Heterogeneous Network Environment Research Articles

Early Address Detection: A Soft Vertical Handover Approach in Proxy Mobile IPv6

Study’s Excerpt Handover technique for Proxy Mobile IPv6 (PMIPv6) networks through a proactive IP address allocation approach is introduced. It significantly reduces handover-blocking probability and latency, thereby ensuring smoother transitions between WLAN and WiMAX domains. It is proved to be superior to velocity-aware handover methods. Full Abstract This study presents an enhanced handover technique for Proxy Mobile IPv6 (PMIPv6) networks aimed at improving mobility management in heterogeneous wireless environments. The proposed scheme introduces a proactive approach where the anchor Local Mobility Anchor (LMA) obtains IP addresses for the Mobile Node (MN) before it leaves the home network, facilitating faster handovers between Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX) domains. The performance of the proposed scheme was evaluated through both mathematical analysis and network simulation, comparing it with a velocity-aware handover approach. Three key metrics were assessed: handover completion probability, handover latency, and packet loss ratio. Mathematical modeling demonstrated the scheme’s effectiveness in reducing handover-blocking probability and latency across various MN velocities. Simulation results, conducted using NS2.35, further established these findings, showing improvements in the number of successful handovers, reduced handover delay, and decreased packet loss compared to the velocity-aware approach. The study’s outcomes indicate that the proposed scheme offers a more efficient handover mechanism, particularly beneficial for maintaining service continuity in heterogeneous network environments.

A Novel Adaptive Random Access Window (RAW) Slot Adjustment Model for IoT Network Using an Integrated Scheme

The RAW (Restricted Access Window) component of the IoT network is deployed to reduce traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands schedules, collision analysis and send/receive matrices to accurately predict the future of stations’ interactions in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and the Access Point(AP), thereby ensuring a flexible transmission even in a dense network environment. This paper critically investigated, analysed and proposed a novel approach to RAW size adjustment. The new approach will invoke data mining instruments and optimisation algorithms to improve performance of stations in an IoT environment and thereafter simulated (with respect to some specified QoS parameters) to ascertain it’s (output) performance index with the already existing models

Collaborative intrusion detection using weighted ensemble averaging deep neural network for coordinated attack detection in heterogeneous network

Detecting coordinated attacks in cybersecurity is challenging due to their sophisticated and distributed nature, making traditional Intrusion Detection Systems often ineffective, especially in heterogeneous networks with diverse devices and systems. This research introduces a novel Collaborative Intrusion Detection System (CIDS) using a Weighted Ensemble Averaging Deep Neural Network (WEA-DNN) designed to detect such attacks. The WEA-DNN combines deep learning techniques and ensemble methods to enhance detection capabilities by integrating multiple Deep Neural Network (DNN) models, each trained on different data subsets with varying architectures. Differential Evolution optimizes the model’s contributions by calculating optimal weights, allowing the system to collaboratively analyze network traffic data from diverse sources. Extensive experiments on real-world datasets like CICIDS2017, CSE-CICIDS2018, CICToNIoT, and CICBotIoT show that the CIDS framework achieves an average accuracy of 93.8%, precision of 78.6%, recall of 60.4%, and an F1-score of 62.4%, surpassing traditional ensemble models and matching the performance of local DNN models. This demonstrates the practical benefits of WEA-DNN in improving detection capabilities in real-world heterogeneous network environments, offering superior adaptability and robustness in handling complex attack patterns.

MOMTA-HN: A Secure and Reliable Multi-Objective Optimized Multipath Transmission Algorithm for Heterogeneous Networks

With the rapid development of heterogeneous network technologies, such as mobile edge computing, satellite communications, self-organizing networks, and the wired Internet, satisfying users’ increasingly diversified and complex communication needs in dynamic and evolving network environments has become a critical research topic. Ensuring secure and reliable information transmission is essential for stable network operation in these complex environments. Addressing this challenge, this study proposed a secure and reliable multi-objective optimized multipath transmission algorithm for heterogeneous networks to enhance security and reliability during data transmission. The core principle of this algorithm was that multipath transmission can provide additional protection through redundant paths. This redundancy ensured that even if one path is attacked or fails, alternative paths can maintain data integrity and reachability. In this study, we employed the Optimized Non-dominated Sorting Genetic Algorithm II (ONSGA-II) to determine the range of the initial population and filter suitable paths by optimizing them according to different demand objectives. In the path selection process, we introduced an innovative deletion graph method, which ensures that redundant paths do not share any common links with the original paths, except when there are unique links. This approach enhances the independence of transmission paths and improves the security of the transmission process. It effectively protects against security threats such as single points of failure and link attacks. We have verified the effectiveness of the algorithm through a series of experiments, and the proposed algorithm can provide decision-makers with high-reliability and low-latency transmission paths in heterogeneous network environments. At the same time, we verified the performance of the algorithm when encountering attacks, which is superior to other classical algorithms. Even in the face of network failures and attacks, it can maintain a high level of data integrity and security.

Monitoring macrophage polarization with gene expression reporters and bioluminescence phasor analysis.

Macrophages exhibit a spectrum of behaviors upon activation and are generally classified as one of two types: inflammatory (M1) or anti-inflammatory (M2). Tracking these phenotypes in living cells can provide insight into immune function, but remains a challenging pursuit. Existing methods are mostly limited to static readouts or difficult to employ for multiplexed imaging in complex 3D environments while maintaining cellular resolution. We aimed to fill this void using bioluminescent technologies. Here we report genetically engineered luciferase reporters for long-term monitoring of macrophage polarization via spectral phasor analysis. M1- and M2- specific promoters were used to drive the expression of bioluminescent enzymes in macrophage cell lines. The readouts were multiplexed and discernable in both 2D and 3D formats with single cell resolution in living samples. Collectively, this work expands the toolbox of methods for monitoring macrophage polarization and provides a blueprint for monitoring other multifaceted networks in heterogeneous environments.

Exploration and Research on Mobile Device Communication Protocols Based on Heterogeneous Networks

With the widespread adoption of mobile devices and the rapid development of network technology, heterogeneous networks have become a vital component of modern communication systems. This paper aims to explore and research communication protocols for mobile devices based on heterogeneous networks to enhance communication efficiency and user experience. Initially, the paper outlines the composition, characteristics, and key technologies of heterogeneous networks, analyzing the classification, advantages, disadvantages, and development trends of existing mobile device communication protocols. On this basis, a new communication protocol design scheme is proposed, considering the characteristics of heterogeneous networks, aiming to achieve efficient and reliable communication. Through the construction of an evaluation index system and the setup of a simulation environment, comprehensive tests and analyses of the new protocol's performance are conducted. Case studies further validate the effectiveness of the protocol in real-world applications. The research results indicate that the proposed communication protocol can significantly enhance the communication performance of mobile devices in heterogeneous network environments. The findings of this study hold significant theoretical and practical implications for advancing communication technology in heterogeneous networks.

An effective deep-Q learning scheme for QoS improvement in physical layer of software-defined networks

The demands of diverse applications necessitate specific communication requirements, such as jitter, packet loss ratio (PLR), and latency, at the end-to-end (E2E) level within the physical layer of software-defined networking (SDN). In heterogeneous network environments, E2E routes traverse multiple domains with varying quality-of-service (QoS) classes of traffic, posing challenges in meeting unique E2E QoS criteria. Hence the performance on the E2E path is significant to meet the needs of applications. Hence, the issue of providing services on the E2E route is challenging due to several QoS classes in a domain on the source-to-destination route. This paper presents an SDN architecture leveraging Deep Q-learning (DQL) to ascertain the appropriate QoS class for E2E routes in software-defined heterogeneous networks, thereby reducing overall E2E delay. The proposed model with DQL is compared with benchmark reinforcement learning. The proposed framework is validated using various real Internet topologies such as Abilene, USNet, and OS3E, assessing not only E2E delay but also jitter, PLR, and throughput, showcasing its efficacy in optimizing network performance regarding these performance metrics.

Multi-agent DRL-based Multipath Scheduling for Video Streaming with QUIC

The popularization of video streaming brings challenges in satisfying diverse Quality of Service (QoS) requirements. The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, has the potential to improve video streaming performance with multiple simultaneously transmitting paths. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, while applying current multipath schedulers into MPQUIC, our experimental results show that they fail to adapt to various receive buffer sizes of different devices and comprehensive QoS requirements of video streaming. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-agent deep Reinforcement learning (MADRL)-based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and deploy in Dynamic Adaptive Streaming over HTTP system. Then, we compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS.

Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Cluster based Wireless Sensor Network

Wireless Sensor Network highly exposed to certain security attacks due to various uncertainties of the network which lead to node compromise. Hence considerable attention has been made by researcher during scheduling of the mobile sink to data collection of sensed information from the sensor nodes. Many secure data aggregation mechanism has been proposed to withstand the network against these kind of attack. Despite of many advantageous, those architectures will lead to several limitations such as large propagation delay and high energy consumption and spatial and temporal uncertainties. In order to mitigate those challenges, Light Weight Concurrent Scheduling of Mobile Sink routing protocol towards Minimization of Propagation Delay against Spatial and Temporal Uncertainties of Wireless Sensor Network is designed. It should be capable achieving energy efficiency and collision avoidance due to uncertainty in transmission and reception. Proposed model eliminates the packet collision and poor channel utilization challenges. Initially, node is partitioned and cluster head is selected based on the highest energy density of the node in the particular location. Employing Full duplex channel easily examines the spatial and temporal characteristic of the node through utilization cache information of routing table. It further helps to build the light weight scheme with behavioral strategies of the nodes for secure propagation of the sensed data to the mobile sink. Light weight scheme deployed in mobile sinks estimate the node trustworthiness and simultaneously collects the secure aggregated data packets of the sensor node through cluster head against dynamic time slots concurrently .Comparing with traditional secure routing architecture, simulation results demonstrates that proposed architecture can reduce the propagation delay against various uncertainties and maximizes the life time of the network on increasing the networking performances with respect to packet delivery ratio, throughput and propagation delay. Finally proposed model obtains the cooperative communication of sensor node information to the base station through mobile sink on heterogeneous wireless sensor network environment. DOI: https://doi.org/10.52783/pst.387

Research on collaborative edge network service migration strategy based on crowd clustering

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency.

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.

Hybrid radio-over-fiber transport system to support heterogeneous indoor mobile network environments

The recent advances in mobile network technologies are based on pioneering higher radio frequency ranges such as millimeter wave and (sub-)terahertz bands. Most likely, the simultaneous use of multiple bands of such high frequency as well as mid/low frequency ranges should be considered to implement heterogeneous mobile environments where diverse conventional and emerging applications can coexist. This study aims to propose, demonstrate, and evaluate a radio-over-fiber (RoF)-based optical transport system that meets the aforementioned future indoor network’s requirements. In particular, we suggest a novel distributed antenna system architecture that can support next-generation mobile services such as millimeter wave 5G and terahertz 6G as well as commercial 5G systems employing low/mid bands. Following the experimental validation of the downlink and uplink transmission of radio signals, we investigate the cost reductions obtained by implementing the proposed system in comparison to conventional topologies such as point-to-point and wavelength division multiplexing-based point-to-multipoint connections.

User Preference-Based Heterogeneous Network Management System for Vertical Handover

Vertical handover management plays an essential role in wireless network technologies, mainly due to the rapid development of various radio access technologies (RATs) that require users to connect seamlessly from one RAT to another. However, in multiple RAT environments, vertical handover management encounters different challenges, including unnecessary handovers, handover failures, ping-pong handovers, and unsuitable access network selection. Essential in vertical handover management is maintaining the desired quality of service (QoS) by the mobile device user. The seamless movement of mobile device users as they run various applications depends on a well-performing vertical handover decision-making algorithm. This bears special significance in a heterogeneous network environment. This paper proposes a vertical handover algorithm that considers user preferences (i.e., a vertical handover algorithm that evaluates the application currently running on a user device). The main objective of the algorithm is to determine when it is necessary to perform the handover, depending on the applications running on the mobile device. The proposed algorithm utilizes a fuzzy logic system to assess whether the handover is necessary and a multiattribute decision-making (MADM) method to select the best available radio access network. A simulation scenario involving different applications at various mobile device velocities was developed. The results proved the algorithm’s effectiveness compared to some of the earlier proposed vertical handover algorithms. At velocities below 10 m/s and 30 m/s, the proposed algorithm had 0% and 15.02% unnecessary handovers, respectively, while the technique for order preference by similarity to ideal solution (TOPSIS) utility’s function-based algorithm obtained 12.38% and 23.24% at the same velocities, respectively. In addition, compared to TOPSIS, the obtained results of the proposed algorithm demonstrated a lower handover failure rate and ping-pong rate for a velocity span of 1–30 m/s for the considered user applications.

Multi-service access selection algorithm in 5G heterogeneous network

As advancements occur in new wireless communication technology, varieties of network forms coexist and integrate, gradually forming a heterogeneous wireless network environment covered by various wireless networks simultaneously. Owing to disparities in wireless access technologies, network selection needs to consider various factors. Therefore, this paper proposes an access selection scheme using Multi-Attribute Decision-Making (MADM). Considering the diversity of user services, each attribute’s subjective and objective weight is respectively calculated by Analytic Hierarchy Process (AHP) and Standard Deviation (SD) for network service quality, user preference, and available load. Combined with utility functions, the synthetic assessments of evaluated networks are obtained. The results of the simulation indicate that the algorithm combines user preference and service characteristics to effectively select the appropriate access network. At the same time, load balancing is realized to some extent.

An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach

Wireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.

Spectrum Situation Awareness for Future Railway IoT: Challenges, Opportunities and Key Technologies

The heterogeneous network environment in railway Internet of Things networking increases the complexity of network hierarchy "macro-small-micro" multilayer fusion of spectrum resources, and the fast mobility condition in the high-speed railway wireless communication exacerbates the correlation and dynamics of "time-space-frequency" multi-domain coupling of spectrum resources. This article fully considers the impact of high-speed mobile conditions and heterogeneous network environments, and reveals the underlying cause of the poor performance of wireless spectrum opportunistic access of high-speed railways caused by the lack of capabilities in the electromagnetic spectrum space cognition, and proposes our multi-domain / multi-granularity / high dynamic electromagnetic spectrum space cognitive theory based on spectrum situation suitable for future high-speed railways. The article specifies the feasibility, operability and effectiveness of of the spectral situation awareness theory in high-speed heterogeneous environments.

Terminal Access Method of Power Equipment Based on eSIM in WSN Environment

To ensure the integration of wireless sensor network and broadband wireless communication network, we establish a new generation of power IoTs information communication system for smart grid, and propose a distribution automation terminal solution based on eSIM card. An adaptive access algorithm based on multi logic eSIM priority, and a WSN based power acquisition system architecture, are also put forward. Then, according to the statistical analysis results of service arrival, the dialing and location information are integrated into one module. Through terminal classification and mobile modeling, a remote management mechanism based on EUICC is finally established. The system simulation test results show that the strategy adopted in this article can reduce the terminal access blocking rate in heterogeneous network environment, and the support of eSIM in WSN environment increases the integration of power equipment.

Distributed Offloading for Multi-UAV Swarms in MEC-Assisted 5G Heterogeneous Networks

Mobile edge computing (MEC) is a novel paradigm that offers numerous possibilities for Internet of Things (IoT) applications. In typical use cases, unmanned aerial vehicles (UAVs) that can be applied to monitoring and logistics have received wide attention. However, subject to their own flexible maneuverability, limited computational capability, and battery energy, UAVs need to offload computation-intensive tasks to ensure the quality of service. In this paper, we solve this problem for UAV systems in a 5G heterogeneous network environment by proposing an innovative distributed framework that jointly considers transmission assessment and task offloading. Specifically, we devised a fuzzy logic-based offloading assessment mechanism at the UAV side, which can adaptively avoid risky wireless links based on the motion state of an UAV and performance transmission metrics. We introduce a multi-agent advantage actor–critic deep reinforcement learning (DRL) framework to enable the UAVs to optimize the system utility by learning the best policies from the environment. This requires decisions on computing modes as well as the choices of radio access technologies (RATs) and MEC servers in the case of offloading. The results validate the convergence and applicability of our scheme. Compared with the benchmarks, the proposed scheme is superior in many aspects, such as reducing task completion delay and energy consumption.

When UAVs Meet Cognitive Radio: Offloading Traffic Under Uncertain Spectrum Environment via Deep Reinforcement Learning

The emerging Internet of Things (IoT) paradigm makes our telecommunications networks increasingly congested. Unmanned aerial vehicles (UAVs) have been regarded as a promising solution to offload the overwhelming traffic. Considering the limited spectrums, cognitive radio can be embedded into UAVs to build backhaul links through harvesting idle spectrums. For the cognitive UAV (CUAV) assisted network, how much traffic can be actually offloaded depends on not only the traffic demand but also the spectrum environment. It is necessary to jointly consider both issues and co-design the trajectory and communications for the CUAV to make data collection and data transmission balanced to achieve high offloading efficiency, which, however, is non-trivial because of the heterogeneous and uncertain network environment. In this paper, aiming at maximizing the energy efficiency of the CUAV-assisted traffic offloading, we jointly design the Trajectory, Time allocation for data collection and data transmission, Band selection, and Transmission power control ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}^{\mathrm{ 3}}\text{B}$ </tex-math></inline-formula> ) considering the heterogeneous environment on traffic demand, energy replenishment, and spectrum availability. Considering the uncertain environmental information, we develop a model-free deep reinforcement learning (DRL) based solution to make the CUAV achieve the best decision autonomously. Simulation results have shown the effectiveness of the proposed DRL- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}^{\mathrm{ 3}}\text{B}$ </tex-math></inline-formula> strategy.

Конвергенція інтернету речей та систем ситуаційного управління

Quick and accurate provision of information in the processes of situational management ac-quires special importance in the critical areas of application – the prevention and elimination of the consequences of catastrophic events, the management of military operations, medical activi-ties, the management of energy systems, etc. The goals and objectives of activities in these are-as also determine the specifics of building appropriate situation management systems for them. The functioning of such systems is based on obtaining accurate and current information about the environment in which situational management is carried out. The convergence of situation management systems with the means of wireless sensor networks (Wireless Sensor Networks, WSN) and the Internet of Things (IoT) ensures obtaining such information. The article analyzes the means of the Internet of Things from the point of view of their ability to be combined in sit-uation management systems to solve management problems in various spheres of activity. The construction features and advantages of using situational systems based on IoT in various spheres of activity are considered. The convergence of IoT in situational systems of various types requires the solution of IoT-typical tasks related to the organization of a heterogeneous network environment with controlled energy consumption and the need to process large vol-umes of data. The paper demonstrates that the scope of application determines the specific re-quirements for the construction of situation management systems, and in each specific case the system of requirements is focused on the support of a critical requirement. One of the main problems of the convergence of IoT and situation management systems is the difficulty of standardizing the operating environment. The multiplicity and variety of options for possible solutions in the development of such systems determine the need for the use of intelligent tech-nologies for the development of systems with the use of artificial intelligence and knowledge models of the target subject area.