learner as a performance indicator, we combine the average number of epochs computed and staleness into a balanced optimization criterion that is agnostic to the underlying FL implementation. We consider two methods for solving the complex trajectory planning optimization problem for static nodes: (1) successive convex programming (SCP) and (2) deep reinforcement learning (RL). Considering the proposed criterion, both methods are compared in three specific scenarios with few nodes. The results show that drone-orchestrated FL outperforms an immobile deployment by providing improvements in the range of 57% to 87.7%. Additionally, RL-guided trajectories are generally superior to SCP provided ones for complex node arrangements.

Monte Carlo Node Localization Based on Improved QUARTE Optimization

Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi‐Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high‐quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.

Retracted] Mathematical Modeling and Simulation of Wireless Sensor Network Coverage Problem

The research on wireless sensor networks has achieved a lot in recent years and some of the results have been put into practical applications, but with the increasing demand and requirements for wireless sensor networks, many old and new problems need to be solved urgently. In this paper, a data topology optimization algorithm based on local tree reconstruction for heterogeneous wireless sensor networks is proposed for data transmission in wireless sensor networks that are easily affected by external instabilities. This heterogeneous network can accomplish better data transmission; firstly, the nodes are divided into different layers according to the hop count of nodes in the network, and a certain proportion of relay nodes are selected for different layer nodes; then, different initial energy is set for different layer nodes, and since the data packets of different nodes have different sizes, the corresponding data aggregation coefficients are used in this paper according to the actual data requirements of the network during data transmission; finally, the topology of the tree is dynamically updated in real time during the operation of the network to extend the lifetime of the nodes. The simulation results verify that the proposed heterogeneous network topology evolution algorithm effectively extends the network lifetime and improves the utilization of nodes. This paper establishes a modified least‐squares target localization model to achieve accurate 3D localization of targets in real scenes and proposes an optimal base station node selection strategy based on spectral clustering using the location distribution information of base station nodes in space. The simulation results show that the error of the terminal 3D coordinates calculated by the proposed algorithm is smaller than the real coordinates, and the error is smaller than other existing algorithms with the same simulation data.

The Impact of Mobile DIS and Rank-Decreased Attacks in Internet of Things Networks

With a predicted 50 billion devices by the end of 2020, the Internet of things has grown exponentially in the last few years. This growth has seen an increasing demand for mobility support in low power and lossy sensor networks, a type of network characterized by several limitations in terms of their resources including CPU, memory and batter, causing manufactures to push products out to the market faster, without the necessary security features. IoT networks rely on the Routing Protocol for Low Power and Lossy Network (RPL) for communication, designed by the Internet Engineering Task Force (IETF). This protocol has been proven to be efficient in relation to the handling of routing in such constrained networks, However, research studies revealed that RPL was inherently designed for static networks, indicating poor handling of mobile or dynamic topologies which is worsen when introducing mobile attacker. In this paper, two IoT routing attacks are evaluated under a mobile attacker with the aim of providing a critical evaluation of the impact the attacks have on the network in comparison to the case with static attacker. The first attack is the Rank attack in which the attacker announces false routing information to its neighbour attracting them to forward their data via the attacker. The second attack is the DIS attack in which the attacker floods the network with DIS messages triggering them to reset their transmission timers and sending messages more frequently. The comparison were conducted in terms of average power consumption and also the packet delivery ratio (PDR). Based on the results collected from the simulations, it was established that when an attacking node is mobile, there’s an average increase of 36.6 in power consumption and a decrease of 14 for packet delivery ratios when compared to a static attacking node.

Predicting-Scheduling-Tracking: Charging Nodes With Non-Deterministic Mobility

In Wireless Sensor Networks (WSN), the energy problem plays the critical role in network performance and lifetime, because of the limited battery capacities of sensor nodes. Recently, emerging wireless charging technologies provide a promising approach to address the energy problem in WSN. Researchers construct Wireless Rechargeable Sensor Networks (WRSN), which introduce mobile chargers with high capacity batteries to charge sensor nodes. Most studies in WRSN have paid attention to charging static nodes or mobile nodes with deterministic trajectories. In this work, we explore how to charge nodes with non-deterministic mobility. We propose a novel approach, named Predicting-Scheduling-Tracking (PST), to perform charging tasks in this case. In the proposed scheme, different from the existing work, we guide the mobile charger to chase the sensor and recharge it. In our work, the base station runs an improved LSTM to predict the future locations of nodes periodically. Then, the mobile charger can select an appropriate node as the charging target by a charging scheduling algorithm. During the energy transferring, a Kalman-filter-based tracking algorithm is used to ensure the charging-required distance between the mobile charger and the target node. The simulation results show that the proposed charging scheme can fulfil the charging tasks in WRSN of nodes with non-deterministic mobility.

Heuristic Edge Server Placement in Industrial Internet of Things and Cellular Networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current information and communications technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile-edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay, is a challenging problem. In this article, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom's base station data set that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multiobjective constraint optimization problem that places edge servers strategically to balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hill climbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost function.

Uniportal versus Multiportal Thoracoscopic Complex Segmentectomy: Propensity Matching Analysis.

Purpose: Uniportal video-assisted thoracoscopic surgery (VATS) complex segmentectomy has been challenging for thoracic surgeons. This study was designed to compare the perioperative outcomes between uniportal and multiportal VATS complex segmentectomy.Methods: Data on a total of 122 uniportal and 57 multiportal VATS complex segmentectomies were assessed. Propensity score (PS) matching yielded 56 patients in each group. A crude comparison and PS matching analyses, incorporating preoperative variables, were conducted to elucidate the short-term outcomes between uniportal and multiportal VATS complex segmentectomies.Results: The uniportal group had a significantly shorter operation time (173 min vs. 195 min, p = 0.004), pleural drainage duration (2.5 d vs. 3.5 d, p <0.001), and postoperative hospital stay (4.2 d vs. 5.3 d, p <0.001) before matching, and a significant difference was also observed after matching for pleural drainage duration (2.5 d vs. 3.6 d, p <0.001) and postoperative hospital stay (4.5 d vs. 5.2 d, p = 0.001). The numbers of dissected lymph nodes in N1 and N2 stations, the intraoperative and postoperative complication rates were not significantly different between these two groups.Conclusions: The uniportal VATS complex segmentectomy was not inferior to multiportal VATS in terms of perioperative outcomes and therefore should be considered as a viable surgical approach for treatment.