Exchange Overhead Research Articles

Distributed Joint Power and Bandwidth Allocation for Multiagent Cooperative Localization

Traditional cooperative localization schemes often rely on centralized information sharing to implement the joint power and bandwidth allocation for localization error minimization. In order to minimize the sharing overhead and reduce the implementation complexity, we propose a low complexity distributed power and bandwidth allocation scheme for high accurate collaborative localization. By decomposing the expected global squared position error bound (SPEB) into individual ones, our proposed scheme is able to determine the power and bandwidth distributively with limited information exchange overhead. Through analytical and numerical examples, we show that the proposed collaborative scheme can achieve about 11% mean square localization error improvement and offer over 71% complexity reduction for six agents, if compared with the traditional iterative searching schemes.

Federated learning empowered mobility-aware proactive content offloading framework for fog radio access networks

Proactive content caching has emerged as a promising solution to cope with exponentially increasing mobile data traffic. The popular user contents can be cached near the network edge for faster retrieval and processing. Current state-of-the-art approaches adopt a centralized model training mechanism that requires high communication and data exchange overheads to predict content popularity. Moreover, these approaches fail to deal with the dynamicity of the environment since they do not take into account the users’ mobility information and are unable to incorporate content offload timings. In this paper, we address these limitations by proposing a novel federated learning-based Mobility and Demand-aware Proactive Content Offloading (MDPCO) framework. MDPCO exploits distributed learning strategies and capitalizes on users’ mobility and demand information for proactive content offloading. Extensive simulations are carried out to validate the efficacy of MDCPO against local and cloud-based models. Our proposed model yields an average performance improvement of 6.7% in comparison to the cloud-based model. Furthermore, with the increase in the number of fog servers, the MDPCO achieves a 9.8% higher data offloading ratio and 1.18% increase in the downlink rates while being more energy-efficient than cloud-based approaches.

Secure lightweight multi‐party key agreement based on hyperelliptic curve Diffie–Hellman for constraint networks

AbstractWith recent developments in hyperelliptic curve cryptographic system (HECC) processing and the proposed work, the myth that HECC is not suited for practical applications in constrained networks is dispelled. This article proposes dynamic authenticated contributory group key agreement (DACGKA) protocol using HEC‐DH. The DACGKA is less costly and ideally appropriate for resource‐restricted networks such as VANETs, MANETs, and WSNs because of its lesser key sizes and advancements in operational processing. The suggested protocol is implemented across a finite field using an HEC of genus 1 (ECC), genus 2, and genus 3, with promising results, including a significant reduction in key size and exchange overhead, an increase in key safety, and a broader range of applicability. According to a comparison of the proposed approach on different genus curves, HECC over genus 2 with a suitable key length can be applied for memory and power restricted devices to increase data secrecy and system efficiency. Further, HECC can outperform ECC for low‐cost, secure group communication applications. As a part of security analysis, first, we proved that each coordinate of HEC‐DH secret value is as hard as the entire DH value, and then concrete security proofs based on the HEC‐DLP were established.

Energy-Efficient Secure Short-Packet Transmission in NOMA-Assisted mMTC Networks With Relaying

Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead.

Contextual User-Centric Task Offloading for Mobile Edge Computing in Ultra-Dense Network

Integrating mobile edge computing (MEC) in the ultra-dense network (UDN) is a key enabler to meet the service demand by allowing smart devices to perform uninterrupted task offloading via densely deployed MEC servers. In most cases, the smart devices randomly move around the whole network. Consequently, the popular ‘`MEC-centralized decision’' offloading approach could be inapplicable, as joint decision-making among multiple MEC servers becomes difficult due to time synchronization and information exchange overhead. In this paper, we take a user-centric approach to minimize a long-term delay for a given task duration under a price budget constraint. To address this problem, we develop a novel contextual sleeping bandit learning (CSBL) algorithm, which integrates contextual information and sleeping characteristic to accelerate the learning convergence and leverage Lyapunov optimization to deal with the price budget constraint. Furthermore, we extend to a multiple offloading scenario where multiple MEC servers can be selected in each offloading round and propose a CSBL-multiple (CSBL-M) algorithm to address the exponential increase of the offloading selections. For both CSBL and CSBL-M, we derive the upper bounds of learning regret and provide rigorous proofs that they asymptotically approach the Oracle algorithm within bounded deviations for finite task duration.

Schedule Sequence Design for Broadcast in Multi-Channel Ad Hoc Networks

Ultra-reliable and low-latency communication (URLLC) is of capital importance for the 5G wireless networks and has received a lot of attention recently. Motivated by the hard delay requirement for URLLC, we aim at devising medium access control (MAC) schemes that can guarantee packet transmissions within a bounded delay. The model we considered is a multi-channel single-hop ad hoc network in which each node has packets to broadcast to other nodes. This broadcast scenario is common, and has been extensively studied in various scenarios such as safety message broadcast in vehicular ad hoc networks (VANETs). Most of the existing MAC schemes employ backoff algorithms to handle transmission failures caused by channel collisions, and thus cannot provide a hard guarantee on transmission delay. Moreover, these schemes rely on time synchronization and coordination among the nodes, which would cause heavy overhead for control message exchange. In this paper, we focus on devising MAC schemes to provide a hard guarantee on broadcast delay, without time synchronization and cooperation among the nodes. We employ the sequence scheme in which each node is pre-assigned a periodic sequence to schedule transmissions and receptions at each time slot. These sequences are referred to as schedule sequences. Since each node starts its transmission schedule independently, there exist relative time offsets among the schedule sequences they use. Previous sequence schemes for broadcast are only conducted in the single channel context. Our objective is to design schedule sequences such that in a multi-channel system, each node can transmit at least one packet to each of its neighbors successfully within a common period, no matter what the time offsets are. The sequence period should be designed as short as possible. In this paper, we analyze the lower bound on sequence period, and propose a sequence construction method by which the period can achieve the same order as the lower bound. We also consider the random scheme in which each node transmits or receives on a channel at each time slot with a pre-determined probability. The frame length and broadcast completion time under different schemes are compared by numerical studies.

Multi-Agent Reinforcement Learning Based Distributed Transmission in Collaborative Cloud-Edge Systems

Millimeter-wave (mmWave) cloud-edge collaboration has emerged as an effective solution for low-latency transmission by harnessing the potentials of cloud and edge nodes (eNodes) as well as abundant bandwidth. In a mmWave dense network, centralized processing and coordinated multi-points processing require large amounts of signaling exchange overhead, which in turn increase the latency. As such, distributed mechanism is demanded. In this paper, we investigate joint user scheduling and beam selection strategies for eNodes and formulate the problem as a constrained Markov decision process. The objective and constraints are long-term average network delay cost minimization and instantaneous quality of service (QoS) guarantee for each user equipment. To update the distributed strategies, we apply the multi-agent reinforcement learning technique and propose a hybrid learning framework by extending the actor-critic model, where centralized training and decentralized execution are implemented. We utilize the Lagrange technique to design the reward functions. Additionally, a novel actor network architecture and an exploration scheme are proposed. Simulation results validate the effectiveness of the proposed intelligent distributed algorithm with a high degree of scalability, and show superior performance in terms of long-term average network delay cost and QoS satisfaction rates compared with other methods.

Efficient Nearest-Neighbor Data Sharing in GPUs

Stencil codes (a.k.a. nearest-neighbor computations) are widely used in image processing, machine learning, and scientific applications. Stencil codes incur nearest-neighbor data exchange because the value of each point in the structured grid is calculated as a function of its value and the values of a subset of its nearest-neighbor points. When running on Graphics Processing Unit (GPUs), stencil codes exhibit a high degree of data sharing between nearest-neighbor threads. Sharing is typically implemented through shared memories, shuffle instructions, and on-chip caches and often incurs performance overheads due to the redundancy in memory accesses. In this article, we propose Neighbor Data (NeDa), a direct nearest-neighbor data sharing mechanism that uses two registers embedded in each streaming processor (SP) that can be accessed by nearest-neighbor SP cores. The registers are compiler-allocated and serve as a data exchange mechanism to eliminate nearest-neighbor shared accesses. NeDa is embedded carefully with local wires between SP cores so as to minimize the impact on density. We place and route NeDa in an open-source GPU and show a small area overhead of 1.3%. The cycle-accurate simulation indicates an average performance improvement of 21.8% and power reduction of up to 18.3% for stencil codes in General-Purpose Graphics Processing Unit (GPGPU) standard benchmark suites. We show that NeDa’s performance is within 13.2% of an ideal GPU with no overhead for nearest-neighbor data exchange.

FLAT: Federated lightweight authentication for the Internet of Things

Federated Identity Management schemes (FIdMs) are of great help for traditional systems as they improve user authentication and privacy. In this paper, we claim that traditional FIdMs are mostly cumbersome and then ill-suited for IoT. As a solution to this problem, we came up with Federated Lightweight Authentication of Things (FLAT), namely a federated identity authentication protocol exclusively tailored to IoT. FLAT replaces weighty protocols and public-key cryptographic primitives used in traditional FIdMs by lighter ones, like symmetric cryptographic primitives and Implicit Certificates. Our results show that FLAT can reduce the data exchange overhead by around 31% when compared to a baseline solution. Also, the FLAT Client, the role played by an IoT device in the protocol, is more efficient than the baseline Client in terms of data exchange, storage, memory, and computation time. Our results indicate that FLAT runs efficiently, even on top of resource-constrained devices like Arduino.

Provable secure lightweight multiple shared key agreement based on hyper elliptic curve Diffie–Hellman for wireless sensor networks

ABSTRACTIn this paper, we propose a new protocol in which session keys can be generated when public keys are exchanged in a one run of the aforesaid protocol based on the difficulty of the hyper elliptic curve discrete logarithm problem. In this work, we are implementing the proposed protocol using an hyper elliptic curve of genus 2 over a finite field and the results are very encouraging. The main advantages of the proposed protocol are a considerable decrease in keys exchange overhead, a further increase in the safety of the keys and broadening of applicability. A relative study among the proposed and existing protocols is made and acceptable results have been obtained. The proposed protocol is immune to known-key attack, replay attack, key compromise impersonation, forgery attack and key control. Further, an upper limit for the quantity of shared keys is obtained in terms of the number of keys exchanged and, in fact, we find a formula for the minimal required number of keys to be exchanged for a given number shared keys. Further we can easily provide session key authentication for various wireless users/sessions on proposed protocol by integrating lightweight three-factor authentication scheme in the back ground.

Blockchain-based Smart Waste Management System

Blockchain generation and the Internet of Things are two of the most famous technology these days. IoT is an interconnection of devices which have the usefulness to detect, degree, a strategy the country of natural markers just as themselves and incite dependent on the enter outfitted. It can help make astute arrangements which could enhance the best of ways of life of individuals. In like manner, blockchain is dispensed database structures that guarantee an extreme dimension of wellbeing and accessibility of actualities with least exchange overhead. In this proposal, we endeavor to convey together that two innovation to grow a Smart Waste Management System (SWMS). The SWMS is weight-based for example Clients need to pay for the utilization of administrations as indicated by the measure of waste they produce. Installments are made the use of a custom digital currency controlled by utilizing Smart Contracts and the total SWMS can be supported with the guide of a DAO through a totally computerized, observably secure strategy. Blockchain can help bring down the infiltration and supplier esteem which might be particularly valuable to developing countries in which governments are not exceptionally inventive. This paper tries to set up an evidence of concept thru size of performance and evaluation of the applicability of this type of device.

Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing both cost-effective and on-demand wireless communications. This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. More particularly, each UAV communicates with a ground user by automatically selecting its communicating users, power levels and subchannels without any information exchange among UAVs. To model the uncertainty of environments, we formulate the long-term resource allocation problem as a stochastic game for maximizing the expected rewards, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Afterwards, we develop a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that: 1) appropriate parameters for exploitation and exploration are capable of enhancing the performance of the proposed MARL based resource allocation algorithm; 2) the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. By doing so, it strikes a good tradeoff between performance gains and information exchange overheads.

Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

This paper proposes a distributed multienergy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multicarrier energy supplies based on the concept of energy hub but also exchanges energy with interconnected microgrids and via the transactive market. The multimicrogrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multimicrogrid multienergy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas, and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multimicrogrid system. The proposed method has been fully tested and benchmarked on the different scaled multimicrogrid system over a 24-h scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multimicrogrid problems.

Timely Status Update in Wireless Uplinks: Analytical Solutions With Asymptotic Optimality

In a typical Internet of Things (IoT) application where a central controller collects status updates from multiple terminals, e.g., sensors and monitors, through a wireless multiaccess uplink, an important problem is how to attain timely status updates autonomously. In this paper, the timeliness of the status is measured by the recently proposed age-of-information (AoI) metric; both the theoretical and practical aspects of the problem are investigated: we aim to obtain a scheduling policy with minimum AoI and, meanwhile, requires little signaling exchange overhead. Toward this end, we first consider the set of arrival-independent and renewal policies; the optimal policy thereof to minimize the time-average AoI is proved to be a round-robin policy with one-packet (latest packet only and others are dropped) buffers (RR-ONE). The optimality is established based on a generalized Poisson-arrival-see-time-average theorem. It is further proved that RR-ONE is asymptotically optimal among all policies in the massive IoT regime. The AoI steady-state stationary distribution under RR-ONE is also derived. An implementation scheme of RR-ONE is proposed which can accommodate dynamic terminal appearances with little overhead. In addition, considering scenarios where packets cannot be dropped, a Lyapunov optimization-based max-AoI-weight policy is proposed which achieves better performance compared with state-of-the-art.

Distributed Zero-Forcing Amplify-and-Forward Beamforming for WSN Operation in Interfered and Highly Scattered Environments

In this paper, amplify-and-forward beamforming (AFB) is considered to establish a communication, through wireless sensor networks (WSNs) of $K$ sensor nodes, from a source to a receiver in the presence of both scattering and interference. All sources send their data to the WSN during the first time slot, while the nodes forward a properly weighted version of their received signals during the second slot. These weights are properly selected to maximize the desired power while completely canceling the interference signals. We show, however, that they depend on information locally unavailable at each node, making the zero-forcing beamformer (ZFB) unsuitable for WSNs, due to the prohibitive data exchange overhead and the power depletion it would require. To address this issue, we exploit the asymptotic expression at large $K$ of the ZFB weights that is locally computable at every node and, further, well-approximates their original counterparts. The performance of the resulting new distributed ZFB (DZFB) version is analyzed and compared with the conventional ZFB and two other distributed AFB benchmarks: the monochromatic (i.e., single-ray) AFB whose design neglects the presence of scattering and the bichromatic AFB which relies on an efficient two-ray channel approximation valid only for low angular spread (AS). We show that the proposed DZFB outperforms its monochromatic and bichromate counterparts while incurring much less overhead and power depletion than ZFB. We show also that it is able to provide optimal performance even in highly scattered environments as in the latter.

Enhancing scalability of peer-to-peer energy markets using adaptive segmentation method

This paper proposes an adaptive segmentation method as a market clearing mechanism for peer-to-peer (P2P) energy trading scheme with large number of market players. In the proposed method, market players participate in the market by announcing their bids. In the first step, players are assigned to different segments based on their features, where the balanced k-means clustering method is implemented to form segments. These segments are formed based on the similarity between players, where the amount of energy for trade and its corresponding price are considered as features of players. In the next step, a distributed method is employed to clear the market in each segment without any need to private information of players. The novelty of this paper relies on developing an adaptive algorithm for dividing large number of market players into multiple segments to enhance scalability of the P2P trading by reducing data exchange and communication overheads. The proposed approach can be used along with any distributed method for market clearing. In this paper, two different structures including community-based market and decentralized bilateral trading market are used to demonstrate the efficacy of the proposed method. Simulation results show the beneficial properties of the proposed segmentation method.

Distributed beamforming algorithm based on game theory in device-to-device communications

In the device-to-device communication network, there is the interference problem when device-to-device users share the same spectrum with cellular users, a distributed beamforming scheme based on non-cooperative game is proposed to maximize weighted sum rate under the rated transmit power and the users’ quality of service. Since the optimization problem is non-concave, we first obtain the solution of the Karush-Kuhn-Tucher (KKT) condition for the downlink beamforming problem of each sender by dual decomposition. Second, a distributed beamforming algorithm based on the non-cooperative game is proposed, which can quickly converge to the Nash equilibrium point with a lower information exchange overhead. Finally, the simulation results show that the proposed algorithm has better performance in terms of system sum rate and fairness than the existing algorithms.

Incentivizing Energy Trading for Interconnected Microgrids

In this paper, we study the interactions among interconnected autonomous microgrids, and propose a joint energy trading and scheduling strategy. Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network. Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits. Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading. We design an incentive mechanism using Nash bargaining theory to encourage proactive energy trading and fair benefit sharing. We solve the bargaining problem by decomposing it into two sequential problems on social cost minimization and trading benefit sharing, respectively. For practical implementation, we propose a decentralized solution method with minimum information exchange overhead. Numerical studies based on realistic data demonstrate that the total cost of the interconnected-microgrids operation can be reduced by up to 13.2% through energy trading, and an individual participating microgrid can achieve up to 29.4% reduction in its cost through energy trading.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Low complexity optimization for user centric cellular networks via large dimensional analysis

Users near cell edges suffer from severe interference in traditional cellular networks. In this paper, we consider the scenario that multiple nearby base stations (BSs) cooperatively serve a group of users which is referred to as the cell free networks. A low complexity optimization method based on the large dimensional analysis is proposed. The advantage of the cell free networks is that the interference caused in the cell edge users can be converted into intended signal. It is not easy to obtain the optimal solution to the network due to coupled relations among the users’ rates. To obtain a suboptimal solution, a precoder that balances signal and interference is adopted to maximize the network capacity. In traditional optimization, it requires instantaneous channel state information. We try to optimize the network sum rate based on the large dimensional analysis. In this way, the optimization can be transformed into another problem that merely depend on the large scale channel statistics. Large dimensional analysis is leveraged to derive the asymptotic signal to interference plus noise ratio that only depends on large scale channel statistics. Based on this result, the power allocation problem does not need to adapt as frequently as the instantaneous channel state information. By this means, signal exchange overhead can be greatly reduced. Numerical results are provided to validate the efficacy of the proposed optimization method.