Resource-constrained Wireless Networks Research Articles

Federated Learning Over-the-Air by Retransmissions

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.

Energy Aware Task Allocation for Semi-Asynchronous Mobile Edge Learning

This paper extends the paradigm of "mobile edge learning (MEL)" by designing an energy-aware optimal task allocation scheme for training a machine learning (ML) model in a semi-asynchronous manner across multiple learners connected via the resource-constrained wireless edge network. The tasks are allocated such that the local dataset size selected at each learner ensures completion within a given global delay constraint and a local maximum energy consumption limit. Hence, the designed method is heterogeneity aware (HA) because it offers a trade-off between resource consumption and MEL performance by directly relating the time and energy consumption to the heterogeneous communication/computational capabilities of learners. Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed. The proposed HA semi-asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) synchronous/asynchronous (HU-Sync/Asyn) equal batch allocation schemes. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU-Sync/Asyn approaches and can even provide gains of up-to 25% compared to the HA-Sync scheme.

Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding.

Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security.

Collision-free output-feedback super-twisting control of robotic surface vehicles for coordinated path following with experiments

This paper addresses the coordinated path following (CPF) control of robotic surface vehicles (RSVs) in the presence of multiple obstacles and lumped disturbances over a resource-constrained wireless network. A collision-free output-feedback super-twisting control architecture is proposed for CPF without using velocity measurements. Specifically, robust exact differentiator observers are designed at first to recover the unmeasured velocities, and estimate the lumped disturbances, simultaneously. Next, anti-disturbance output-feedback super-twisting control laws are proposed to achieve individual path following in earth-fixed reference frame, and an adaptive potential field with a user-defined collision hazard degree is introduced to avoid the collisions with obstacles. To reduce the network traffic, self-triggered path updating laws are then designed for avoiding the continuous transmitting and listening of neighboring RSVs. The stability analysis shows that the closed-loop system is input-to-state stable outside the collision avoidance region, and Zeno behavior will not happen. Experiments are finally conducted to validate the efficacy of the proposed collision-free output-feedback super-twisting control method for the self-triggered CPF of RSVs.

DRL-Driven Dynamic Resource Allocation for Task-Oriented Semantic Communication

Semantic communication has been regarded as a promising technology to serve upcoming intelligent applications. However, few studies have addressed the problem of resource allocation in semantic communication networks. Most resource allocation mechanisms act fairly to all original data, ignoring the meaning behind the transmitted bits. In this paper, a dynamic resource allocation scheme for the task-oriented semantic communication network (TOSCN) based on deep reinforcement learning (DRL) is proposed, which allows data with richer semantic information to preferentially occupy limited communication resources. This paper aims to design a deep deterministic policy gradient (DDPG) agent at the micro base station to maximize the long-term transmission efficiency of tasks. Firstly, the relationship between semantic information and task performance is investigated. Subsequently, a novel wireless resource allocation model for TOSCN is proposed by taking the image classification task as an example. Then, a joint optimization problem of the semantic compression ratio, transmit power, and bandwidth of each user is formulated. The agent is trained in an interactive learning environment to obtain a decent trade-off between the amount of data delivered to the receiver and the accuracy of intelligent tasks. Simulation results demonstrate that the proposed scheme achieves significant advantages in relieving communication pressure and improving task performance in resource-constrained wireless networks.

Reinforcement learning based flow and energy management in resource-constrained wireless networks

This paper aims at developing a learning-based framework for MAC sleep–listen–transmit scheduling in wireless networks. The Reinforcement Learning-based paradigm is shown to work in the absence of network time-synchronization and other complex hardware features, such as carrier-sensing, thus making it suitable for low-cost transceivers for IoT and wireless sensor nodes. The framework allows wireless nodes to learn policies that can support throughput-sustainable flows while minimizing node energy expenditure and sleep-induced packet drops and delays. Each node independently learns a scheduling policy without explicit communication with other network nodes. The trade-off between packet drops and energy efficiency is analyzed, and an application-specific solution is proposed for handling the trade-off. It is shown how this model allows users to prioritize between energy efficiency and packet drops depending on specific application requirements. An analytical model is developed for understanding the underlying system dynamics, which is also validated using extensive simulation experiments. Moreover, the developed mechanism is experimented with for heterogeneous network topologies and traffic patterns

Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.

Centralized and Distributed Intrusion Detection for Resource-Constrained Wireless SDN Networks

Software-defined networking (SDN) was devised to simplify network management and automate infrastructure sharing in wired networks. These benefits motivated the application of SDN in resource-constrained wireless networks to leverage solutions for complex applications. However, some of the core SDN traits expose the networks to Denial-of-Service (DoS) attacks. There are proposals in the literature to detect DoS in wireless SDN networks; however, not without shortcomings: there is little focus on resource constraints, high detection rates have been reported mostly for small networks and the detection is disengaged from the identification of the type of attack or the attacker. Our work targets these shortcomings by introducing a lightweight, online change point detector to monitor performance metrics that are impacted when the network is under attack. A key novelty is that the proposed detector is able to operate in either centralized or distributed mode. The centralized detector has very high detection rates and can further distinguish the type of attack from a list of known attacks. In turn, the distributed detector can be useful to identify the nodes launching the attack. Our proposal is tested over IEEE 802.15.4 networks. The results show detection rates exceeding 96% in networks of 36 and 100 nodes and identification of the type of attack with a probability exceeding 89% when using the centralized approach.

A Transfer Learning Approach for Securing Resource-Constrained IoT Devices

In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order to explore the complex characteristics of IoT, machine learning methods, which rely on long training time to generate intrusion detection models, are proposed in the literature. Furthermore, these systems need to learn a new/fresh model from scratch when the environment changes. This study explores the use of transfer learning in order to generate intrusion detection algorithms for such dynamically changing IoT. Transfer learning is an approach that stores knowledge learned from a problem domain/task and applies that knowledge to another problem domain/task. Here, it is employed in the following two settings: transferring knowledge for generating suitable intrusion algorithms for new devices, transferring knowledge for detecting new types of attacks. In this study, Routing Protocol for Low-Power and Lossy Network (RPL), a routing protocol for resource-constrained wireless networks, is used as an exemplar protocol and specific attacks against RPL are targeted. The experimental results show that the transfer learning approach gives better performance than the traditional approach. Moreover, the proposed approach significantly reduces learning time, which is an important factor for putting devices/networks in operation in a timely manner. Even though transfer learning has been considered a potential candidate for improving IoT security, to the best of our knowledge, this is the first application of transfer learning under these two settings in RPL-based IoT networks.

Adaptive Complex Filtering for Narrowband Jamming Mitigation in Resource-Constrained Wireless Networks

Jamming attacks present a major challenge for the physical layer security of wireless communication systems. Among the large number of existing methods for jamming mitigation, filtering is a simple but promisingly effective solution that can be applied in resource-constrained and low-power wireless networks. In this article, a first-order adaptive complex narrowband notch filter is proposed for use against high-power narrowband jamming attacks. The central frequency of the filter is adaptively adjustable to match the interfering frequency. This enables the proposed solution to perform effectively in the presence of a jammer whose frequency varies in fixed or random time periods. The simulation results demonstrate the filter's behavior and the efficiency of the proposed technique.

Wireless Technologies and Process Automation

Prerequisites for the field communications networks in the process industries include effective support for hybrid traffic, durability, availability, reliability, security, and scalability in a harsh industrial environment. Moreover, the establishment of such a network on a license-free scale raises worries regarding its security, safety, functioning and service quality (QoS). Quality of Service relates to the management of network resources and the provision of services required by the application by controlling delay, jitter, packet loss rate and bandwidth. To develop an industrial-strength wireless network that can handle stringent process automation requirements, impose restrictions on network design that includes hardware and software components used. To achieve the quality of service required, a broad view of systems must be seen as the overall performance of network-based applications based on the operation, interaction and collaboration of individual components. To achieve the perceived network, various problems are dealt with. As part of this research, three specific issues were addressed: time synchronization problem in distributed systems, closed-loop control on a resource-constrained wireless network, and transmission power monitoring (TPC) in wireless field nodes. They address the problem of limited resources available in distributed field nodes with a view to maximizing the use of available resources. This will be explained in detail later. Recent developments in wireless communications technologies creating new opportunities for wireless communication to field equipment in industries such as gas and oil, bulk water distribution and chemical processing. Wireless communications can help the above industries improve factory knowledge by getting complementary, measurements of operations and devices when wired communication is not allowed. The operational field communications network requirements include active support for hybrid traffic, durability, reliability, safety, and scalability in the industrial environment of harsh. Moreover, the establishment of such a network on a scale that does not carry any license raises worries regarding its security, safety, functioning and governance.

Futures-Based Resource Trading and Fair Pricing in Real-Time IoT Networks

Technology advancements on sensing, communications, and computing directly accelerate the recent development of Internet of Things (IoT), leading to rich and diverse applications in industry and business processes. For enabling IoT applications, smart devices and networks rely on the capabilities for information collection, processing and tight collaboration with their neighbors. However, the rapid proliferation of IoT devices and real-time IoT applications result in dramatically increased demand for communication and computing resources, posing a significant challenge for the underlying resource constrained wireless networks. Due to their resource constraint, it is extremely difficult for such devices and networks to meet stringent requirements of many real-time IoT applications. To overcome these challenges, resource sharing among IoT devices and networks has been studied as an effective solution by exploiting unused resources.

SPS and DPS: Two New Grid-Based Source Location Privacy Protection Schemes in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) have been widely deployed to monitor valuable objects. In these applications, the sensor node senses the existence of objects and transmitting data packets to the sink node (SN) in a multi hop fashion. The SN is a powerful node with high performance and is used to collect all the information sensed by the sensor nodes. Due to the open nature of the wireless medium, it is easy for an adversary to trace back along the routing path of the packets and get the location of the source node. Once adversaries have got the source node location, they can capture the monitored targets. Thus, it is important to protect the source node location privacy in WSNs. Many methods have been proposed to deal with this source location privacy protection problem, and most of them provide routing path diversity by using phantom node (PN) which is a fake source node used to entice the adversaries away from the actual source node. But in the existing schemes, the PN is determined by the source node via flooding, which not only consumes a lot of communication overhead, but also shortens the safety period of the source node. In view of the above problems, we propose two new grid-based source location privacy protection schemes in WSNs called grid-based single phantom node source location privacy protection scheme (SPS) and grid-based dual phantom node source location privacy protection scheme (DPS) in this paper. Different from the idea of determining the phantom node by the source node in the existing schemes, we propose to use powerful sink node to help the source node to determine the phantom node candidate set (PNCS), from which the source node randomly selects a phantom node acting as a fake source node. We evaluate our schemes through theoretical analysis and experiments. Experimental results show that compared with other schemes, our proposed schemes are more efficient and achieves higher security, as well as keeping lower total energy consumption. Our proposed schemes can protect the location privacy of the source node even in resource-constrained wireless network environments.

Bio-inspired energy conserving adaptive power and rate control in MANET

Maximizing network lifetime of unreliable and resource constrained wireless networks such as mobile ad hoc networks involves optimal allocation of power and shared channels in the network. Existing optimal power and channel allocation mechanisms mainly focus on throughput maximization and energy efficiency but fail to obtain a trade-off between delay and energy consumption in the network. This paper proposes a game theoretic energy efficient approach that mimics a fair non-cooperative game among nodes competing for shared channels and power level. Unlike existing power and rate control mechanisms, the proposed energy conserving adaptive power and rate control (ECAPRC) mechanism takes into consideration the constraints of outage probability and total average delay to deal with stochastic changes in the channel that improves the network performance in terms of throughput, delay and total energy consumption. Proposed super-modular game has a unique convergence point called “Nash Equilibrium” and its existence and uniqueness is proved in the paper. To enhance the convergence rate of proposed ECAPRC approach, an adaptive grey wolf optimizer is employed to deal with delay and energy constraints. Simulation results and their analysis show that proposed ECAPRC approach outperforms existing approaches such as dynamic rate and power allocation algorithm, rate-effective network utility maximization and energy conserving power and rate control in terms of above mentioned network parameters and total utility of the system.

Evolution of Cryptographic Algorithm for Resource Constrained Wireless Networks: An Investigation Approach

Evolution of Cryptographic Algorithm for Resource Constrained Wireless Networks: An Investigation Approach

Full-duplex distributed switch-and-stay energy harvesting selection relaying networks with imperfect CSI: Design and outage analysis

We analyze the effect of distributed switch and stay combining (DSSC) technique and imperfect channel state information (CSI) in the scenario, where the communication between the source and the destination is conducted through either the direct transmission or with the help of the best relay among K full-duplex energy harvesting relays by using partial relay selection. To activate such communication, at the destination, we apply DSSC technique, which is considered as an effective method to achieve spatial diversity in a distributed manner with low processing complexity and compatibility to resource-constrained wireless networks. Considering two popular relaying protocols, i.e., decode-and-forward (DF) and amplify-and-forward (AF), the performance analysis in terms of outage probability (OP) is carried out. Numerical results show that imperfect CSI has a negative impact on system performance. Besides that, our DSSC-full duplex selection relaying (FDSSCSR) system achieves a better performance in terms of outage probability than that of the DSSC-half duplex selection relaying (HDSSCSR) and the full duplex/half duplex dual hop relaying with partial relay selection (FD/HDDRPRS) systems. Simulation results are conducted to verify the correctness of our analysis.

Robust decentralized data storage and retrieval for wireless networks

In this paper we study the problem of distributed data storage using Rateless codes for large-scale resource-constrained wireless networks. We focus on building a robust storage system using Fountain codes from physically decentralized sources. Fountain codes, e.g. LT-codes, can achieve reduced complexity of both encoding and decoding, which caters well to the nature of such networks. We propose an energy-efficient distributed dissemination and coding scheme to build a decentralized LT-codes based storage over a network of resource-limited nodes to provide data survivability against possible failures. In the proposed scheme, each sensor node is assigned a selection probability derived from a Robust Soliton Distribution (RSD) in a distributed fashion. Source nodes then disseminate their data over the storage network randomly, in accordance with the selection probabilities. The proposed scheme is compared to similar schemes in the literature by means of simulations. We evaluate the energy required for building the storage as well as the energy needed for data retrieval from the storage system. Results show that energy consumption can be substantially reduced while achieving the required storage requirements.

Efficient IoT Gateway over 5G Wireless: A New Design with Prototype and Implementation Results

The growth of IoT is expected to herald a better quality of life and open up new industrial opportunities. However, it is also raising new challenges in resource constrained wireless networks. Originally designed for human-to-human wireless communications, present wireless networks fail to satisfy the huge connectivity and varying traffic requirements of IoT. Next generation 5G wireless networks are expected to exploit emerging features, like mmWave, massive MIMO, and C-RAN, for providing the enormous connectivity, resource pooling, and energy efficiency required to support commercial rollout of IoT. Exploring these emerging features, we develop 5G-enabled IoT gateways to communicate with the RRHs of 5G C-RAN. The gateways are endowed with efficient compression schemes to significantly improve uplink resource utilization. Our 5G C-RAN prototyping and laboratory experimental results point out a huge opportunity for supporting a massive number of IoT-enabled devices by sophisticated 5G C-RAN deployment and efficient IoT gateway development.

Enhanced low latency queuing algorithm with active queue management for multimedia applications in wireless networks

Low cost broadband services used widely in the recent years increased the demand for multimedia applications. Many of these applications require different quality of service (QoS) in terms of throughput and delay. In the current resource constrained wireless networks, resource management plays a vital role. In order to handle the resources effectively and to increase the QoS, proper packet scheduling algorithms need to be developed. Low latency queuing (LLQ) is a packet scheduling algorithm which combines strict priority queuing (SPQ) to class-based weighted fair queuing (CBWFQ). In this paper, an enhanced low latency queuing (ELLQ) algorithm is proposed in which an additional SPQ is introduced along with the existing SPQ. Further, the QoS is improved by integrating congestion avoidance algorithm with ELLQ. Simulation results using the OPNET modeller show that the proposed algorithm outperforms the existing algorithm in terms of throughput and delay for the multimedia applications.

Enhanced low latency queuing algorithm with active queue management for multimedia applications in wireless networks

Low cost broadband services used widely in the recent years increased the demand for multimedia applications. Many of these applications require different quality of service (QoS) in terms of throughput and delay. In the current resource constrained wireless networks, resource management plays a vital role. In order to handle the resources effectively and to increase the QoS, proper packet scheduling algorithms need to be developed. Low latency queuing (LLQ) is a packet scheduling algorithm which combines strict priority queuing (SPQ) to class-based weighted fair queuing (CBWFQ). In this paper, an enhanced low latency queuing (ELLQ) algorithm is proposed in which an additional SPQ is introduced along with the existing SPQ. Further, the QoS is improved by integrating congestion avoidance algorithm with ELLQ. Simulation results using the OPNET modeller show that the proposed algorithm outperforms the existing algorithm in terms of throughput and delay for the multimedia applications.