Distributed channel assignment based on congestion information in wireless mesh network

IEEE 802.11a protocol provides 12 non-overlapping channels. If nearby nodes operate on the same frequency channel, they can interfere with each other and produce congestion in the logical links. The use of Multi-Radio, Multi-Channel (MR-MC) can provide more coverage area due to multi-hop forwarding and offer more capacity by simultaneously operating on multiple radios. In this paper, we propose a dynamic, distributed channel assignment scheme for WMN, which is based on node queue length information. The proposed method assigns frequency channels based on queue threshold level which indicates the congestion status of the link. The algorithm does not allow the node to switch to the channel in which nearby nodes are operating. It also keeps record of previously congested channel to avoid assigning the same channel again. Simulation based experiment evaluated the performance in term of Round-Trip time (RTT)

Multi-hop wireless network modelling using OMNET++ simulator

The multi-hop networking plays an important role in wireless coverage area and cost reduction. In this paper, we have presented our experience to design the multi-hop wireless network and explained the realistic behavior using OMNET++ simulator. The simulation is based on proper selection of wireless node, routing protocol and other important parameters. This paper presents a detailed analysis about how INET framework can be used to simulate multi-hop wireless network. We also discussed the important modules and methodology to define simulation parameters and analyze the results for the simple scenario of multi-hop wireless network

Survey of channel assignment algorithms for multi-radio multi-channel wireless mesh networks

Over the past few years, the wireless mesh network (WMN) with a multi-radio multi-channel (MR-MC) has attracted increasingly high attention because of its wider coverage area. The use of multiple radios and the function of multi-hop forwarding allows WMN to achieve a greater capacity and coverage area. MR-MC can be used to utilize the radio spectrum efficiently. However, the performance of WMN is highly affected by several radios operating at frequencies close to each other. This problem can be solved using one of the key techniques called channel assignment (CA). In this paper, we first present the six main constraints of CA algorithms, i.e., interference, delay, routing, connectivity, congestion, and link scheduling. Then, various CA techniques proposed in the literature to improve the performance of WMN are discussed in detail

A Survey on Resource Management in IoT Operating Systems

Recently, the Internet of Things (IoT) concept has attracted a lot of attention due to its capability to translate our physical world into a digital cyber world with meaningful information. The IoT devices are smaller in size, sheer in number, contain less memory, use less energy, and have more computational capabilities. These scarce resources for IoT devices are powered by small operating systems (OSs) that are specially designed to support the IoT devices' diverse applications and operational requirements. These IoT OSs are responsible for managing the constrained resources of IoT devices efficiently and in a timely manner. In this paper, discussions on IoT devices and OS resource management are provided. In detail, the resource management mechanisms of the state-of-the-art IoT OSs, such as Contiki, TinyOS, and FreeRTOS, are investigated. The different dimensions of their resource management approaches (including process management, memory management, energy management, communication management, and file management) are studied, and their advantages and limitations are highlighted

Channel assignment and congestion control in multi-radio multi-channel wireless mesh networks

Wireless Mesh Network (WMN) has been growing rapidly due to its low cost and selforganizing feature. Capacity is one of the most important design goals for WMN. Overall network capacity can be improved by using the Multi-Radios with Multi-Channels (MRMC). IEEE 8021.11a protocol provides 12 non-overlapping channels. In an MR-MC system, the fundamental research problem is the assignment of limited number of frequency channels to the respective radio interfaces. The ultimate objective of this channel assignment (CA) strategy is to reduce the overall network interference and link congestion. If nearby nodes operate on the same frequency channel, they can interfere with each other and produce congestion in the logical links. The MR-MC can provide more coverage area due to multi-hop forwarding and can offer more capacity by simultaneously operating on multiple radios. In this study, a Joint Channel Assignment and Congestion Control (JCACC) scheme for MR-MC WMN has been proposed. The proposed method is based on node queue length information which as-signs the frequency channels based on queue threshold level that indicates the congestion status of the link. OMNET++ simulation tool and graph theory concept have been used to model the network. The algorithm does not allow the node to switch to the channels in which non-intended nodes are operating. JCACC schedules the channel selection mechanism and keeps record of previously congested channel to avoid assigning the same channel again. The simulation based experiment shows the CA for WMN in a quick, efficient and effective manner. The proposed JCACC mechanism provides a more sophisticated solution with 25.16% reduction in round-trip time (RTT) and 24.1% improvement in throughput as compared to previously proposed Distributed Congestion Aware Channel Assignment (DCACA) algorithm

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments : Theoretical Perspective and Challenges

Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device’s communication-related decision making, with the goal of improving performance. In this paper, we explore RL’s potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms

Unraveling Energy Consumption Patterns : Insights Through Data Analysis and Predictive Modeling

Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87

Hybrid Deep Learning: An Efficient Reconnaissance and Surveillance Detection Mechanism in SDN

Software defined network (SDN) centralized control intelligence and network abstraction aims to facilitate applications, service deployment, programmability, innovation and ease in configuration management of the underlying networks. However, the centralized control intelligence and programmability is primarily a potential target for the evolving cyber threats and attacks to throw the entire network into chaos. The authors propose a control plane-based orchestration for varied sophisticated threats and attacks. The proposed mechanism comprises of a hybrid Cuda-enabled DL-driven architecture that utilizes the predictive power of Long short-term memory (LSTM) and Convolutional Neural Network (CNN) for an efficient and timely detection of multi-vector threats and attacks. A current state of the art dataset CICIDS2017 and standard performance evaluation metrics have been employed to thoroughly evaluate the proposed mechanism. We rigorously compared our proposed technique with our constructed hybrid DL-architectures and current benchmark algorithms. Our analysis shows that the proposed approach out-performs in terms of detection accuracy with a trivial trade-off speed efficiency. We also performed a 10-fold cross validation to explicitly show unbiased results