Wide Area Network Research Articles

A Review on Security Frameworks for Software Defined Networks

The internet of thins (IoT) framework has helped in design of high speed connected networks which include Wide Area Networks (WANs). Cognitive Radio can be considered to be one of the key enablers of high speed data transfer with evolving generation of wireless networks. Cognitive networks find their applications in military and defence as well with the advent of internet of things and its allied applications in military warfare. Cognitive Radio Networks often share common resources such as bandwidth or spectrum among several users or stations. Due to continued sharing of resources, cognitive networks often come under security attacks, most common of which are jamming and eavesdropping attacks. In this paper, the basics of cognitive IoT networks have been presented with the focus of security aware cognitive IoT networks. Previous work in the allied domain has been discussed along with their salient features. Keywords: Wide Area Network (WAN), Internet of Things (IoT), cognitive radio network (CRN), security aware spectrum assignment, cyber-attacks, false alarm, and throughput.

LoRaSync: Energy efficient synchronization for scalable LoRaWAN

AbstractLow‐power wide‐area networks connect a large number of battery‐powered wireless devices over long distances. Among them, long range wide area networks (LoRaWAN) implement a pure ALOHA medium access scheme to save device energy by minimizing the radio usage. However, frame collisions restrain the network scalability when the traffic load increases. In this context, synchronization can be used to exploit the available bandwidth more efficiently by controlling the timing of frame transmissions and reducing the collision probability. Such strategy allows to increase the network throughput at the cost of an extra energy demand due to the inherent overhead. In that, the entailed network scenario is still supposed to address low power applications. Therefore this article timely presents LoRaSync, an energy‐efficient synchronization scheme designed for LoRa networks of any size. An accurate clock drift model was established based on measurements made on real cheap devices, and leveraged to support the design of LoRaSync. Our mechanism has been used to evaluate the same ALOHA‐based random access but on a time‐slotted basis, thus increasing the maximum achievable throughput compared to the legacy access. Throughput and energy efficiency models are established to evaluate the performances of a LoRaSync‐operated network. These models are validated with a simulation environment mimicking large‐scale deployments, and then used to determine the most energy efficient slot size for any traffic load. As a final proof of concept, LoRaSync has been implemented and tested on a LoRa testbed to demonstrate the feasibility of our solution on real hardware.

Want More WANs? Comparison of Traditional and GAN-Based Generation of Wide Area Network Topologies via Graph and Performance Metrics

Want More WANs? Comparison of Traditional and GAN-Based Generation of Wide Area Network Topologies via Graph and Performance Metrics

Asynchronous Random Access Systems With Immediate Collision Resolution for Low Power Wide Area Networks

Asynchronous Random Access Systems With Immediate Collision Resolution for Low Power Wide Area Networks

Advancing Performance in LoRaWAN Networks: The Circular Region Grouped Bit-Slot LoRa MAC Protocol

LoRaWAN is an emerging Low-Power Wide-Area Network (LPWAN) technology, widely adopted in various Internet of Things (IoT) applications due to its long transmission range, low power consumption, and robust anti-interference capabilities. However, using the ALOHA medium access control (MAC) protocol in LoRaWAN significantly reduces the packet delivery rate, particularly in high-density networks where end devices (EDs) access the network randomly. It seriously affects the overall network performance. This paper introduces the Circular Region Grouped Bit-Slot CGBS-LoRa MAC protocol to address this challenge. The protocol takes a proactive approach by allocating transmission parameters for end devices, executing regional segmentation based on the distance between EDs and the gateway using different spreading factors. Concurrently, improving the ALOHA access method ensures the efficient communication of EDs in the region. Experimental results demonstrate that our proposed protocol markedly improves the scalability of LoRa networks and minimizes device collisions compared to three other MAC protocols. Even as the LoRaWAN network expands, the proposed protocol maintains a high packet delivery rate and low latency.

Implementing mainstream genetic counseling within the area-wide network of the German Consortium Hereditary Breast and Ovarian Cancer (GC-HBOC): Satisfaction of primary care providers with the provided state-of-the-art training by the Cologne Center.

The German Cancer Society (Deutsche Krebsgesellschaft DKG) has published a position paper to address the challenges of cancer patient care in the era of genomic medicine. The German Consortium Hereditary Breast and Ovarian Cancer (GC-HBOC) has implemented this recommendation in its care concept for families at risk. Core elements are the outcome-oriented evaluation of structured and standardized clinical measures and reporting recommendations derived therefrom to primary care providers and patients. A cross-sector network with certified breast cancer and gynecological cancer centers was founded in 2015, starting from the Cologne Center of the GC-HBOC. To guarantee the knowledge transfer for mainstream genetic counseling, the Cologne center has established an educational program for physicians and specialized nurses in order to pilot trans-sectoral knowledge transfer on risk assessment and risk-stratified care. It consists of face-to-face lectures with written knowledge test, attending a genetic case conference and genetic counseling sessions with the opportunity to counsel under supervision. The lectures were accompanied by a structured evaluation of the participants' satisfaction and feedback of the needs in mainstream genetic counseling. Thereby, the network ensures that genetic counseling and testing is provided according to state-of-the-art knowledge and allows physicians to participate in knowledge-generating care outside the university setting and patients to receive care close to home. After multiple feedback cycles to improve the educational program, the GC-HBOC, in cooperation with the German Cancer Society, has now adopted this concept and developed a common and uniform online curriculum funded by the Federal Ministry of Health. https://www.krebsgesellschaft.de/fortbildung-familiaerer-krebs.html.

A Comparative Study and Analysis of LoRaWAN Performance in NS3

Long Range Wide Area Network (LoRaWAN) is a widely adopted Internet of Things (IoT) protocol due to its high range and lower energy consumption. LoRaWAN utilizes Adaptive Data Rate (ADR) for efficient resource (e.g., spreading factor and transmission power) management. The ADR manages these two resource parameters on the network server side and end device side. This paper focuses on analyzing the ADR and Gaussian ADR performance of LoRaWAN. We have performed NS3 simulation under a static scenario by varying the antenna height. The simulation results showed that antenna height has a significant impact on the packet delivery ratio. Higher antenna height (e.g., 50 m) has shown an improved packet success ratio when compared with lower antenna height (e.g., 10 m) in static and mobility scenarios. Based on the results, it is suggested to use the antenna at higher allevation for successful packet delivery

Machine Learning for Intrusion Detection for Massive IoT Networks

Wide area networks such as fog and internet of things often encounter network level security. There would exist a continued trade-off between the error rate (authentication metric), system overhead, computational complexity and latency of the system. Hence an extremely meticulous system design with appropriate choice of stochastic parameters and authentication scheme should be adopted. In this proposed work, an acceleration learning based LSTM network has been proposed to detect attacks in IoT networks. It can be observed from the obtained results that the proposed system attains better performance compared to previously existing system. The performance enhancement can be attributed to additional features computed and the LSTM with acceleration used to train and further detect errors. Keywords: Internet of Things (IoT), Network Level Security, Neural Networks, Deep Learning, Accuracy, Gateway Utility.

Dynamic relay node selection and routing for cloud-native Software Defined WANs

Cloud Native Software Defined Wide Area Networks (CN-WANs) are proposed to optimize the network performance based on the application requirement. In this paper, we propose a Relay Node Selection and Routing (RNSR) approach to minimize the number of relay nodes in CN-WANs under the end-to-end (E2E) transmission latency constraint. By doing so, we can not only ensure the Service Level Agreement (SLA) to applications, but also reduce the network operation expenses. To improve the system scalability, several algorithms are proposed for networks with different sizes. According to experiments on a prototype built on EC2 and extensive simulations in a custom in-house simulator, we demonstrate that RNSR has a superior performance on reducing the number of required relay nodes, outperforming the state-of-the-art benchmarks by up to 92.48% specifically. In addition, the algorithms designed for large scale networks will not suffer too much performance degradation but significantly improve the system scalability.

A Tree Attenuation Factor Model for a Low-Power Wide-Area Network in a Ruby Mango Plantation

Ruby mangoes are a cultivar with a thick skin, firm texture, red color, no splinters, and thin seeds that is grown in eastern Thailand for export. Implementing a low-power wide-area network (LPWAN) for smart agriculture applications can help increase the crop quality or yield. In this study, empirical path loss models were developed to help plan a LPWAN, operating at 433 MHz, of a Ruby mango plantation in Sakaeo, eastern Thailand. The proposed models take advantage of the symmetric pattern of Ruby mango trees cultivated in the plantation by using tree attenuation factors (TAFs) to consider the path loss at the trunk and canopy levels. A field experiment was performed to collect received signal strength indicator (RSSI) measurements and compare the performance of the proposed models with those of conventional models. The proposed models demonstrated a high prediction accuracy for both line-of-sight and non-line-of-sight routes and performed better than the other models.

Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.

Determining critical nodes in optimal cost attacks on networked infrastructures

A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender’s side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times.

Toward Wireless Smart Grid Communications: An Evaluation of Protocol Latencies in an Open-Source 5G Testbed

Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices’ contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol’s time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G.

Internet adoption in select Nigeria university libraries

The paper examined the Internet adoption in Nigerian university library with emphasis on the extent to which computer networks aid the use of the internet to obtain academic information within the university library. It also determined the extent at which students use the internet to obtain academic information within the university library and to determine the cost, labour and maintenance of Internet adoption in the university library. A descriptive survey design was employed and the convenience sampling method was employed to select 132 samples for analysis. The analytical tools employed are simple percentages, frequency counts, Pearson Correlation and regression analysis via the Statistical Package for the Social Sciences (SPSS). The findings revealed that Local Area Network, Wide Area Network, Internet, Campus Area Network, and Networks were the existing computer networks available in selected Nigerian university libraries. The information services provided using internet include; digital library/ repositories, searching library data, searching scholarly content, file storage, building community power and library automation. The paper recommended that university libraries should be proactive. More academic research could only be achieved with effective internet adoption and integration into university libraries.

ECRLoRa: LoRa Packet Recovery under Low SNR via Edge–Cloud Collaboration

Low-Power Wide-Area Networks (LPWANs), extensively utilized for connecting billions of IoT devices, encounter wireless interference challenges in unlicensed frequency bands. Cutting-edge research suggests employing Received Signal Strength Indication (RSSI) sequences for error detection to mitigate interference-related issues. Nevertheless, the effectiveness of this method significantly declines under low signal-to-noise ratios (SNRs). Additionally, long-range communication often results in low SNR received signals, sometimes even below the noise floor. Targeting this fundamental issue, this article proposes the LPWAN packet technique, broadly applicable across diverse scenarios through edge–cloud collaboration. On the edge side, we propose an innovative architecture that fully exploits spatial distribution and interference independence in the field. Rather than utilizing resource-intensive RSSI-based error detection, we leverage a lightweight coding scheme for error detection at the Long Range (LoRa) edge, forwarding correct frames to the cloud. On the cloud side, packet recovery is achieved utilizing group-weighted voting. We design and implement ECRLoRa with commercially available devices (SemTech’s SX1278 and SX1302 LoRa chipsets) and assess its performance in low SNR environments. Our thorough evaluation demonstrates that our approach attains a Packet Recovery Ratio of 96% with low SNR (i.e., below −10 dB), resulting in 1.8× throughput, 7.5× faster recovery time, and 4.92× average accuracy compared to state-of-the-art cloud-optimized application layer solutions.

On Cyber-Physical Fault Resilience in Data Communication: A Case From A LoRaWAN Network Systems Design

Systems offering fault-resilient, energy-efficient, soft real-time data communication have wide applications in Industrial Internet-of-Things (IIoT). While there have been extensive studies for fault resilience in real-time embedded systems, investigations from cyber-physical systems (CPS) perspective are still much needed, as CPS faults occur not just from abnormal conditions in the software/hardware of the system, but also from the physical environment in which the system operates. At the same time, in addition to conventional fault tolerance strategies embedded in the software/hardware of the target system, CPS faults could be mitigated via some strategic systems re-configuration made available by the physical environment. This paper presents a design and implementation for CPS fault-resilient data communication, in the context of IIoT networks running LoRaWAN, a low-power wide-area networking standard. The proposed design combines collaborative IIoT end devices plus a network gateway piggybacked on a third-party cruising object that is part of the environment. With the focus on data communication, the study illustrates challenges and opportunities to address CPS fault resilience while meeting the needs for energy efficiency and communication timeliness that are common to IIoT systems. The implementation of the design is based on ChirpStack, a widely used open source framework for LoRaWAN. The results from experiment and simulation both show that the proposed scheme can tolerate limited errors of data communication while saving operating energy and maintaining timeliness of data communication to some extent.

Optimizing Link Efficiency: Compression Mechanism Analysis in Wide Area Networks An Enterprise Network Case Study

The study aims to analyze the effectiveness of compression mechanisms in optimising link efficiency in Wide Area Networks (WANs). Using an enterprise network as a case study, the researcher employed OPNET Modeler 14.5 for simulation. Three scenarios were simulated, with two using predefined compression mechanisms. Results suggest that a point-to-point network with a compressed layer 2 payload but uncompressed header, achieves higher throughput than one with header compression, or no compression applied. Additionally, layer 2 payload compression reduces both delay and packet loss. The researchers propose a novel Msiska-Lubobya Improved Payload (MLIP) Compression technique to optimize link efficiency for WAN.

Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization

LoRaWANs play a critical role in various applications such as smart farming, industrial IoT, and smart cities. The strategic placement of gateways significantly influences network performance optimization. This study presents a comprehensive analysis of the tradeoffs between system costs and bitrate maximization for selecting optimal gateway locations in LoRaWANs. To address this challenge, a rigorous mathematical model is formulated to incorporate essential factors and constraints related to gateway selection. Furthermore, we propose an innovative metaheuristic algorithm known as the M-VaNSAS algorithm, which effectively explores the solution space and identifies favorable gateway locations. The Pareto front and TOPSIS methods are employed to evaluate and rank the generated solutions, providing a robust assessment framework. Our research findings highlight the suitability of a network model comprising 144 gateways tailored for the Ubon Ratchathani province. Among the evaluated algorithms, the M-VaNSAS method demonstrates exceptional efficiency in gateway location selection, outperforming the PSO, DE, and GA methods.

A MEASUREMENT APPROACH FOR INLINE INTRUSION DETECTION FOR HEARTBLEED-LIKE ATTACKS IN IOT FRAMEWORKS

Cyber security is one of the most crucial aspects of the Internet of Things (IoT). Among the possible threats, great interest is today paid toward the possible capturing of information caused by external attacks on both client and server sides. Whatever the IoT application, the involved nodes are exposed to cyber attacks mainly through the vulnerability of either the sensor nodes themselves (if they have the capabilities for networking operatively) or the IoT gateways, which are the devices able to create the link between the local nodes of the IoT network, and the wide area networks. Due to the low-cost constraints typical of many IoT applications, the IoT sensor nodes and IoT gateways are often developed on low performance processing units, in many cases customized for the specific application, and thus not easy to update against new cyber threats that are continuously identified. In the framework of cyber attacks aimed at capturing sensitive information, one of the most known was the heart bleed, which, has allowed attackers to remotely read protected memory from an estimated 24–55 percent of popular HTTPS sites. To overcome such a problem, which was due to a bug of the OpenSSL, a suitable patch was quickly released, thus allowing to avoid the problem in most of the cases. However, IoT devices may require more advanced mitigation techniques, because they are sometimes unable to be patched for several practical reasons. In this scenario, the paper proposes a novel measurement method for inline detecting intrusions due to heart bleed and heart bleed-like attacks. The proposed solution is based on an effective rule which does not require decoding the payload and that can be implemented on a low-performance general-purpose processing unit. Therefore, it can be straightforwardly implemented and included in either IoT sensor nodes or IoT gateways. The realized system has been tested and validated on a number of experiments carried out on a real network, showing performance comparable (in some cases better) with the heavier machine learningbased methods.

Stable Byzantine Fault Tolerance in Wide Area Networks With Unreliable Links

Stable Byzantine Fault Tolerance in Wide Area Networks With Unreliable Links