Cross-layer Approach Research Articles

Survey on Opportunistic Routing Protocols in Multihop Wireless Networks

Routing is portrayed as one of the most important prevailing challenges in research with reference to multi-hop networks in a wireless environment. Opportunistic routing (OR) protocol is an emerging area related to research, due to the improvement in communication reliability, compared to the traditional routing models. The major perception related to OR is to determine a group of neighboring node candidates, named as a candidate set using the advantages of broadcast capability of the wireless medium thereby to collaboratively transmit data packets towards the destination using the coordination of the forwarded candidate set. The design and performance of OR protocols over multi-hop wireless networks mainly depend on the processes of forwarding selection of candidates and assignment of priorities. Therefore, the researchers have designed and developed several different algorithms for those OR processes. In this paper, following a short outline on traditional routing and OR protocols, metrics involved in the design of existing OR protocols, classification of OR based protocols, and hurdles in the design of OR protocols over multi-hop wireless networks are examined. More precisely, the OR protocols are divided into two categories, based on the forwarding candidate set selection and forwarding candidate coordination methods. Furthermore, the most significant challenges of OR protocol design, such as prioritization of forwarding candidates, utilizing the cross-layer approach for candidate coordination, and achieving the quality of service also investigated.

Cross-Layer Adaptation with Safety-Assured Proactive Task Job Skipping

During the operation of many real-time safety-critical systems, there are often strong needs for adapting to a dynamic environment or evolving mission objectives, e.g., increasing sampling and control frequencies of some functions to improve their performance under certain situations. However, a system's ability to adapt is often limited by tight resource constraints and rigid periodic execution requirements. In this work, we present a cross-layer approach to improve system adaptability by allowing proactive skipping of task executions, so that the resources can be either saved directly or re-allocated to other tasks for their performance improvement. Our approach includes three novel elements: (1) formal methods for deriving the feasible skipping choices of control tasks with safety guarantees at the functional layer, (2) a schedulability analysis method for assessing system feasibility at the architectural layer under allowed task job skippings, and (3) a runtime adaptation algorithm that efficiently explores job skipping choices and task priorities for meeting system adaptation requirements while ensuring system safety and timing correctness. Experiments demonstrate the effectiveness of our approach in meeting system adaptation needs.

Energy Efficient Data Gathering in IoT Networks With Heterogeneous Traffic for Remote Area Surveillance Applications: A Cross Layer Approach

In this paper, the problem of energy-efficient data gathering in an Internet of Things (IoT) based remote area surveillance application is addressed by designing a suitable MAC layer uplink solution. We follow the 3GPP specified scheduled access scheme for narrowband IoT (NB-IoT) and consider presence of both delay sensitive and delay tolerant traffic in the network. First, we present a mixed integer non-linear programming (MINLP) based optimization framework to minimize the IoT devices’ cumulative transmission energy per uplink frame under the constraints of finite resource blocks, mean queue stability and residual energy levels of IoT nodes. A cross layer approach has been adopted. Precisely, using Lyapunov optimization, we propose a dynamic, distributed transmit power allocation for IoT nodes and a centralized node scheduling scheme. Further, assuming Poisson model for traffic generation at delay tolerant traffic generating nodes, we suggest a distributed probabilistic sleep scheduling scheme to improve the average delay experience of delay sensitive traffic while improving the overall energy conservation. Simulation results suggest impressive performance of our proposed solution over the existing uplink solution for NB-IoT, in terms of delay experience of delay sensitive traffic, buffer length requirement and nodes’ total energy consumption at high traffic load.

DETECTION OF BLACK HOLE ATTACK USING CROSS LAYER CONCEPT IN MANET

Mobile Nodes can communicate mutually if it falls in the sensing range of the neighboring nodes. Connected nodes can transmit, receive and forward packets to connected nodes. In this paper, we focus on detection of Black Hole attack in the MANET using the cross-layer techniques that can optimize the energy consumption. The proposed cross-layer framework contains three layers and uses parameters like mobile nodes, area and the data in network layer. The MAC and data-link layer is used as a sub-layer, and a full-duplex interface is developed through the physical (PHY) layer. Our focus here is make a secure mechanism particularly to counter Black Hole Attacks through cross-layer proposal. The proposed framework is simulated using NS-3 version NS-3.17. Our examination observes the correlation between Cross Layer and Black Hole Cross-Layer scenario. This correlation is noticed between the simulation time and mobile nodes in view of the QoS parameter like execution time, redundancy gain, average end to end delay, average throughput, and network reliability. A redundancy gain performing 100% in Cross Layer with respect to Black Hole Cross Layer, thus increases the performance of the network. The simulation results show that Cross Layer approach and detection of Black Hole in Cross Layer Network is better than Black Hole Cross Layer.

Cross-Layer Routing for Outage Minimization in Multihop Ad-Hoc Networks

In this study, cross-layer approach for joint routing and power allocation problem is formulated in an optimization framework for end-to-end outage minimization under the constraint of total permissible transmission power. A closed form solution for optimal transmission power is obtained following the extraction of routing metric. The scheme is referred as minimum end-to-end outage probability (MEO) strategy. A distributed implementation of the proposed strategy is also presented. Simulation results prove that our proposed MEO routing and power allocation strategy succeeds in achieving significant improvement of end-to-end outage probability over MEO routing and equal power allocation scheme.

An optimal energy-throughput efficient cross-layer solution using naked mole rat algorithm for wireless underground sensor networks

Wireless Underground Sensor Networks (WUSNs) have emerged as prominent solution for variety of applications such as monitoring of underground pipelines, maintenance of power grids, smart and precision agriculture, sports fields maintenance, keeping check on underground oil leakage and many more. Whereas traditional electromagnetic wave communication mechanism doesn’t work good for such sensor networks, the magnetic induction (MI) technique offers stable communication channel conditions and therefore, is considered better option by researchers. As well as communication protocols are concerned in light of tough underground conditions, cross-layered approach has proven to be better in comparison to conventional layered protocol approach. In this article, a distributed cross-layer solution for MI-operated WUSNs will be proposed, which is based on a novel optimization approach - Naked Mole Rat algorithm (NMRA). This Distributed Energy-Throughput Efficient Cross-Layer solution using NMRA approach, also called as DECN, has been developed for Direct MI communication as well as MI waveguide communication (using relay coils) using the interaction of various layer features to fulfill Quality of Service (QoS) parameters and attain an optimal saving in energy consumption and at the same time, gain in throughput. The simulation results have indicated that significant energy saving, high performance efficiency and reliable channel communication are achieved with the proposed cross-layered approach for WUSNs.

Redesigning compound TCP with cognitive edge intelligence for WiFi-based IoT

This article explores how to enhance Compound TCP design to learn and fix delays in future WiFi-based IoT networks continuously. We adopt a cross-layer approach and design multiple intelligent access points (APs) with cognitive collaboration approaches to improve efficiency across industry 4.0 WiFi networks. Comprehending the efficiency of Compound TCP is difficult due to its hybrid congestion mitigation strategies and the interrelationships with wireless channel failures, medium access level contentions/collisions, and overflows at the APs. We are therefore designing a detailed analysis to investigate the efficiency of Compound TCP streams over WiFi-based IoT. We take into account all kinds of losses at different TCP/IP layers. Our conceptual model contains WiFi network constraints, such as the maximum number of retries and the AP buffer capacity, which affect transport-layer latency and quality. More specifically, we are developing an analytic model for dual-APs and highlighting multiple AP settings’ benefits. Based on the developed theoretical model and findings, we show that cognitive radio approaches in Dual-AP systems can dramatically increase the performance of industry 4.0 WiFi connections and predict performance enhancements in multiple APs settings.

CBI4.0: A cross-layer approach for big data gathering for active monitoring and maintenance in the manufacturing industry 4.0

Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.

Practical Cross-Layer Radio Frequency-Based Authentication Scheme for Internet of Things.

The Internet of Things world is in need of practical solutions for its security. Existing security mechanisms for IoT are mostly not implemented due to complexity, budget, and energy-saving issues. This is especially true for IoT devices that are battery powered, and they should be cost effective to be deployed extensively in the field. In this work, we propose a new cross-layer approach combining existing authentication protocols and existing Physical Layer Radio Frequency Fingerprinting technologies to provide hybrid authentication mechanisms that are practically proved efficient in the field. Even though several Radio Frequency Fingerprinting methods have been proposed so far, as a support for multi-factor authentication or even on their own, practical solutions are still a challenge. The accuracy results achieved with even the best systems using expensive equipment are still not sufficient on real-life systems. Our approach proposes a hybrid protocol that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, which has a measurable benefit while keeping an acceptable security level. We implemented a full system operating in real time and achieved an accuracy of 99.8% for the additional cost of energy, leading to a decrease of only ~20% in battery life.

New adaptation method based on cross layer and TCP over protocols to improve QoS in mobile ad hoc network

Due to rapid growth of multimedia traffic used over the mobile ad-hoc networks (MANETs), to keep up with the progress of this constraints MANETs protocols becoming increasingly concerned with the quality of service. In view of the random mobility nodes in MANET, TCP becomes more unreliability in case of higher energy consumption and packet loss. In this paper we proposed a new optimization approach to enhance decision making of TCP based on some changes of IEEE 802.11 MAC uses cross layer approach. The aim is to minimize the impact of retransmissions of packet lost and energy consumption in order to analysed and chose the appropriate routing protocol for TCP that can be enhance QoS MANET. Our simulation results based QoS study using NS3 show that, our proposed achieves better performance of TCP in MANETs significantly, and also improved the throughput, energy consumption and facilitates the traffic transmission over routing protocol.

Joint Lossy Compression and Power Allocation in Low Latency Wireless Communications for IIoT: A Cross-Layer Approach

Low-latency communication is expected to play a key role in the Industrial Internet of Things (IIoT). Although there has been considerable effort on reducing latency, few attention has been focused on the low-latency transmission of distortion-tolerant data, e.g. IIoT's analog samples, over fading channels. In this paper, we present a cross-layer approach to jointly adapt the transmission power, rate, and compression ratio based on the instantaneous buffer and channel states. In particular, to minimize the average delay under both average power and distortion constraints, we formulate a Constrained Markov Decision Process (CMDP) with multi-dimensional state and action spaces. A novel solution is then presented by judiciously decomposing the multi-dimensional CMDP into a deterministic hierarchical optimization consisting of a linear programming and a convex optimization problem. Furthermore, we show the optimality of a threshold-based scheduling policy that highly reduces the complexity and present an optimal delay-power-distortion tradeoff that characterizes a fundamental performance tradeoff between the physical, network, and application layers. The joint lossy compression and power allocation scheme realizes a deep cross-layer optimization of source coding, queuing control, and wireless transmission, which outperforms the traditional cross-layer design consisting of only two layers, not to mention layered protocols.

Survey on Cross Layer Approach for Robust Communication in VANET

Vehicular Ad-hoc network (VANET) is a wide spread and organized model that occurred as a prevailing methodology to increase driving well-being and traffic management. In VANET path stability is more important for sending a data to destination. A number of researches have been proposed for Cross layer approach for robust communication in VANET. Cross-layer plan permits material to be swapped and shared between layer limits to implement effective and robust procedures. A detailed survey has been carried out to detect the numerous investigation articles obtainable in the literature for analyzing the cross layer approach for robust communication in VANET. Here, forty one papers are analyzed. The detail description of the survey is analyzed in this chapter.

Modified Energy Efficient with ACO Routing Protocol for MANET

Due to the specific design of the network upon the requirements of the demands as well as the situation at which the setting up of a physical network is impossible, a significant role is played by mobile ad-hoc network (MANET) in the applications of military. Various critical tasks like robust & dynamic military workstations, devices as well as smaller sub-networks within the battle field are handled by the presented network type controls the infrastructure less communication. A highly demand of the efficient routing protocol’s design is existing which ensures safety as well as reliability to transmit the extremely vulnerable as well as the secret military data within the defense networks. The designing of an energy efficient layer routing protocol within the network on behalf of the military applications is done in this method and the simulation is done with the help of a novel cross layer approach of design for increasing the reliability as well as the lifetime of the network. However, the optimum path selection is not allowed by PDO-AODV technique. Therefore, a novel ACO-DAEE (Ant colony optimization with delay aware energy efficient) to select an optimum path as well as to mitigate the delay time within the network system is proposed. Maintaining the optimum paths within the network while transmitting the data effectively is the major objective. With respect to packet delivery ratio, end to end delay, and throughput, it is indicated by the simulation outputs that the performance of ACO-ADEE is quite good. The efficiency of this technique is verified in the simulation outcomes using NS2 software.

Cross-Layer Approximate Hardware Synthesis for Runtime Configurable Accuracy

Approximate computing trades off computation accuracy against energy efficiency. The extent of approximation tolerance, however, significantly varies with a change in input characteristics and applications. We propose a novel cross-layer approach for the synthesis of runtime accuracy-configurable hardware that minimizes energy consumption at area expense. To that end, first, we explore instantiating multiple hardware blocks in the architecture with different fixed approximation levels. These blocks can be selected dynamically and thus allow to configure the accuracy during runtime. They benefit from having fewer transistors and also synthesis relaxations in contrast to state-of-the-art gating mechanisms that only switch off a group of paths of the circuit. Our cross-layer approach combines instantiating such blocks in the architecture with area-efficient gating mechanisms that reduce toggling activity, creating a fine-grained design-time knob on energy versus area. We present a systematic methodology to explore this joint design space and find energy-area optimal solutions as a function of required accuracies, their utilization in the workload, together with hardware parameters: dynamic power savings, area of the hardware block, and leakage of the technology. Examining total energy savings for a range of circuits under different workloads and accuracy tolerances shows that our method finds Pareto-optimal solutions providing up to 32% and 60% energy savings compared to state-of-the-art accuracy-configurable gating mechanism and an exact hardware block, respectively, at 2× area cost.

Improvement of Sybil Attack Detection in Vehicular Ad-Hoc Networks Using Cross-layer and Fuzzy Logic

Nowadays Vehicular Ad-Hoc Networks (VANETs) are very popular and significantly used, due to their unique abilities to improve road safety. As a consequence, the security of these networks is of great importance and it has become one of the central topics in scientific and research fields such as information exchange. Sybil attack is one of the challenges for Ad-Hoc networks security. In this paper, a cross-layer approach and fuzzy logic method are used to detect the Sybil attacks. The proposed fuzzy logic method has four inputs form different OSI layers: entry time to the network, a number of neighbors, buffer size and signal to noise ratio. These inputs are imported to several membership functions of the fuzzy logic methods and the simulation results indicate that the proposed solution provides a robust technique in Sybil attack detection.

Cross Layer Approach and ANFIS based Optimized Routing in Wireless Multi-Hop Ad Hoc Networks

Mobile ad hoc networks are non-dependent on any wired infrastructure and provide exceptional benefits during typical situations like conference, fair/exhibition, disaster recovery and emergency rescue operations. But these networks have to deal with several open issues, since they are battery operated and the dynamic topology can change due to mobility. Thus, multi-hop wireless ad hoc networking needs an energy efficient routing protocol that discovers/maintains routes to any other member node in the network, minimizing computation/communication overhead and battery power consumption. An adaptive neuro fuzzy inference system (ANFIS) based ad hoc routing approach has not been investigated before. In this paper, an ANFIS and cross layer approach-based energy efficient routing protocol for wireless multi-hop networks is proposed and its performance is compared with the Ad Hoc On-demand Distance Vector (AODV) Routing Protocol and fuzzy based AODV, using the network simulator ns2. Using remaining battery power, hop count and link stability as the input variables, the ANFIS module computes the optimum path cost, as the output. The proposed ANFIS based approach produces better results when compared using the metrics: average throughput, packet delivery ratio, average end-to-end delay in packet delivery between source and destination and average routing overhead.

A cross-layer approach towards developing efficient embedded Deep Learning systems

With the evolution of Smart Cyber–Physical Systems (CPS) and Internet-of-Things (IoT), the number of connected (intelligent) devices is increasing at an exponential rate, and so as the data being produced by them. To process this gigantic amount of data, algorithms are being proposed that can extract information from it without much human intervention. Deep Learning (DL) is one of the fields which has recently shown significant potential towards this direction, as it allows computational models (deep neural networks) composed of multiple processing layers to learn autonomously to interpret data with the help of the back-propagation algorithm. However, state-of-the-art deep neural networks (DNNs) are highly resource-hungry. To enable the use of DNNs in resource constraint mobile devices, several optimization techniques have been proposed at different abstraction layers of the computing stack. In this paper, we highlight the main categories of the optimization techniques and present a cross-layer methodology for developing performance/energy efficient DNN-based systems. At the software level, we employ a structured pruning technique along with the quantization of inputs and network parameters to reduce the computational complexity and memory requirements for the DNN inference process. At the hardware level, exploiting the error resilience characteristics of the DNNs, we make use of functional approximations in the arithmetic modules to further improve the efficiency of DNN-based systems.

Combining Power Allocation and Superposition Coding for an Underlay Two-way Decode-and-forward Scheme

Combining Power Allocation and Superposition Coding for an Underlay Two-way Decode-and-forward Scheme

Towards an energy balancing solution for wireless sensor network with mobile sink node

The issue of energy holes, or hotspots, in wireless sensor networks is well referenced. As is the proposed mobilisation of the sink node in order to combat this. However, as the mobile sink node may communicate with some nodes more than others, issues remain, such as energy spikes. In this study we propose a lightweight MAC layer solution — Dynamic Mobility and Energy Aware Algorithm (DMEAAL). Building on existing solutions utilising a communication threshold between static nodes and a sink node using a predictable mobility pattern, DMEAAL takes knowledge of optimum energy consumption levels and implements a cross-layer approach, utilising current energy consumption and dynamically adjusting communication threshold size based on target energy consumption. This approach is shown to balance energy consumption across individual nodes without increasing overall energy consumption compared to previous solutions. This without detrimentally affecting frame delivery to the sink. As such, network lifetime is improved. In addition we propose Mobile Edge Computing (MEC) applications for this solution, removing certain functionality from static nodes and instead deploying this within the mobile sink at the network edge.

A simple cross-layer mechanism for congestion control and performance enhancement in a localized multiple wireless body area networks

Commercialization of the wireless body area network (WBAN) envisions future new normal for WBAN devices coexistence in a localized area. The coexistence may allow devices to freely change positions, associate, or dissociate with the neighbours as users interact. Devices’ interaction in a stationary or mobile fashion radiates heat and also competes for the limited network resources resulting to unreliable communication and other performance challenges. Besides alarming user safety, device mobility affects network performance through topology changes, which result to recursive link disconnections, energy waste, packet delay, degraded throughput, and congestion due to excessive control messaging during route repair. In this article, we propose WBAN performance optimization criteria focusing on improving energy efficiency, network throughput, and reducing the end to end delay in multiple existence schemes. Firstly, we propose an alternative routing algorithm, whose routing decision depends on a cost function considering the parameterized residue energy to node distance ratio, link energy reliability, and specific heat absorption in addition to node sequence number and hop count as fundamental route selection metrics. Secondly, we implement congestion control adaptation in the medium access control (IEEE 802.11MAC) mechanism, which improves throughput, reduces congestion, and delay. Due to link discontinuities during mobility, we demonstrate a comparative network performance to address the effect of WBAN speed at different hello intervals. The comparative analysis show, protocol implementation with a cross-layer approach outperforms the conventional protocol without MAC adaptations in terms of energy efficiency, network throughput, and a reduced end to end delay, by an average of 0.45%, 2.8%, and 13.7%, respectively.