Cross-layer Architecture Research Articles

A Blockchain-Enabled Trust Management Framework for Energy-Efficient and Secure Routing in Mobile Ad-Hoc Networks

This study principally addresses the challenges associated with streaming videos in wireless Mobile ad hoc networks (MANETs), primarily constrained by wireless channels and node mobility. This study proposes a novel routing protocol incorporating a feedback mechanism and cross-layer architecture, supported by three key enhancements. First, it incorporates a route recovery strategy for detecting link failures and re-establishing connections based on predefined Quality of Service (QoS) metrics. Second, a novel algorithm estimates the available bandwidth, enabling dynamic adaptation of bitrate at the application layer concerning the source video. This novel rate-adaptive approach utilizes a scalable layered structure of the video coding, thereby removing layers that the network cannot efficiently support due to bandwidth limitations. Third, a gateway discovery procedure enhances the connectivity among MANETs and other infrastructure networks. This method employs available bandwidth as a parameter while selecting appropriate gateways and dynamically adjusts operational parameters like proactive area size and advertisement message frequency. The performances of the proposed Enhanced OLSR (E-OLSR) protocol are compared with the state-of-the-art routing procedures such as Adhoc on-demand distance vector (AODV), destination sequenced distance vector (DSDV), and optimized link state routing (OLSR), in terms of metrics like packet delivery ratio (PDR), end-to-end (E2E) delay, throughput, as well as energy consumption. The simulation results demonstrated notable improvements, including reduced packet delay, fewer dropped packets, and decreased link failures, indicating effective utilization of available bandwidth. The overall performance of the proposed E-OLSR protocol exceeds other traditional protocols and its security is enhanced through the integration of Blockchain Technology.

Spatio-temporal fragmentation-aware time-varying service provisioning in computing power networks based on model-assisted reinforcement learning

Widespread application of AI with high computing requirements has promoted the rapid development of cloud and edge computing. Ubiquitous computing resources need to be interconnected through high-performance IP and optical networks. Computing power networks (CPNs) have been studied as an IP/optical cross-layer architecture to provide on-demand computing services. The service provisioning in CPNs can be modeled as a virtual optical network embedding problem; however, the time-varying characteristics of service requirements increase the complexity. In particular, there can be stranded computing or bandwidth resources that are isolated in the spatio-temporal dimension and therefore difficult for other requests to utilize. This phenomenon is referred to as spatio-temporal resource fragmentation, which will lead to inefficient use of resources and higher energy consumption. To meet the time-varying requirements and achieve an energy-efficient CPN, in this paper, we first analyze the main reason for spatio-temporal resource fragmentation and excess energy consumption in cross-layer CPNs, which is the mismatched embedding scheme and the inappropriate scheduling order. Therefore, an auxiliary-graph-model-assisted edge-featured graph-attention-network-enabled DRL algorithm, DeepDefrag, is studied to solve the problem. Its core highlight is co-optimizing the scheduling and embedding of time-varying virtual networks along with the awareness of resource fragmentation. In addition, an integer linear programming algorithm is developed to determine the performance bounds. We validate DeepDefrag through a series of ablation studies and comparison algorithms under different numbers of requests. The results show that DeepDefrag can achieve >26.4% lower energy consumption for CPNs compared with the DRL-based baseline. Moreover, it is shown to generalize well across requests and networks.

Energy harvesting in self-sustainable IoT devices and applications based on cross-layer architecture design: A survey

The Internet of Things (IoT) is envisioned to become a driving force in the evolution of fifth-generation (5G) mobile networks, autonomous continuous monitoring and control platforms, and low-power consumption devices. Conventionally, these devices are battery-operated. The battery lifespan degrades faster in the case of continuous monitoring devices. Limited battery lifetime motivates us to investigate an eco-friendly solution to solve the issue of limited battery life and frequent replacement. Energy harvesting (EH) is a key-enabling technique that provides a viable solution to the challenge at hand. EH minimizes battery dependence by collecting energy from ambient sources. Although several studies have been conducted on EH-IoT networks, a tutorial on a possible mapping between the use cases/application to the IoT devices and the IoT devices to EH power source still needs to be included. To address this gap, we classify IoT devices based on their applications. Based on the different interfacing sections of layer architecture design of IoT device, this paper tries to fill the gap between IoT applications and the self-sustainable IoT system design. Next, each interfacing section is categorized into layer architecture of IoT device to study layer-specific power requirements (or demand) versus EH sources (or supply). We have considered various applications like agriculture, healthcare, industry, city, security, transportation, and water management. Further, we discuss different energy sources to harness ambient energy and the method to store the harvested energy. Then, the widely used standards and the various steps taken for the interoperability and compatibility of EH-IoT are outlined to understand the elements of the EH interface. Finally, we point out future research issues requiring specific attention for deploying self-sustainable IoT-based delay-limited applications.

Distributed resource allocation model with presence of multiple jammer for underwater wireless sensor networks

Underwater-wireless sensor network (WSN) are prone to the jamming attacks; mainly in case of reactive jamming. Reactive jamming has emerged as one of the critical security threat for underwater-WSN; this occurs due to the reactive jammer capabilities of controlling and regulating jamming duration. Further reactive jammer possesses low detection probability and high vulnerability; moreover the existing model has been designed in consideration with terrestrial-WSN. Hence these models possesses limited capabilities of detecting the jamming and distinguish among uncorrupted and corrupted packets; also it fails to adapt with the dynamic environment. Furthermore co-operative mechanism of jamming model is presented for utilizing the resources in efficient way; however only few existing work has been carried out through the co-operative jamming detection; especially under presence of multiple jammer nodes. For overcoming research issues this paper presents distributed resource allocation (DRA) model adopting cross layer architecture under presence of multiple jammer. DRA algorithm is designed for allocating resource to jammer user in optimal manner. Experiment outcome shows the proposed DRA model achieves much better detection rate considering multi-jammer environment. Thus aid in achieving much better detection accuracy, packet drop, packet transmission and resource utilization performance.

Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity

We consider multipath TCP (MPTCP) flows over the data networking dynamics of IEEE 802.11ay for drone surveillance of areas using high-definition video streaming. Mobility-induced handoffs are critical in IEEE 802.11ay (because of the smaller coverage of mmWaves), which adversely affects the performance of such data streaming flows. As a result of the enhanced 802.11ay network events and features (triggered by beamforming, channel bonding, MIMO, mobility-induced handoffs, channel sharing, retransmissions, etc.), the time taken for packets to travel end-to-end in 802.11ay are inherently time-varying. Several fundamental assumptions inherent in stochastic TCP models, including Poisson arrivals of packets, Gaussian process, and parameter certainty, are challenged by the improved data traffic dynamics over IEEE 802.11ay networks. The MPTCP model’s state estimation differs largely from the actual network values. We develop a new data-driven stochastic framework to address current deficiencies of MPTCP models and design a foundational architecture for intelligent multipath scheduling (at the transport layer) considering lower layer (hybrid) beamforming. At the heart of our cross-layer architecture is an intelligent learning agent for actuating and interfacing, which learns from experience optimal packet cloning, scheduling, aggregation, and beamforming using successful features of multi-armed bandits and federated learning. We demonstrate that the proposed framework can estimate and optimize jointly (explore–exploit) and is more practicable for designing the next generation of low-delay and robust MPTCP models.

A secure cross-layer architecture for reactive routing in vehicle to vehicle (V2V) communications

Vehicular communication is one of the essential technologies for increasing road safety, traffic efficiency, and comfort for pedestrians and drivers. In this context, the internet of vehicles is an emerging paradigm. However, with advances in vehicular communication, security threats have also emerged. Several vulnerabilities exist in vehicular communications, including Denial of Service (DoS), black hole attacks, and fabrication attacks. A malicious attack alters the packet information in a fabrication attack, causing congestion and high delays in the vehicular network. We propose two algorithms to protect the routing protocols in a vehicle-to-vehicle scenario against several attacks that target confidentiality, authentication, privacy, and integrity. The first algorithm detects the malicious behavior of each vehicle by calculating the percentage of modified destination addresses. If it exceeds a predetermined threshold, this vehicle is classified as malicious. Otherwise, it is a normal vehicle. The second algorithm detects malicious modifications based on the Signal to Interference Ratio (SIR) by monitoring the SIR value, adjusting the distance, altering the power received, and changing the transmitted power value. We performed simulations using the SUMO 0.22 simulator and Network Simulator (NS). The results obtained show an improvement in End-to-End (E2E) delay, Packet Delivery Ratio (PDR), and reduced overhead.

New Mobility Model for Mobile and Geographic Cross-layer Architecture for Mobile IoT Networks

The paper proposes a new mobility model. For a mobile and geographic cross-layer architecture. This cross-layer architecture allows service differentiation between real-time and best-effort traffic. for a mobile internet of things (IoT) network. We present a mathematical model, which works on the parameters of the MAC layer and the network layer. The mathematical model makes it possible to calculate the throughput, between a source object and a destination object (gateway). Under conditions of stable queues. We use the positional (geographic) information in the mathematical model of the proposed cross-layer architecture. To study the impact of mobility on the performance of the proposed mathematical model. The proposed mobility model defines two-time intervals. The first interval for communication (transmission and reception of data without mobility at the same time). And the second time interval for the mobility of objects of the IoT network (without communication at the same time). The results show that the calculated throughput depends on the position of the objects, as well as the probability of transmission. Finally, we study the effect of distance and speed on the performance of the proposed cross-layer architecture.

A cross-layer based optimized feature selection scheme for intrusion detection in wireless sensor network

The Wireless Sensor Networks (WSNs) contain a significant quantity of sensor nodes that computes and communicates for data transmission. The data packet sensed and transmitted contains various cross layer feature set that includes many important information. Many essential aspects, which include storage capability, consumption of energy, and, computational power should be taken into account while dealing with the data packets. On the other hand, many past researchers have carried out their work in order to detect intrusion utilizing cross-layer packets but fail in detecting them at the same time. Cross-layer and feature selection techniques play a key role in building an efficient Intrusion Detection System (IDS). An advantage of using the cross-layer technique is to achieve a higher correlation among different layers of the protocol so that one layer can use the parametric information of the other layer by breaking the traditional layer barriers. In this work, we propose a cross-layer based multi-feature selection model for intrusion detection in WSNs. Firstly, an optimized multi-feature selection algorithm is proposed for selecting efficient and useful features from the cross-layered architecture of the network. Secondly, a multi class intrusion detection model is proposed for the classification of different cross-layer based intrusion in the network. The proposed algorithm is developed for providing total security to cross-layer based networks by selecting prominent features and detecting intrusion at the same time. The simulation results are utilizing on real-time intrusive data from the network by analyzing the proposed model.

A Strategy-Based Formal Approach for Fog Systems Analysis

Fog systems are a new emergent technology having a wide range of architectures and pronounced needs making their design complex. Consequently, the design of fog systems is crucial, including service portability and interoperability between the various elements of a system being the most essential aspects of fog computing. This article presents a fog system cross-layer architecture as a first step of such a design to provide a graphical and conceptual description. Then, a BiAgents* (Bigraphical Agents) formal model is defined to provide a rigorous description of physical, virtual, and behavioural aspects of Fog systems. Besides, this formalisation is implemented and executed under a Maude strategy system. The proposed approach is illustrated through a case study: an airport terminal Luggage Inspection System (LIS) while checking the correctness of its relevant properties: the portability of data and their interoperability. The integration of the Maude strategies in the rewriting of Fog system states made it possible to guide the execution of the model and its analysis.

Robust and Fast Converging Cross-Layer Failure Correction in Segment-Routed Networks

Due to overlay technologies, service providers have a logical view of the underlay network and can optimize the experience quality without modifying the physical network. However, the cross-layer interaction inevitably causes network fluctuation due to their inconsistent optimization objectives. Aside from that, network failures that occur in both layers not only cause network performance degradation but also significantly increase the frequency of cross-layer interaction. These problems make the network fluctuate for a long time, reduce the network performance, and influence the user experience, especially for time-sensitive applications. In this paper, we design a cross-layer architecture in which the logical layer can satisfy the service function chain demands and maximize the user experience and physical layer so it can optimize the overall network performance. Our cross-layer architecture can make proactive corrections in both layers. Furthermore, we investigate the cross-layer interaction and design two strategies to eliminate fluctuations and make the network converge quickly.

A survey on FANET routing from a cross-layer design perspective

With the introduction of UAVs to networking, ad hoc communications have evolved past confinement to the terrestrial grid and have moved towards aerial meshes. Until now, Flying Ad-hoc Networks (FANETs) have been relying on strictly layered communication protocols for their function and routing, a tradition set by conventional networks. With layers of said protocols functioning as ”black boxes”, any form of interaction between non-adjacent layers constitutes a direct violation of the protocols’ architecture. The work presented in this survey intends to examine existing protocols of both legacy and cross-layer architectures in terms of their potential in accommodating routing in FANET deployments. Special attention is given to multi-altitude (3D) deployments, where a substantially greater amount of processing and packet route complexity is observed, and a greater amount of node location precision is required. The potential of cross-layer designs is expressed as a function of power budgeting, mobility (and awareness thereof), security, and resource allocation, given their importance for efficient control of flying ad hoc networks.

Quality-of-Service Prediction Techniques for Wireless Sensor Networks

The rapid development of smart sensor nodes generates great interest in Wireless Sensor Network (WSN). The distinct applications of these networks have evolved in the field of border surveillance, Internet of Things, agriculture, healthcare, and disaster monitoring. It is a complex task to satisfy the need for Quality-of-Service (QoS) in a real-world environment, due to the size and dynamic nature of the mobile sensor nodes. This paper addresses the QoS metric based on the layered architecture of WSN. There are two types of layered architectures, namely classic-layered architecture and cross-layered architecture. In a classic-layered architecture, QoS is achieved based on the individual layer. Due to the interconnected nature of these layers, it is difficult to achieve the overall QoS in a classic-layered architecture. This challenge is resolved with the help of cross-layered architecture. Further, the proposed statistical analysis illustrated the fact that the QoS metric enhances the performance of the network in terms of latency, throughput, energy, and reliability. Finally, the machine learning approaches are discussed in the light of QoS metrics to enhance the overall performance of the network.

Full Duplex Physical and MAC Layer-Based Underwater Wireless Communication Systems and Protocols: Opportunities, Challenges, and Future Directions

Underwater wireless communication has gained a great deal of attention in the last couple of decades because of its applications in the military, industrial, and monitoring sectors. Despite the extreme physical and MAC layer difficulties, acoustics are used for various applications among the various modes of underwater communication technologies used. While significant research efforts have been made to address these issues, the bottleneck remains in achieving high bandwidth, high throughputs, and data rate. Researchers have begun to look into full duplex (FD) implementation to improve bandwidth efficiency and increase data rate and throughput. Users can send and receive data simultaneously over the FD links, maximizing bandwidth utilization and increasing throughput. As a result, we thoroughly reviewed various FD physical layered UWAC systems and MAC layered protocols for underwater communication. The various problems that the aforementioned systems and protocols have faced, as well as the solutions suggested in previous works to solve each problem, are also highlighted. Various metrics are used to compare the performance of various physical layered FD systems and FD MAC protocols. We also explore some of the open research questions in these FD-physical layered and MAC layered protocols, as well as future research directions. Based on ample information, we suggest a cross-layered architecture based on various IBFD-SI cancellations, DA-CSMA, and FD-MAC protocols. This review provides a broad view of the current FD physical and MAC layered protocols based on acoustic communication, as well as recommendations.

JSMCRP: Cross-Layer Architecture based Joint-Synchronous MAC and Routing Protocol for Wireless Sensor Network

The exponential rise in wireless technologies and allied applications has revitalized academia-industries to develop more efficient and economic routing solution to meet Quality-of-Service (QoS) provision. Amongst the major wireless communication systems, Wireless Sensor Network (WSN) is the most sought technology for defense surveillance, healthcare monitoring, industrial monitoring and control, civic and strategic infrastructure surveillance etc. Additionally, the up surge in Internet of Things (IoT) and Machine to Machine (M2M) communication systems too have broadened the horizon for WSNs based communication. However, the parallel increase in communication environment, complex network conditions etc confine efficacy of the classical WSN protocols. Dynamic network and node conditions often force classical protocols to undergo pre-mature link outage, delayed transmission, energy exhaustion and eventual QoS violation. To address such issues enhancing Median Access Control (MAC) and allied routing decision can have vital significance. However major existing efforts either focus on MAC enhancement or routing optimization, even under static WSN topology. Considering it as motive in this paper a highly robust “Cross-layer architecture based Joint-Synchronous MAC and Routing Protocol for WSN communication (JSMCRP)” has been developed. Being cross layer model JSMCRP protocol employs Application Layer, Network Layer, MAC Layer and PHY Layer to perform Network Adaptive MAC scheduling and Dynamic Routing Decision. JSMCRP employs Data Traffic Assessment, Prioritization and Scheduling (DTAPS), Proactive Network Monitoring and Knowledge (PNMK), Dynamic Congestion Index Estimation (DCIE), Adaptive Link Quality, Packet Injection Rate and Cumulative Rank Sensitive Routing Decision (CRSRD) to perform routing decision. Additionally, exploiting dynamic network/node conditions it performs Cognitive MAC scheduling to ensure QoS centric communication over IEEE 802.15.4 protocol stack. JSMCRP exhibited higher PDR, lower PLR and low delay under varying network conditions, suitable for real-time communication over constrained mobile WSN conditions.

A QoS-Based Medium Access Control Protocol for WBANs and Its Performance Evaluation

Nowadays, Wireless Body Area Networks (WBANs) are used in many fields. WBANs are described as small sensor nodes that communicate wirelessly and provide services to the personal area. Quality of Service (QoS) is an essential issue for WBANs due to the importance of human life. QoS problems can only be solved with a robust Medium Access Control (MAC) protocol in WBANs. To find a solution to this problem, developers performed many MAC protocols for WBANs. ISO/IEEE 11073 health informatics defines the standard of personal health information and purposes to provide interoperability between medical technologies. This paper presents a MAC protocol that provides ISO/IEEE 11073 communication standards with QoS support, bases on cross-layer architecture. We designed a slot assignment scheme, prioritization mechanism, admission control mechanism to provide QoS. The performance evaluation of the proposed MAC protocol is compared with IEEE 802.15.4 and IEEE 802.15.6 protocols by considering end-to-end delay, packet loss ratio, and throughput parameters, and it has achieved out performance. It is observed that the proposed protocol doesn't exceed 45 ms delay, reached 81% traffic load, and a maximum error rate of 0.162%.

A Cross-Layer Architecture with Service Adaptability for Wireless Multimedia Networks

A Cross-Layer Architecture with Service Adaptability for Wireless Multimedia Networks

Time-Critical Wireless Networked Embedded Systems: Feasibility and Experimental Assessment

In this article, we investigate an innovative solution, to implement high sampling frequency industrial control by means of networked embedded systems connected via WiFi. The basic idea relies on a co-design approach for the control application, which is then able to adapt its sampling period, as well as to tune the Wi-Fi parameters, according to the feedback coming from the network. To this end, we implemented a cross-layer architecture acting at both application and data-link layers, which features a robust frame-delay state estimator, a time-efficient communication policy, and a specific tuning of the critical protocol parameters. Suitable hardware-in-the-loop experiments have been carried out exploiting two different embedded systems available off-the-shelf. The preliminary results, obtained from an extensive experimental campaign, are encouraging since they show that the proposed architecture enables industrial control applications requiring a sampling rate up to 1000 Hz, even in presence of communication impairments.

LoRa based architecture for smart town traffic management system

While traffic congestion has been pointed out as everyday driving stress, few attempts are specialized in traffic management by using current IoT technology. In order to help alleviate traffic stress from drivers, this article proposes a cross-layer LoRa architecture and a machine-learning algorithm for smart town’s traffic management systems. LoRa is selected since it has strengths in range and power when compared to other wireless communication technologies. We introduce the cross-layer LoRa architecture, which is devised to facilitate its cognitive analysis. By dynamically allocating network and information resources, it complements the limitations of the standard LoRa protocol. We also have designed the logistic regression algorithm-which runs above its cognitive engine. The proposed algorithm outputs traffic coefficients based on density and travel time. This algorithm has achieved 97% of accuracy in the simulation. With further research, we believe the proposed system could be an excellent solution for smart traffic management.

Optimal Huffman Coding Performance of Ad-Hoc Networks Based on Cross-Layer Design.

Effective data compression and sustainable management of limited bandwidth are the fundamental challenges in the design of current ad-hoc wireless networks. Dynamic Huffman coding protocol with optimal prefix-free codewords assigned to network parameters through cross-layer methodology can considerably address these issues for performance enhancement in these networks. In this paper, the entropy based Huffman binary and ternary coding schemes are implemented with cross-layer design in mobile ad-hoc networks. This cross-layer approach for achieving higher adaptivity incorporated four layers of the traditional networking stack. The simulation results demonstrated the performance tradeoff between throughput and coding metrics for the proposed cross-layer architecture. Moreover, our proposed model exhibits substantially higher compression ratio and coding efficiency than the other existing methods through the integration of this lossless coding protocol in the developed cross-layer framework.

Blockchain-enabled logistics finance execution platform for capital-constrained E-commerce retail

As one of the most prevailing retail channels, E-commerce has nowadays facilitated retailers to sell goods to customers worldwide and tremendously increased the supply chain efficiency by removing most intermediate links. The broaden business scope and accelerated goods circulation, nevertheless, have generally led to capital shortages for retailers, especially small and medium enterprises (SMEs). Given the SMEs’ difficulty in acquiring capital from financial institutions such as banks, logistics finance (LF) has emerged as an alternative, which is the combination of logistics and financial service. However, the frequent order fulfillment and diversity of pledges in E-commerce hinder SMEs in meeting LF's financing requirements. Furthermore, the current LF relies more on large and reputable third-party logistics (3PLs) to alleviate financing risks, which in turn raises the entry threshold for other 3PLs. Hence, this paper has proposed a blockchain-enabled logistics finance execution platform (BcLFEP) as an integrated solution to facilitate LF for E-commerce retail. A cross-layered architecture is proposed to organize and manage involved resources, workflows and decisions based on the object-oriented methodology (OOM). A hybrid finite state machine-based smart contract (HFSM-SC) is designed to associate and coordinate with all kinds of agents for LF operations throughout its lifecycle. Moreover, blockchain is integrated with agent technology to construct a blockchain-enabled multi-agent system (BcMAS), providing a trusted runtime environment to more autonomously and efficiently execute smart contract. Finally, a case study is conducted to implement BcLFEP-enabled dynamic pledge management for verification and evaluation.