Capabilities Of Nodes Research Articles

Data accumulation in WSNs using a mobile sink: A linear programming approach

Sensor Nodes (SNs) in Wireless sensor networks (WSNs) continuously monitor the environment and accumulate data. But due to the limited memory and processing capabilities of the sensor nodes, the data must be transmitted to the central server for further analysis. To do this, routing via multi-hop channel is used which consumes a lot of particular SN's energy and also utilizes the resources, including the battery and memory of all the SN's involved in routing. This process drains the SN's energy rapidly and further leading to disconnection from the central server. To address this issue, mobile sink concept is introduced. The mobile sink collects the data from the SN's and hands it over to the central server which reduces the SN's involvement in data transmitting, but this process involves computationally complex tasks. In this context, we use linear programming to find the best visiting order to collect the information from a set of prioritized SN's, where other nodes transmit their data to them. We confirm that this process is computationally lightweight. In addition, this approach meat the performance criteria such as the lifespan of WSNs, the usage of buffer, throughput, and delay. We simulate the proposed model using Python, and they show improvement in efficiency when tested with different metrics of WSNs.

Starlet: Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things. To guarantee the network's overall security, we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit. Firstly, we propose the method for dynamic setting of node defense resource thresholds to obtain the defender (attacker) benefit function of edge servers (nodes) and distribution. Secondly, we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes. Subsequently, we use the decomposability and Lipschitz continuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically. Finally, experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

RLQ: Workload Allocation With Reinforcement Learning in Distributed Queues

Distributed workload queues are nowadays widely used due to their significant advantages in terms of decoupling, resilience, and scaling. Task allocation to worker nodes in distributed queue systems is typically simplistic (e.g., Least Recently Used) or uses hand-crafted heuristics that require task-specific information (e.g., task resource demands or expected time of execution). When such task information is not available and worker node capabilities are not homogeneous, the existing placement strategies may lead to unnecessarily large execution timings and usage costs. In this work, we formulate the task allocation problem in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Markov Decision Process</i> framework, in which an agent assigns tasks to an available resource, and receives a numerical reward signal upon task completion. Our adaptive and learning-based task allocation solution, Reinforcement Learning based Queues ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLQ</i> ), is implemented and integrated with the popular Celery task queuing system for Python. We compare <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLQ</i> against traditional solutions using both synthetic and real workload traces. On average, using synthetic workloads, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLQ</i> reduces the execution cost by approximately 70%, the execution time by a factor of at least 3×, and the waiting time by almost 7×. Using real traces, we observe an improvement of about 20% for execution cost, around 70% improvement for execution time, and a reduction of approximately 20× in waiting time. We also compare <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLQ</i> with a strategy inspired by E-PVM, a state-of-the-art solution used in Google's Borg cluster manager, showing we are able to outperform it in five out of six scenarios.

AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges

The paradigm of edge computing has formed an innovative scope within the domain of the Internet of Things (IoT) through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous, distributed and resource-constrained essence of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in Artificial Intelligence (AI) (especially Machine Learning), and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge computing framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology to offer automated and secure AI-based applications at the network edge.

Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning

Abstract Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with respect to task demands, fog node capabilities, information asymmetry, missing information, low trust, transaction failures, and so on. Several machine learning techniques have been proposed for the task offloading in fog environments, but they lack efficiency. In this paper, a novel uncertainty proof Type-2-Soft-Set (T2SS) enabled apprenticeship learning based task offloading framework is proposed which formulates the optimal task offloading policies. The performance of the proposed T2SS based apprenticeship learning is compared and found to be better than Q-learning and State-Action-Reward-State-Action (SARSA) learning techniques with respect to performance parameters such as total execution time, throughput, learning rate, and response time.

Investigating on Black Holes in Segment Routing Networks: Identification and Detection

Network Black Holes (BHs) are logical failures that create a service disruption for a subset of traffic flows, generally due to device misconfiguration. Detection of a BH is a hard task due to its specific nature: the infrastructure is up and the disconnection affects a limited number of flows. An example of BH is the one caused by the failure of the Path MTU Discovery procedure in IPv6. The Segment Routing (SR) Architecture is an overlay infrastructure that provides source routing support by exploiting the connectivity service offered by the underlay IPv6 (SRv6). Thus, SR inherits the problems related to BHs affecting IPv6. In SR this problem is even more stressed due to the encapsulation mechanism that is required to enforce the segment lists on packets. Even worse, existing active probing based tools to detect network BHs for IPv6 are not suitable in SR. In this paper we investigate the problem of detecting SR Black Holes in SR domains. As first, we provide an experimental demonstration of the creation of an SR Black Holes. Then we show that existing tools based on active probing are not suitable to detect SR BHs. Then, a passive framework named Segment Routing Black Holes Detection (SR-BHD) is introduced. SR-BHD makes use of specific traffic counters available in SR capable nodes to verify the validity of the flow conservation principle on each network element. Experimental evaluation carried out through simulation and emulation shows the effectiveness of SR-BHD in detecting the presence of SR BHs.

Multi-Functional Time Expanded Graph: A Unified Graph Model for Communication, Storage, and Computation for Dynamic Networks Over Time

Space-air-ground integrated network (SAGIN) aided multi-tier computing network can be modelled as a dynamic and predictable network. For the SAGIN aided multi-tier computing network, the traditional time expanded graph (TEG) can only jointly model communication and storage capability, as well as one computing function for one mission flow within one same node. However, for multiple computing functions for one mission flow in one same node, TEG is not applicable. In this paper, for SAGIN aided multi-tier computing networks, we propose an multi-functional time expanded graph (MF-TEG) to jointly model the communication, storage, and computation capability of nodes where multiple computing functions for one mission flow in one same node can be characterized. Specifically, based on TEG, for each node having computation functions, we adopt the virtual network graph (VNG) to virtually decompose it into three virtual components: sub-virtual node, virtual computing nodes, and virtual transmission links, where the virtual computing node provides the computing function. We characterize the amount of data flow on each link and also present four kinds of fundamental constraints for the data flow in the MF-TEG for joint communication, storage, and computing function: computation capacity constraints, communication capacity constraints, storage capacity constraints, and flow conservation constraints. We provide one example of using MF-TEG to model the SAGIN aided multi-tier computing network with a service function chain (SFC), where satellite nodes could provide communication, storage, and multiple computing functions for one mission flow in one same node, where TEG is not valid. Furthermore, simulation results show that for SAGIN aided multi-tier computing network, the proposed MF-TEG model significantly outperforms the snapshot graph-aided VNG (SSG-aided VNG) model. The reason for that is only communication and computation capability is considered by the SSG-aided VNG model, while storage capability is not exploited.

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

How Has the Inter-City Corporate Network Spatio-Temporally Evolved in China? Evidence from Chinese Investment in Newly Established Enterprises from 1980–2017

Based on social network analysis (SNA) and the modified Boston Matrix (also known as Market Growth Rate—Relative Market Share Matrix or Four Quadrant Analysis, which was developed by the Boston Consulting Group (BCG) in the early 1970s.), this paper presents the temporal and spatial evolution process of inter-city corporate investment networks in 338 city regions (prefecture level and above) in China from 1980 to 2017. First, we find that inter-city investment linkages formed a diamond-shaped connection structure, with Beijing–Shanghai–Shenzhen–Chongqing as the spatial vertices; moreover, the overall investment network underwent a transition from looseness to aggregation. Second, the spatial layout of module division presents a situation of overall fragmentation and partial agglomeration, and regional integration is increasing. Third, the resource control capability of nodes showed the Beijing unipolar pattern before the 2000s and developed into the Beijing–Shanghai–Hangzhou one-pole, dual-core pattern after the 2000s. The reachability of nodes presents the Beijing uni-polar pattern during all the study years. The core–periphery model of nodes weakened over the study period, and the nodes’ positions have obvious administrative hierarchy and economic level orientation. Fourth, the node potentials and development stages are stronger in the east than in the west overall. Finally, we propose some political implications that the government can consider in order to improve the regional coordination of development.

AoI Minimization for WSN Data Collection With Periodic Updating Scheme

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.

A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation, electrical security, and other aspects. Accordingly, this study proposes a distributed power trading scheme based on blockchain and AI. To protect the legitimate rights and interests of consumers and producers, credibility is used as an indicator to restrict untrustworthy behavior. Simultaneously, the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency, and a weighted communication tree construction algorithm is designed to achieve superior data forwarding. Finally, AI sensors are set up in power equipment to detect electricity generation and transmission, which alert users when security hazards occur, such as thunderstorms or typhoons. The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.

Novel Sensing Hole Recovery with Expanded Relay Node Capability

The occurrence of ‘sensing holes’ not only hinders seamless data collection but also leads to misinterpretation of information in certain areas under extensive data analysis. In order to overcome this, various sensor relocation strategies have been proposed, but the existing relocation strategies revealed problems such as the ping-pong, shaded area, network disconnection, etc. This paper conducted research on relocation protocols in a distributed environment that is very suitable for real-world situations and efficiently recovering the problem of sensing holes. First, a simulation was performed on the distribution of the shaded area for data collection, which is a problem with the existing representative relocation protocol. After that, a data collection capability was newly added to the relay node, which had been in charge of only communication between cluster zones so far, and with this additional functionality, the performance of the revised sensor relocation algorithm was dramatically improved to overcome the existing problems. In addition, the performance and validity of the proposed algorithm were verified through various simulations.

Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines

This paper presents a low-power, self-sustainable, and modular wireless sensor node for aerodynamic and acoustic measurements on wind turbines and other industrial structures. It includes 40 high-accuracy barometers, 10 microphones, 5 differential pressure sensors, and implements a lossy and a lossless on-board data compression algorithm to decrease the transmission energy cost. The wireless transmitter is based on Bluetooth Low Energy 5.1 tuned for long-range and high throughput while maintaining adequate per bit energy efficiency (80 nJ). Moreover, we fieldassessed the node capability to collect precise and accurate aerodynamic data. Outdoor experimental tests revealed that the system can acquire and sustain a data rate of 850 kbps over 438 m. The power consumption while collecting and streaming all measured data is 120 mW, enabling self-sustainability and long-term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> monitoring with a 111 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> photovoltaic panel.

Multi-Task Partial Offloading with Relay and Adaptive Bandwidth Allocation for the MEC-Assisted IoT

The fifth-generation (5G) wireless network is visualized to offer many types of services with low latency requirements in Internet of Things (IoT) networks. However, the computational capabilities of IoT nodes are not enough to process complex tasks in real time. To solve this problem, multi-access edge computing (MEC) has emerged as an effective solution that will allow IoT nodes to completely or partially offload their computational tasks to MEC servers. However, the large communication delay at a low transmission rate for nodes far from the access point (AP) makes this offloading less meaningful. This paper studies joint multi-task partial offloading from multiple IoT nodes to a common MEC server collocated with an AP, and it uses relay selection to help nodes far from the AP. The computation time of all tasks is minimized by adaptive task division and resource allocation (bandwidth and computation resource), and it is solved with an evolutionary algorithm. The simulation results confirm that the proposed method with both relay selection and adaptive bandwidth allocation outperforms the methods with neither or only one function.

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing

How to enable high-performance task offloading and preserve the trust between participants is imperative yet nontrivial to the Cloud-Edge-Device (CED) computing, mainly because the resources are geo-distributed and operated by different parties. Also, the CED participants are highly dynamic and heterogeneous in resource provision and may conflict in interest. This paper proposes BlockChain-empowered CED (BC-CED), a blockchain-empowered collaborative task offloading for CED computing. In BC-CED, blockchain plays a central role in the main functionality of CED, including task offloading, brokerage of resource usage, and incentives. We distinguish the BC-CED from the existing solutions by modifying the blockchain consensus process, enabling the participants to reach an agreement via solving the task offloading problem. For this purpose, we formulate the offloading problem by considering the computation capabilities of candidate nodes and the network performance. BC-CED allows each participant to apply reinforcement learning-based methods to solve this problem and compete for the right of block output by comparing the offloading policy performance and accepting the best policy as the offloading scheme within the next period. We also propose a truthful incentive mechanism to encourage resource contributions in BC-CED and force them to be honest. Extensive tests by implementing our solutions in a commercialized blockchain platform have shown how BC-CED achieves a superior performance in task offloading and blockchain maintenance.

Ranking influential spreaders based on both node k-shell and structural hole

The ranking of individual spreaders aims to measure the influential capability of individual nodes and is important to control information spreading in a network. However, many ranking methods are either degree-based, k-shell-related or a combination of the two, which are not necessarily related to influential capability. Inspired by the strengths of the k-shell decomposition method, this work improves it on the basis of structural holes (SH) and proposes a novel ranking method, SHKS. Different from the efforts that aim only to improve the k-shell decomposition method, this work considers the k-shell and SH-based centrality of a node as well as its neighbors and second-order neighbors. Based on the flexible combination of k-shell and SH, SHKS can identify not only the core nodes with large k-shell indices but also the nodes that have small k-shell indices but play an important role in bridging different parts of a network. Experimental results show that SHKS presents better performance than baseline methods in terms of the Kendall τ correlation results, and the average improvements range from 1.3% to 121.1%. SHKS also has the best monotonicity, and its average monotonicity value on experimental networks is close to 0.99. Moreover, SHKS has good performance in identifying the most influential top-k nodes compared with baseline methods.

Cooperative distributed state estimation: resilient topologies against smart spoofers

A network of observers is considered, where through asynchronous (with bounded delay) communications, they cooperatively estimate the states of a linear time-invariant (LTI) system. In such a setting, a new type of adversary might affect the observation process by impersonating the identity of the regular node, which is a violation of communication authenticity. These adversaries also inherit the capabilities of Byzantine nodes, making them more powerful threats called smart spoofers. We show how asynchronous networks are vulnerable to smart spoofing attack. In the estimation scheme considered in this paper, information flows from the sets of source nodes, which can detect a portion of the state variables each, to the other follower nodes. The regular nodes, to avoid being misguided by the threats, distributively filter the extreme values received from the nodes in their neighborhood. Topological conditions based on strong robustness are proposed to guarantee the convergence. Two simulation scenarios are provided to verify the results.

Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET

Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize intrusion detection, which controls the system to detect additional security issues. Monitoring is essential to avoid attacks and provide extra protection against unauthorized access. Although the current solutions have been designed to defeat the attack nodes, they still require additional hardware, have considerable delivery delays, do not offer high throughput or packet delivery ratios, or do not do so without using more energy. The capability of a mobile node to forward packets, which is dependent on the platform's life quality, may be impacted by the absence of the network node power source. We developed the Safe Routing Approach (SRA), which uses behaviour analysis to track and monitor attackers who discard packets during the route discovery process. The attacking node recognition system is made for irregular routing node detection to protect the controller network's usual properties from becoming recognized as an attack node. The suggested method examines the nearby attack nodes and conceals the trusted node in the routing pathway. The path is instantly assigned after the initial discovery of trust nodes based on each node's strength value. It extends the network's life span and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy consumption, network performance, and detection of attack nodes, the suggested approach is contrasted with AIS, ZIDS, and Improved AODV. The findings demonstrate that the recommended strategy performs superior in terms of PDR, residual energy, and network throughput.

A Novel Machine Language-Driven Data Aggregation Approach to Predict Data Redundancy in IoT-Connected Wireless Sensor Networks

Real world data aggregation and delivery in Internet of Things (IoT) technology are essential to predict and retrieve target data in short time so that the end user feels no delay but ensures a high quality of information. In addition to habitat monitoring and disaster management, these networks have a wide range of other uses, including security and military operations. The processing capabilities of sensor nodes are restricted due to the fact that they have a limited battery life and hence a modest size and processing capacity. WSNs are also susceptible to failure as a result of the limited battery power available. In WSNs, data aggregation is practiced as an energy efficient strategy to reduce computing and transmission latency. It is because of sensor node distribution density that shares the same data at a time data redundancy comes to exist. It is possible to reduce redundancy by adopting a suitable machine learning algorithm while executing the data aggregation process. Researchers are still chasing behind algorithms and modeling strategies effectively to ease the process of developing an effective and acceptable data aggregation strategy from existing wireless sensor network (WSN) models. A three stage framework is proposed for an efficient data aggregation mechanism, and the stages are Modified LEACH, extreme learning machines (ELM), adaptive Kalman filter, and Bi-LSTM. This experiment result shows better performance than the existing methods.