Recent datacenter network topologies are shifting towards heterogeneous and structured topologies for high throughput, low cost, and simple manageability. However, they rely on sub-optimal routing approaches that fail to achieve their designed capacity. This paper proposes a process for designing optimal oblivious routing that is programmed compactly on programmable switches. The process consists of three contributions in tandem. We first transform a robust optimization problem for designing oblivious routing into a linear program, which is solvable in polynomial time but cannot scale for datacenter topologies. We then prove that the repeated structures in a datacenter topology lead to a structured optimal solution. We use this insight to formulate a scalable linear program, so an optimal oblivious routing solution is obtained in polynomial time for large-scale topologies. For real-world deployment, the optimal solution is converted into forwarding rules for programmable switches with stringent memory. With this constraint, we utilize the repeated structures in the optimal solution to group the forwarding rules, resulting in compact forwarding rules with a much smaller memory requirement. Extensive evaluations show our process i) obtains optimal solutions faster and more scalable than a state-of-the-art technique and ii) reduces the memory requirement by no less than 90% for most considered topologies.

Wireless Sensor Networks (WSNs) consist of devices communicating information wirelessly from a monitored field. Sensors are designed with limited energy resources pushing application designers to optimize energy consumption. It is common practice in WSNs to organize nodes in clusters with a device (designed as a cluster head) with superior energy resources. However, this clustered architecture exposes vulnerabilities, particularly to energy depletion attacks targeting the cluster head. Energy depletion attacks pose a significant threat to sensor node survival. To overcome such attacks, we propose a cyber deception defense mechanism based on game theory to model the actions between the attacker agent and the cluster head agent and hence, extract optimal strategies during conflicting interactions between the agents. In this paper, we propose using a game with incomplete information to find the Nash equilibrium point. Cyber deception, particularly the integration of a honeypot system, is employed to enhance the solution’s effectiveness in optimizing cluster head energy in the face of potential attacks. The proposed solution demonstrates its effectiveness in mitigating attacks of varying intensity against the cluster head.

Abstract The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet-connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low-power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource-constrained, with limited computing power, bandwidth, memory, and battery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifically targets neighboring nodes through DIO messages and poses a significant security threat to RPL-based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack; however, they produce high false-positive and false-negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic-based intrusion detection scheme to secure the RPL protocol (FLSec-RPL) to protect against this attack. Our method is built of three key phases consecutively: (1) it tracks attack activity variables to determine potential malicious behaviors; (2) it performs fuzzy logic-based intrusion detection to identify malicious neighbor nodes; and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the effectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static-RPL and Mobile-RPL networks. We compare the performance of our proposed method with that of the state-of-the-art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end-to-end delay, and packet delivery ratio metrics.

Recent datacenter network topologies are shifting towards heterogeneous and structured topologies for high throughput, low cost, and simple manageability. However, they rely on sub-optimal routing approaches that fail to achieve their designed capacity. This paper proposes a process for designing optimal oblivious routing that is programmed compactly on programmable switches. The process consists of three contributions in tandem. We first transform a robust optimization problem for designing oblivious routing into a linear program, which is solvable in polynomial time but cannot scale for datacenter topologies. We then prove that the repeated structures in a datacenter topology lead to a structured optimal solution. We use this insight to formulate a scalable linear program, so an optimal oblivious routing solution is obtained in polynomial time for large-scale topologies. For real-world deployment, the optimal solution is converted into forwarding rules for programmable switches with stringent memory. With this constraint, we utilize the repeated structures in the optimal solution to group the forwarding rules, resulting in compact forwarding rules with a much smaller memory requirement. Extensive evaluations show our process i) obtains optimal solutions faster and more scalable than a state-of-the-art technique and ii) reduces the memory requirement by no less than 90% for most considered topologies.

Android-based mobile devices have attracted a large number of users because they are easy to use and possess a wide range of capabilities. Because of its popularity, Android has become one of the most important platforms for attackers to launch their nefarious schemes. Due to the rising sophistication of Android malware obfuscation and detection avoidance tactics, many traditional malware detection approaches have become impractical due to their limited representation capabilities. Inspired by the success of deep learning in representation learning, this article presents an effective improved deep neural network to safeguard Android devices from malicious apps called AMDI-Droid. The presented approach contains three enhancements: 1) from the ensemble classifier perspective, we propose a new architecture based on a deep neural network, where the predictive outputs obtained from all hidden layers are blended to produce a final prediction; 2) the first hidden layer learns an effective feature representation from the original data through multiple subnetworks; and 3) a loss function is formulated by combining the predictive loss of each base classifier connected to the corresponding hidden layer. The superior performance of the proposed model is verified via intensive evaluations against state-of-the-art techniques in terms of the accuracy, precision, recall,$F1$-score, and Matthews correlation coefficient (MCC) metrics.

Owing to the exponential proliferation of internet services and the sophistication of intrusions, traditional intrusion detection algorithms are unable to handle complex invasions due to their limited representation capabilities and the unbalanced nature of Internet of Things (IoT)-related data in terms of both telemetry and network traffic. Drawing inspiration from deep learning achievements in feature extraction and representation learning, in this study, we propose an accurate hybrid ensemble deep learning framework (HEDLF) to protect against obfuscated cyber-attacks on IoT networks. To address complex features and alleviate the imbalance problem, the proposed HEDLF includes three key components: 1) a hierarchical feature representation technique based on deep learning, which aims to extract specific information by supervising the loss of gradient information; 2) a balanced rotated feature extractor that simultaneously encourages the individual accuracy and diversity of the ensemble classifier; and 3) a meta-classifier acting as an aggregation method, which leverages a semisparse group regularizer to analyze the base classifiers’ outputs. Additionally, these improvements take class imbalance into account. The experimental results show that when compared against state-of-the-art techniques in terms of accuracy, precision, recall, and F1-score, the proposed HEDLF can achieve promising results on both telemetry and network traffic data.

In this paper, we study the problem of how to provide reliable communications for energy harvesting (EH) wireless sensor network (WSN). Using the example of an autonomous quarry, where self-driving trucks autonomously collect and transport goods, there is a need for multiple wireless sensors collecting data about where and when goods can be collected, while guaranteeing reliable operation of the quarry. The vehicles transfer energy to the wireless sensors within range, forming a cluster. The sensors use this energy to transmit data to the vehicles. Finally, the vehicles relay information to an access point (AP). The AP processes the collected information and synchronize the operation of all vehicles. We propose an interference channel selection policy for the sensors-to-vehicles links and vehicles-to-AP links to improve the reliability of the communications, while enhancing the energy utilization. Accordingly, closed-form expression on how to achieve reliable communication within the considered system is derived and numerical results show that the proposed channel selection strategy not only improves the probability of achieving sufficiently reliable communication but also enhances the energy utilization.

Due to their low cost, various range-free localization techniques are widely applied to estimate device locations in wireless sensor networks (WSNs), especially where other communication signals, such as those from a global positioning system, are absent. Among range-free techniques, the centroid algorithm has gained popularity because of its simplicity and low computational cost, rendering it suitable for power-sensitive sensor nodes or nodes. WSN topologies are unpredictable because sensor nodes are often deployed at arbitrary locations, making it difficult for sufficient numbers of known (anchor) nodes to cover all unknown nodes. Consequently, both centroid algorithm and its enhanced versions [e.g., weighted centroid (WC) algorithms] yield relatively high localization inaccuracy. In this paper, to successfully form anchor-node triangles, we propose a low-cost technique to determine the number of virtual anchor nodes or virtual nodes together with their positions. Specifically, we develop and provide proof for accurate approximate unknown node sides. We also improve localization accuracy by adding virtual nodes that collaborate with physical anchor nodes to provide the necessary coverage of the unknown nodes. We address estimations of unknown node locations by applying a fuzzy-based centroid localization method to prioritize anchor nodes by assigning different fine-tuned weighted factors. The results show that the proposed algorithm outperforms state-of-the-art fuzzy-based localization techniques for WC algorithms.

Network congestion is a key challenge in resource‐constrained networks, particularly those with limited bandwidth to accommodate high‐volume data transmission, which causes unfavorable quality of service, including effects such as packet loss and low throughput. This challenge is crucial in wireless sensor networks (WSNs) with restrictions and constraints, including limited computing power, memory, and transmission due to self‐contained batteries, which limit sensor node lifetime. Determining a path to avoid congested routes can prolong the network. Thus, we present a path determination architecture for WSNs that takes congestion into account. The architecture is divided into 3 stages, excluding the final criteria for path determination: (1) initial path construction in a top‐down hierarchical structure, (2) path derivation with energy‐aware assisted routing, and (3) congestion prediction using exponential smoothing. With several factors, such as hop count, remaining energy, buffer occupancy, and forwarding rate, we apply fuzzy logic systems to determine proper weights among those factors in addition to optimizing the weight over the membership functions using a bat algorithm. The simulation results indicate the superior performance of the proposed method in terms of high throughput, low packet loss, balancing the overall energy consumption, and prolonging the network lifetime compared to state‐of‐the‐art protocols.

In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks among the potential SNs to balance the energy, and, finally, adopting a multihop hierarchical routing scheme to reduce the long-distance transmission energy. Simulation results show that our scheme can prolong the overall network lifetime and achieve high throughput, in comparison with a traditional routing scheme and its state-of-the-art variants.

Cellular networking standards organizations such as the 3rdGeneration Partnership Project (3GPP) are currently developing System Architecture Evolution (SAE) as their core network architecture. SAE is all-IP based. However, IP-based networks face several known issues, such as mobility, multi-homing, location privacy, path preference, etc. Mobile IP (MIP) and its variants, such as Mobile IPv6 (MIPv6), Hierarchical MIP, and Proxy MIP, have been developed primarily to alleviate the mobility problem. These variation and extensions, however, still do not provide many of the features required in Next Generation Wireless Networks (NGWN). The limitations are especially due to the overloading of IP addresses as both node identity and locator. In this paper, we propose an extension to MIPv6 called Virtual ID. This concept applies the ID/Locator split idea into a Mobile IPv6 environment. Virtual ID and its extensions provide many features that would be desired in the NGWN. Since our proposed scheme is based on the standard MIPv6 and Proxy MIPv6, the scheme is fully compatible with the legacy MIPv6.

High definition video streams are gaining larger shares of the Internet usage for typical users on daily basis. This is an expected result of the current boom in the online standard and high definition (HD) video streaming services such as YouTube and Hulu. Because of these video streams' unique statistical characteristics and their high bandwidth requirements, they are considered to be a continuous challenge in both network scheduling and resource allocation fields. In this paper we provide a statistical analysis of over 50 high definition video traces that resembles wide varieties of high definition video traffic workloads. We performed both factor and cluster analysis on our collection of video traces to support a better understanding of video stream workload characteristics and their impact on network traffic. Additionally, we compare and evaluate different modeling approaches for high definition videos traces.

We propose a simple model that is capable of capturing the statistical characteristics of mobile video traces encoded using MPEG4-Part2, AVC and SVC-TS encoding standards. The model can be adjusted to adapt to diverse workload configurations, which gives fellow researchers a great flexibility to evaluate different network traffic scenarios. We also discuss the model-based trace generator and the challenges of its implementation. Moreover, we present the simulation results validating the model using different encoding settings. In addition to that, we provide several insights about our video modeling approach. This will help in testing, simulating and validating mobile video transmission and resource scheduling strategies over broadband wireless networks such as WiMAX and LTE.

Many challenges to the Internet including global routing scalability have drawn significant attention from both industry and academia, and have generated several new ideas for the next generation. MILSA (Mobility and Multihoming supporting Identifier Locator Split Architecture) and related enhancements are designed to address the naming, addressing, and routing scalability challenges, provide mobility and multihoming support, and easy transition from the current Internet. In this paper, we synthesize our research into a multiple-tier realm-based framework and present the fundamental principles behind the architecture. Through detailed presentation of these principles and different aspects of our architecture, the underlying design rationale is justified. We also discuss how our proposal can meet the IRTF RRG design goals. As an evolutionary architecture, MILSA balances the high-level long-run architecture design with ease of transition considerations. Additionally, detailed evaluation of the current inter-domain routing system and the achievable improvements deploying our architecture is presented that reveals the roots of the current difficulties and helps to shape our deployment strategy.

The key to proper resource allocation for mobile video on wireless networks is to have a good model for the resource demands. In this paper, we present the results of analysis of a number of mobile video streams and show that a simple seasonal ARIMA model (SAM) can provide a very good representation for both MPEG4-Part2 and MPEG4-Part10 videos, the formats that are commonly used for mobile videos. The model has been implemented to provide both video frame and RTP packet generators. The model can be used to represent different movies and can be easily adjusted to produce different workloads for simulation studies. We use the SAM generator to compare the performance of three different scheduling methods for video over WiMAX networks: Earliest Deadline First (EDF), Deficit Round Robin (DRR) and a combination of the two. The results show that under overload, EDF introduces unfairness. DRR with deadline is fair and gives the best performance.

Interest in broadband wireless access (BWA) has been growing due to increased user mobility and the need for data access at all times. IEEE 802.16e based WiMAX networks promise the best available quality of experience for mobile data service users. Unlike wireless LANs, WiMAX networks incorporate several quality of service (QoS) mechanisms at the Media Access Control (MAC) level for guaranteed services for data, voice and video. The problem of assuring QoS is basically that of how to allocate available resources among users in order to meet the QoS criteria such as delay, delay jitter and throughput requirements. IEEE standard does not include a standard scheduling mechanism and leaves it for implementer differentiation. Scheduling is, therefore, of special interest to all WiMAX equipment makers and service providers. This paper discusses the key issues and design factors to be considered for scheduler designers. In addition, we present an extensive survey of recent scheduling research. We classify the proposed mechanisms based on the use of channel conditions. The goals of scheduling are to achieve the optimal usage of resources, to assure the QoS guarantees, to maximize goodput and to minimize power consumption while ensuring feasible algorithm complexity and system scalability.

In this paper, an explicit rate control framework for Ethernet applications, especially data centers, is described. The framework guarantees zero packet drops at the congested switch and fast convergence to fair and stable state. In order to manage the congestion, design choices on 2-point and 3- point structures, the reactive and proactive signaling, explicit and implicit rate controls are compared. Then the core component of the framework, queue control, is carefully studied. Furthermore, we show that this framework can seamlessly cooperate with IEEE 802.3x PAUSE mechanism to recover from severe congestion scenarios. Numerical results are provided to support the claims.

Wireless broadband technologies like WiMAX2are spreading especially in areas where wired broadband is not expected to reach. Video streaming is continuously acquiring a larger share of Internetpsilas traffic resulting in a need to have a reliable video traffic model. In this paper, we analyze several video streams compressed for mobile streaming and develop their optimal Seasonal ARIMA models. Although these optimal models are very different, we find that a simple model, which we call Simplified Seasonal ARIMA Model (SAM), represents all of the streams very well. This model is ideally suitable for video generation in mobile video simulation studies. We also present the parameter values suitable for such studies.