Persona-based, empathetic approaches can foster sustainable long-term user-agent engagement in aging-in-place contexts. We present PersonaBot, a persona-driven persuasive agent built on a Dual-Persona framework that constructs user personas and generates culturally diverse, gender- and personality-varied agent personas, pairing users with preferred agent personas and adapting them over time. In an eight-week field deployment (8 participants; 1005 participant messages; 2432 agent messages), PersonaBot significantly increased perceived empathy, slowed engagement decline relative to a non-persona baseline, and elicited more elaborative interactions. Effectiveness varied with users’ technological self-efficacy, autonomy preferences, cultural identity, and social patterns, underscoring heterogeneous persona needs. Contrary to our initial assumptions, participants sometimes chose cross-cultural agents for perceived professionalism (over demographic similarity) and favored teacher-like personas balancing authority and warmth. Many framed the agent as a co-pilot rather than a caregiver replacement and engaged selectively, indicating agent personas should respect autonomy and invite—rather than demand—interaction.

Traditional centralized model training assumes that data samples are readily available and can be processed without constraints. In contrast, decentralized machine learning (DML) addresses the limitation by collaborative model training and inference directly on distributed data sources. The transformation from data centralization to decentralization helps comply with data regulations and improves system scalability with reduced reliance on cloud servers. However, a tradeoff between model personalization and generalization exists: the fine-tuning of local training data distribution sacrifices model generalization on the testing data distribution that differs from the training data distribution. To improve the tradeoff, we propose a DML framework that can inherently make model personalization and generalization easier by selecting a model among multiple ones judiciously. We develop a scalable selector for model selection and use blockchain to achieve model consensus. The personalized model selector is then proposed for test-time adaptation. Using computer simulations, we show that our method not only outperforms competitive personalization benchmarks but also generalizes well for new data distributions with various shifts.

In recent years, as a privacy-preserving distributed training method, Federated Learning (FL) has gained popularity from both academia and industry, much attention has been focused on various aspects of FL, e.g., model efficiency, client selection and resource allocation, aiming to implement FL over the practical distributed edge networks. One of the main challenges in FL is the high communication cost due to large distances between the FL server and workers. As a result, many FL workers drop out in the FL training process, resulting in poor performance of the FL model. As such, Hierarchical Federated Learning (HFL) has been proposed. HFL includes an additional layer of edge servers which relay the communications between the FL server and the FL workers. However, the improvement in communication efficiency may be negated by the increase in the number of communication rounds needed for model convergence given non-independent and non-identically distributed (IID) data of the FL workers. In addition, the FL workers may not be motivated to contribute to the FL model. To this end, we propose a synthetic-data-empowered HFL framework to overcome the statistical challenge of non-IID local datasets while motivating the contribution of FL workers in the HFL network. In our proposed framework, the edge servers generate and distribute synthetic datasets to the FL workers in their clusters. The FL workers decide on which edge server to join, taking into consideration the resources that they need to train on both their local datasets and the synthetic datasets.

Social insurance benefits qualification assessment is an important task to ensure that retirees enjoy their benefits according to the regulations. It also plays a key role in curbing social security frauds. In this paper, we report the deployment of the Intelligent Benefit Certification and Analysis (IBCA) platform, an AI-empowered platform for verifying the status of retirees to ensure proper dispursement of funds in Shandong province, China. Based on an improved Gated Recurrent Unit (GRU) neural network, IBCA aggregates missing value interpolation, temporal information, and global and local feature extraction to perform accurate retiree survival rate prediction. Based on the predicted results, a reliability assessment mechanism based on Variational Auto-Encoder (VAE) and Monte-Carlo Dropout (MC Dropout) is executed to perform reliability assessment. Deployed since November 2019, the IBCA platform has been adopted by 12 cities across the Shandong province, handling over 50 terabytes of data. It has empowered human resources and social services, civil affairs, and health care institutions to collaboratively provide high-quality public services. Under the IBCA platform, the efficiency of resources utilization as well as the accuracy of benefit qualification assessment have been significantly improved. It has helped Dareway Software Co. Ltd earn over RMB 50 million of revenue.

In the Internet of Things (IoT) employing centralized machine learning, security is a major concern due to the heterogeneity of end devices. Malicious devices could launch poisoning attacks to degrade machine learning models. Distributed machine learning (DML) with blockchain provides a potential solution. Once local weights are recorded on the blockchain, model aggregation with defensive schemes can be executed on smartphones to prevent attacks. However, blockchain with the proof-of-work (PoW) consensus mechanism wastes computing resources and adds latency to DML. Computing resources can be utilized more efficiently with proof-of-useful-work (uPoW), which secures transactions by solving relevant real-world problems. We propose a novel uPoW method to minimize per-round latency of DML. The uPoW mining process schedules DML instances among multi-access edge computing (MEC) servers by solving a multi-way number partitioning problem. Moreover, poisoning attacks on heterogeneous training data pose significant challenges to blockchain-based DML. To address this problem, we propose a novel aggregation protocol, named$\mathit{Corrected Krum}$, to counter such attacks and improve the convergence speed of DML. By leveraging the mean-field approximation method, training errors are corrected to reduce the negative impact of poisoning attacks. Simulation results show that our proposed blockchain approach can significantly speed up DML compared with benchmarks.

Recent advances in federated learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL and overlook the interests of the FL clients. This may result in unfair treatment of clients, which discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse fairness-aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey that helps readers gain insight into this interdisciplinary field. This article aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by the existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation, and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches and suggest promising future research directions toward FAFL.

In recent years, knowledge graphs (KGs) have been applied in various domains, where the construction and maintenance of the KGs are usually time- and labor-intensive. In this context, constructing shareable KG through multiple constructors is being attempted to reduce costs. In this collaborative process, security and quality issues are critical. The system for constructing shareable KGs should be capable to recover the KG from most malicious attack and to filter out wrong triples from dynamically submitted ones. Blockchain could naturally prevent malicious tampering with its record data, perfect for solving the security issue. However, the integration of multi-source KGs as well as the quality issue still lacks solutions. To address the issues, this paper proposes a blockchain-based high-quality KG collaborative construction framework to ensure the KG quality in its long-term evolution. The framework is built on the underlying consensus mechanism of the blockchain, adopted to an extensible data structure to store multi-source triples on the distributed ledger. A smart contract is implemented to publish triples, assess the contributor credibility and evaluate triple quality to keep the KG in high-quality. Anti-attack mechanisms are designed to defend against malicious triple submissions. Experiments are conducted demonstrating the effectiveness of the framework.

Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences. Although this problem shares some common issues with the conventional estimated time of arrival (ETA), it is more challenging with the following aspects: 1) Inductive inference. Models are required to predict ETA for orders with unseen retailers and addresses; 2) High-order interaction of order semantic information. Apart from the spatio-temporal features, the estimated time also varies greatly with other factors, such as the packaging efficiency of retailers, as well as the high-order interaction of these factors. In this paper, we propose an inductive graph transformer (IGT) that leverages raw feature information and structural graph data to estimate package delivery time. Different from previous graph transformer architectures, IGT adopts a decoupled pipeline and trains transformer as a regression function that can capture the multiplex information from both raw feature and dense embeddings encoded by a graph neural network (GNN). In addition, we further simplify the GNN structure by removing its non-linear activation and the learnable linear transformation matrix. The reduced parameter search space and linear information propagation in the simplified GNN enable the IGT to be applied in large-scale industrial scenarios. Experiments on real-world logistics datasets show that our proposed model can significantly outperform the state-of-the-art methods on estimation of delivery time.

Metaverse, also known as the Internet of 3-D worlds, has recently attracted much attention from both academia and industry. Each virtual subworld, operated by a virtual service provider (VSP), provides a type of virtual service. Digital twins (DTs), namely, digital replicas of physical objects, are key enablers. Generally, a DT belongs to the party that develops it and establishes the communication link between the two worlds. However, in an interoperable metaverse, data-like DTs can be “shared” within the platform. Therefore, one set of DTs can be leveraged by multiple VSPs. As the quality of the shared DTs may not always be satisfying, in this article, we propose an agile solution, i.e., a dynamic hierarchical framework, in which a group of Internet of Things devices in the lower level are incentivized to collectively sense physical objects’ status information and VSPs in the upper level determine synchronization intensities to maximize their payoffs. We adopt an evolutionary game approach to model the devices VSP selections and a simultaneous differential game to model the optimal synchronization intensity control problem. We further extend it as a Stackelberg differential game by considering some VSPs to be first movers. We provide open-loop solutions based on the control theory for both formulations. We theoretically and experimentally show the existence, uniqueness, and stability of the equilibrium to the lower level game and further provide a sensitivity analysis for various system parameters. Experiments show that the proposed dynamic hierarchical game outperforms the baseline.

Code search, which aims at retrieving the most relevant code fragment for a given natural language query, is a common activity in software development practice.Recently, contrastive learning is widely used in code search research, where many data augmentation approaches for source code (e.g., semantic-preserving program transformation) are proposed to learn better representations.However, these augmentations are at the raw-data level, which requires additional code analysis in the preprocessing stage and additional training costs in the training stage.In this paper, we explore augmentation methods that augment data (both code and query) at representation level which does not require additional data processing and training, and based on this we propose a general format of representationlevel augmentation that unifies existing methods.Then, we propose three new augmentation methods (linear extrapolation, binary interpolation, and Gaussian scaling) based on the general format.Furthermore, we theoretically analyze the advantages of the proposed augmentation methods over traditional contrastive learning methods on code search.We experimentally evaluate the proposed representationlevel augmentation methods with state-of-theart code search models on a large-scale public dataset consisting of six programming languages.The experimental results show that our approach can consistently boost the performance of the studied code search models.

Spurred by the severe restrictions on mobility due to the COVID-19 pandemic, there is currently intense interest in developing the Metaverse, to offer virtual services/business online. A key enabler of such virtual service is the digital twin, i.e., a digital replication of real-world entities in the Metaverse, e.g., city twin, avatars, etc. The real-world data collected by IoT devices and sensors are key for synchronizing the two worlds. In this paper, we consider the scenario in which a group of IoT devices are employed by the Metaverse platform to collect such data on behalf of virtual service providers (VSPs). Device owners, who are self-interested, dynamically select a VSP to maximize rewards. We adopt hybrid evolutionary dynamics, in which heterogeneous device owner populations can employ different revision protocols to update their strategies. Extensive simulations demonstrate that a hybrid protocol can lead to evolutionary stable states.

With the ubiquitous sensing enabled by the Internet-of-Things (IoT), massive amount of data is generated every second, transforming the way we interact with the world. To manage big data and enable analytics at the edge of the network, large amount of computation power is required to perform the computation intensive tasks. However, the energy-constrained IoT devices are not able to perform the computation tasks without compromising the quality-of-service of the applications. In this paper, we propose a hybrid network in which users can offload their computation tasks to edge servers through coded edge offloading or perform local computation with the wireless power transfer derived from coalitions of unmanned aerial vehicles (UAVs) serving as mobile charging stations. We consider a two-level optimization approach where an optimal UAV coalitional structure that minimizes the network cost is formed. In the performance evaluation, we provide extensive sensitivity analyses to study the performance of the cost minimization approach amid varying network parameters.

In Internet of Things (IoT) employing centralized machine learning, security is a major concern due to the heterogeneity of end devices. Decentralized machine learning (DML) with blockchain is a potential solution. However, blockchain with proof-of-work (PoW) consensus mechanism wastes computing resources and adds latency to DML. Computing resources can be utilized more efficiently with proof-of-useful-work (uPoW), which secures transactions by solving real-world problems. We propose a novel uPoW method that exploits PoW mining to accelerate DML through a task scheduling framework for multi-access edge computing (MEC) systems. To provide a good quality-of-service for the system, we minimize the latency by solving a multi-way number partitioning problem in the extended form. A novel uPoW-based mechanism is proposed to schedule DML tasks among MEC servers effectively. Simulation results show that our proposed blockchain strategies accelerate DML significantly compared with benchmarks.

Recently, coded distributed computing (CDC), with advantages in intensive computation and reduced latency, has attracted a lot of research interest for edge computing, in particular, IoT applications, including IoT data preprocessing and data analytics. Nevertheless, it can be challenging for edge infrastructure providers (EIPs) with limited edge resources to support IoT applications performed in a CDC approach in edge networks, given the additional computational resources required by CDC. In this article, we propose “coded edge federation” (CEF), in which different EIPs collaboratively provide edge resources for CDC tasks. To study the Nash equilibrium, when no EIP has an incentive to unilaterally alter its decision on edge resource allocation, we model the CEF based on the evolutionary game theory. Since the replicator dynamics of the classical evolutionary game are unable to model economic-aware EIPs, which memorize past decisions and utilities, we propose “fractional replicator dynamics” with a power-law fading memory via Caputo fractional derivatives. The proposed dynamics allow us to study a broad spectrum of EIP dynamic behaviors, such as EIP sensitivity and aggressiveness in strategy adaptation, which classical replicator dynamics cannot capture. Theoretical analysis and extensive numerical results justify the existence, uniqueness, and stability of the equilibrium in the fractional evolutionary game. The influence of the content and the length of the memory on the rate of convergence are also investigated.

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL ensures the privacy of the FL workers, its performance is limited by several bottlenecks, which become significant given the increasing amounts of data generated and the size of the FL network. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In CFL, the FL server helps to compute a subset of the partial gradients based on the composite parity data and aggregates the computed partial gradients with those received from the FL workers. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.

For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a federated learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and age of information (AoI), i.e., the time elapsed between data aggregation from the deployed industrial Internet of Things devices to completion of the FL-based training. On one hand, if the data are collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner toward AoI and service latency. The performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme toward satisfying varying preferences of AoI and service latency.

In fighting the COVID-19 pandemic, the main challenges include the lack of prior research and the urgency to find effective solutions. It is essential to accurately and rapidly summarize the relevant research work and explore potential solutions for diagnosis, treatment and prevention of COVID-19. It is a daunting task to summarize the numerous existing research works and to assess their effectiveness. This paper explores the discovery of new COVID-19 research approaches based on dynamic link prediction, which analyze the dynamic topological network of keywords to predict possible connections of research concepts. A dynamic link prediction method based on multi-granularity feature fusion is proposed. Firstly, a multi-granularity temporal feature fusion method is adopted to extract the temporal evolution of different order subgraphs. Secondly, a hierarchical feature weighting method is proposed to emphasize actively evolving nodes. Thirdly, a semantic repetition sampling mechanism is designed to avoid the negative effect of semantically equivalent medical entities on the real structure of the graph, and to capture the real topological structure features. Experiments are performed on the COVID-19 Open Research Dataset to assess the performance of the model. The results show that the proposed model performs significantly better than existing state-of-the-art models, thereby confirming the effectiveness of the proposed method for the discovery of new COVID-19 research approaches.

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this article, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners' participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete amongst each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.

Amid growing concerns on data privacy, Federated Learning (FL) has emerged as a promising privacy preserving distributed machine learning paradigm. Given that the FL network is expected to be implemented at scale, several studies have proposed system architectures towards improving the network scalability and efficiency. Specifically, the Hierarchical FL (HFL) network utilizes cluster heads, e.g., base stations, for the intermediate aggregation and relay of model parameters. Serverless FL is also proposed recently, in which the data owners, i.e., workers, exchange the local model parameters among a neighborhood of workers. This decentralized approach reduces the risk of a single point of failure but inevitably incurs significant communication overheads. To achieve the best of both worlds, we propose the Serverless Hierarchical Federated Learning (SHFL) framework in this paper. The SHFL framework adopts a two-layer system architecture. In the lower layer, the FL workers are grouped into clusters under cluster heads. In the upper layer, the cluster heads exchange the intermediate parameters with their one-hop neighbors without the aid of a central server. To improve the sustainable efficiency of the FL system while taking into account the incentive design for workers marginal contributions in the system, we propose the reputation-aware hedonic coalition formation game in this paper. Specifically, the workers are rewarded for their marginal contribution to the cluster, whereas the reputation opinions of each cluster head is updated in a decentralized manner, thereby deterring malicious behaviors by the cluster head. This improves the performance of the network since cluster heads with higher reputation scores are more reliable in relaying the intermediate model parameters. The simulation results show that our proposed hedonic coalition formation algorithm converges to a Nash-stable partition and improves the network efficiency.

As the amount of data collected for crowdsensing applications increases rapidly due to improved sensing capabilities and the increasing number of Internet of Things (IoT) devices, the cloud server is no longer able to handle the large-scale datasets individually. Given the improved computational capabilities of the edge devices, coded distributed computing has become a promising approach given that it allows computation tasks to be carried out in a distributed manner while mitigating straggler effects, which often account for the long overall completion times. Specifically, by using polynomial codes, computed results from only a subset of devices are needed to reconstruct the final result. However, there is no incentive for the edge devices to complete the computation tasks. In this paper, we present an all-pay auction to incentivize the edge devices to participate in the coded computation tasks. In this auction, the bids of the edge devices are represented by the allocation of their Central Processing Unit (CPU) power to the computation tasks. All edge devices submit their bids regardless of whether they win or lose in the auction. The all-pay auction is designed to maximize the utility of the cloud server by determining the reward allocation to the winners. Simulation results show that the edge devices are incentivized to allocate more CPU power when multiple rewards are offered instead of a single reward.

Task offloading has been proposed and studied to overcome the problem of energy and computation constrained terminals. Computationally intensive tasks are often parallelable, and therefore the execution time can be further improved via a coded distributed computing (CDC) approach, as CDC offers robustness against stragglers by introducing redundant computational tasks. In this paper, we study a user-centric task offloading problem, in which the edge performs the of-floaded computation with CDC. Furthermore, the extent of the straggler's effect on servers is also unknown to the user. This requires users to explore server and code settings of the CDC, and “opportunistically” select the best combo to maximize the utility. For simplicity, we refer to this scenario as opportunistic coded distributed computing. We formulate the problem as an evolutionary game in which each user is self-interested. The payoff is calculated based on the monetary cost of CDC-as-a-Service and total delay, weighted by user-defined parameter values. For the game solution, an evolutionary stable equilibrium (ESS) is used, i.e., probabilistic joint selection of server and code configuration. To obtain the ESS, we present an iterative algorithm based on the revision protocol. A theoretical analysis of equilibrium in terms of existence, uniqueness, stationarity, and stability is provided. Numerical simulations are conducted to support the theoretical findings and the adaption of equilibrium states to the hyper-parameters.

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.

Currently, the data collected by the Internet of Healthcare Things, i.e., healthcare oriented Internet of Things (IoT), still rely on cloud-based centralized data aggregation and processing. To reduce the need for transmission of data to the cloud, the edge computing architecture may be adopted to facilitate machine learning at the edge of the network through leveraging on the amassed computation resources of pervasive IoT devices. In this article, federated learning (FL) is proposed to enable privacy-preserving collaborative model training at the edge of the network across distributed IoT users. However, the users in the FL network may have different willingness to participate (WTP), a hidden information unknown to the model owner. Furthermore, the development of healthcare applications typically requires sustainable user participation, e.g., for the continuous collection of data during which a user’s WTP may change over time. As such, we leverage on the dynamic contract design to consider a two-period incentive mechanism that satisfies the intertemporal incentive compatibility (IIC), such that the self-revealing mechanism of the contract holds across both periods. The performance evaluation shows that our contract design satisfies the IIC constraints and derives greater profits than that of the uniform pricing scheme, thus validating its effectiveness in mitigating the adverse impacts of the information asymmetry.

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owners, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing, computation, and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design, and shows the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction-coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied to solve the allocation of UAV coalitions. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer to support the IoV components independently and not to form any coalition.

Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.

Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is increasingly popular in the Internet of Vehicles (IoV) applications in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owner, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design and shows the efficiency of our matching.

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning. However, the performance of the FL suffers from the failure of communication links and missing nodes. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework. The joint auctioncoalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profitmaximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer not to form any coalition.

Order dispatch is an important area where artificial intelligence (AI) can benefit ride-sharing systems (e.g., Grab, Uber), which has become an integral part of our public transport network. In this paper, we present a multi-agent testbed to study the spread of infectious diseases through such a system. It allows users to vary the parameters of the disease and behaviours to study the interaction effect between technology, disease and people's behaviours in such a complex environment.

Population aging is becoming an increasingly important issue around the world. As people live longer, they also tend to suffer from more challenging medical conditions. Currently, there is a lack of a holistic technology-powered solution for providing quality care at affordable cost to patients suffering from co-morbidity. In this paper, we demonstrate a novel AI-powered solution to provide early detection of the onset of Dementia + Parkinson's disease (DPD) co-morbidity, a condition which severely limits a senior's ability to live actively and independently. We investigate useful in-game behaviour markers which can support machine learning-based predictive analytics on seniors' risk of developing DPD co-morbidity.

Argumentation has gained traction as a formalism to make more transparent decisions and provide formal explanations recently. In this paper, we present an argumentation-based approach to decision making that can support modelling and automated reasoning about complex qualitative preferences and offer dialogical explanations for the decisions made. We first propose Qualitative Preference Decision Frameworks (QPDFs). In a QPDF, we use contextual priority to represent the relative importance of combinations of goals in different contexts and define associated strategies for deriving decision preferences based on prioritized goal combinations. To automate the decision computation, we map QPDFs to Assumption-based Argumentation (ABA) frameworks so that we can utilize existing ABA argumentative engines for our implementation. We implemented our approach for two tasks, diagnostics and prognostics of Alzheimer's Disease (AD), and evaluated it with real-world datasets. For each task, one of our models achieves the highest accuracy and good precision and recall for all classes compared to common machine learning models. Moreover, we study how to formalize argumentation dialogues that give contrastive, focused and selected explanations for the most preferred decisions selected in given contexts.

One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm called Federated Learning (FL). However, communication inefficiency remains a key bottleneck in FL. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this paper, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange of the data owners' participation, and the data owners are free to choose among any clusters to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, given that each cluster head can choose to serve a model owner, the model owners have to compete for the services of the cluster head. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing property of the deep learning based auction.

In recent years, the enhanced sensing and computation capabilities of Internet-of-Things (IoT) devices have opened the doors to several mobile crowdsensing applications. In mobile crowdsensing, a model owner announces a sensing task following which interested workers collect the required data. However, in some cases, a model owner may have insufficient data samples to build an effective machine learning model. To this end, we propose a federated learning (FL)-based privacy-preserving approach to facilitate collaborative machine learning among multiple model owners in mobile crowdsensing. Our system model allows collaborative machine learning without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in the contract theory under information asymmetry. For the latter, to ensure the stability of a federation through preventing free-riding attacks, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. Considering the inherent hierarchical structure of the involved entities, we propose a hierarchical incentive mechanism framework. Using the backward induction, we first solve the contract formulation and then proceed to solve the coalitional game with the merge and split algorithm. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design, in terms of incentive compatibility of our contract design and fair payoffs of model owners in stable federation formation.

In this article, we study the performance of blockchains by analyzing the common prefix depth, chain quality coefficient, and chain growth speed coefficient. These three parameters characterize the liveness and consistency of transactions which are important for the proper operation of the blockchain. We examine how these three parameters are affected under an adaptive double-spend attack (ADSA). To maintain the performance of a blockchain against ADSA, the user nodes can use a larger number, z, of confirmation blocks for validating a transaction. A comparison of the values of z needed to achieve a given target probability of successful attack is provided for ADSA and the traditional double-spend attack with different system models. The results indicate that a larger value of z is required under ADSA. A more realistic reward model for attackers is also introduced. It is found that the expected reward of an attacker decreases rapidly to zero as z is increased.

The process of arguing is also the process of justifying and explaining. Here, we focus on argumentative explanations in Abstract Bipolar Argumentation. We propose new defence and acceptability semantics, which operates on both attack and support relations, and use them to formalize two types of explanations, concise and strong explanations. We also show how to compute the explanations with Bipolar Dispute Trees.

In artificial intelligence (AI) mediated workforce management systems (e.g., crowdsourcing), long-term success depends on workers accomplishing tasks productively and resting well. This dual objective can be summarized by the concept of productive laziness. Existing scheduling approaches mostly focus on efficiency but overlook worker wellbeing through proper rest. In order to enable workforce management systems to follow the IEEE Ethically Aligned Design guidelines to prioritize worker wellbeing, we propose a distributed Computational Productive Laziness (CPL) approach in this paper. It intelligently recommends personalized work-rest schedules based on local data concerning a worker's capabilities and situational factors to incorporate opportunistic resting and achieve superlinear collective productivity without the need for explicit coordination messages. Extensive experiments based on a real-world dataset of over 5,000 workers demonstrate that CPL enables workers to spend 70% of the effort to complete 90% of the tasks on average, providing more ethically aligned scheduling than existing approaches.

With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.

Currently, much of machine learning is opaque, just like a "black box." However, in order for humans to understand, trust and effectively manage the emerging AI systems, an AI needs to be able to explain its decisions and conclusions. In this paper, I propose an argumentation-based approach to explainable AI, which has the potential to generate more comprehensive explanations than existing approaches.

Over the years, government service provision in China has been plagued by inefficiencies. Previous attempts to address this challenge following a toolbox e-government system model in China were not effective. In this paper, we report on a successful experience in improving government service provision in the domain of social insurance in Shandong Province, China. Through standardization of service workflows following the Complete Contract Theory (CCT) and the infusion of an artificial intelligence (AI) engine to maximize the expected quality of service while reducing waiting time, the Smart Human-resource Services (SmartHS) platform transcends organizational boundaries and improves system efficiency. Deployments in 3 cities involving 2,000 participating civil servants and close to 3 million social insurance service cases over a 1 year period demonstrated that SmartHS significantly improves user experience with roughly a third of the original front desk staff. This new AI-enhanced mode of operation is useful for informing current policy discussions in many domains of government service provision.

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.

In this paper, we propose a novel blockchain‐based contractual routing (BCR) protocol for a network of untrusted IoT devices. In contrast to conventional secure routing protocols in which a central authority (CA) is required to facilitate the identification and authentication of each device, the BCR protocol operates in a distributed manner with no CA. The BCR protocol utilizes smart contracts to discover a route to a destination or data gateway within heterogeneous IoT networks. Any intermediary device can guarantee a route from a source IoT device to a destination device or gateway. We compare the performance of BCR with that of the Ad-hoc On‐Demand Distance Vector (AODV) routing protocol in a network of 14 devices. The results show that the routing overhead of the BCR protocol is 5 times lower compared to AODV at the cost of a slightly lower packet delivery ratio. BCR is fairly resistant to both Blackhole and Greyhole attacks. The results show that the BCR protocol enables distributed routing in heterogeneous IoT networks.

Rehabilitation exergames can engage the elderly in physical activities and help them recover part of their deteriorating capabilities. However, most existing exergames lack measures of how suitable they are to specific individuals. In this paper, we propose the Computational Person-Environment Fit model to evaluate the adaptability of the exergames to each individual elderly user.

We consider the problem of making efficient quality-time-cost trade-offs in collaborative crowdsourcing systems in which different skills from multiple workers need to be combined to complete a task. We propose CrowdAsm - an approach which helps collaborative crowdsourcing systems determine how to combine the expertise of available workers to maximize the expected quality of results while minimizing the expected delays. Analysis proves that CrowdAsm can achieve close to optimal profit for workers in a given crowdsourcing system if they follow the recommendations.

The current generation of senior citizens are enjoying unparalleled levels of good health than previous generations. The need for personal fulfilment after retirement has driven many of them to participate in productive aging activities such as volunteering. This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. It provides an algorithmic crowdsourcing platform to enable seniors to contribute their effort through productive aging activities and help organizations efficiently utilize seniors' collective productivity.

Algorithmic Crowdsourcing (AC) is an emerging field in which computational methods are proposed to automate cer- tain aspects of crowdsourcing. A number of AC methods have proposed recently in an attempt to address this problem. However, existing AC approaches are based on highly simplified models of worker behaviour which limit their practical applicability. To make efficient utilization of human resources for crowdsourcing tasks, the following tech- nical challenges remain open: Fairness of the solution, temporal changes in behaviour, optimizing wellbeing, and non-compliance by users. For AI researchers to propose effective solutions to these challenges, labelled datasets reflecting various aspects of human decision-making related to task allocation in crowd-sourcing are needed. We construct an anonymized dataset based on player behavior trajectories captured by a multiagent game platform - Agile Manage. It allows players to demonstrate their task delegation strategies under different scenarios based on key characteristics involved in crowdsourcing task allocation. The game adopts implicit human computation in which players contribute data which are valuable for research through informal games.

As the global population ages, assisted living technologies for the elderly are becoming more popular. A person normally performs activities of daily living (ADLs) on a regular basis. A person's ability to perform recurring ADLs indicates the person's wellness. Anomalies in activity patterns of a person might indicate changes in the person's wellness. A method is proposed in this paper for detecting anomalies in activity patterns of a lone occupant using his/her electricity consumption data. The proposed method infers anomalies in activity patterns of an occupant from electricity consumption patterns instead of explicitly monitoring the underlying individual activities. The proposed method provides a score which is a quantitative assessment of anomalies in electricity consumption pattern of an occupant for a given day. A survey was conducted to obtain the hourly activities of three lone occupants for a month. From the survey, the level of suspicion values which are quantitative assessments of anomalies in activity patterns of the occupants were deduced. Using Fuzzy C-Means (FCM) clustering with Euclidean distance measure, the scores and level of suspicion values were clustered respectively. A day was then classified as regular or irregular from an electricity consumption perspective (score) and an activity perspective (level of suspicion value) respectively. Our results show that anomalies in electricity consumption patterns correlate well with anomalies in the underlying activity patterns.

Activity recognition is a key problem in multi-sensor systems. In this work, we introduce Computational Assumption-based Argumentation, an argumentation approach that seamlessly combines sensor data processing with high-level inference. Our method gives classification results comparable to machine learning based approaches with reduced training time while also giving explanations.

Wireless energy harvesting in a decode-and-forward (DF) relay network is studied. The relay node is energy constrained and harvests energy from the radio frequency (RF) signal of the source node. The RF signal also carries information from the source to be forwarded via the relay to a destination in the presence of interference from unknown sources. We study the performance of three relaying protocols, the time switching relaying (TSR) protocol, the power splitting relaying (PSR) protocol and a proposed hybrid TSR-PSR protocol. Analytical expressions for the outage probability and throughput in the delay-sensitive transmission mode are derived for the three protocols. Using the derived expressions, we compare the throughput performances of these protocols. Our results demonstrate that the throughput of the hybrid protocol is generally higher than that of TSR and PSR protocols.

Long Term Evolution (LTE) has been standardized by the 3GPP consortium since 2008 in 3GPP Release 8, with 3GPP Release 12 being the latest iteration of LTE Advanced (LTE-A), which was finalized in March 2015. High Efficiency Video Coding (H.265) has been standardized by MPEG since 2012 and is the Video Compression technology targeted to deliver High-Definition (HD) and Ultra High-Definition (UHD) Video Content to users. With video traffic projected to represent the lion's share of mobile data traffic, providing users with high Quality of Experience (QoE) is key to designing 4G systems and future 5G systems. In this paper, we present a cross-layer scheduling framework which delivers frames to unicast video users by exploiting the encoding features of H.265. We extract information on frame references within the coded video bitstream to determine which frames have higher utility for the H.265 decoder located at the user's device and evaluate the performances of best-effort and video users in 4G networks using finite buffer traffic models. Our results demonstrate that there is significant potential to improve the QoE of all users compared to the baseline Proportional Fair method by adding media-awareness in the scheduling entity at the Medium Access Control (MAC) layer of a Radio Access Network (RAN).

With the prevalence of social networks, social recommendation is rapidly gaining popularity. Currently, social information has mainly been utilized for enhancing rating prediction accuracy, which may not be enough to satisfy user needs. Items with high prediction accuracy tend to be the ones that users are familiar with and may not interest them to explore. In this paper, we take a psychologically inspired view to recommend items that will interest users based on the theory of social curiosity and study its impact on important dimensions of recommender systems. We propose a social curiosity inspired recommendation model which combines both user preferences and user curiosity. The proposed recommendation model is evaluated using large scale real world datasets and the experimental results demonstrate that the inclusion of social curiosity significantly improves recommendation precision, coverage and diversity.

Wireless energy harvesting is regarded as a promising energy supply alternative for energy-constrained wireless networks. In this paper, a new wireless energy harvesting protocol is proposed for an underlay cognitive relay network with multiple primary user (PU) transceivers. In this protocol, the secondary nodes can harvest energy from the primary network (PN) while sharing the licensed spectrum of the PN. In order to assess the impact of different system parameters on the proposed network, we first derive an exact expression for the outage probability for the secondary network (SN) subject to three important power constraints: 1) the maximum transmit power at the secondary source (SS) and at the secondary relay (SR); 2) the peak interference power permitted at each PU receiver; and 3) the interference power from each PU transmitter to the SR and to the secondary destination (SD). To obtain practical design insights into the impact of different parameters on successful data transmission of the SN, we derive throughput expressions for both the delay-sensitive and the delay-tolerant transmission modes. We also derive asymptotic closed-form expressions for the outage probability and the delay-sensitive throughput and an asymptotic analytical expression for the delay-tolerant throughput as the number of PU transceivers goes to infinity. The results show that the outage probability improves when PU transmitters are located near SS and sufficiently far from SR and SD. Our results also show that when the number of PU transmitters is large, the detrimental effect of interference from PU transmitters outweighs the benefits of energy harvested from the PU transmitters.

Reputation systems aggregate the ratings provided by buyers to gauge the reliability of sellers in e-marketplaces. The evaluation accuracy of seller reputation significantly impacts the sellers' future utility. The existence of unfair ratings is well-recognized to negatively affect the accuracy of reputation evaluation. Most of the existing approaches dealing with unfair ratings focus on filtering/discounting/aligning the possible unfair ratings caused by malicious attacks or subjective difference. However, these approaches are not effective against unfair ratings in the form of misreporting (e.g., a well-behaving buyer misjudged a seller and provided a negative rating to a transaction which deserves a positive one, and the buyer is willing to revert the misreported negative rating). In this case, how should the buyer undo the damage caused by such misreported ratings and help the seller recover utility loss? In this paper, we propose a reputation revision mechanism to mitigate the negative effects of the misreported ratings. The proposed mechanism temporarily inflates the reputation of the misjudged seller for a period of time, which allows the seller to recover his utility loss caused by the misreported ratings. Extensive realistic simulation based experiments demonstrate the necessity and effectiveness of the proposed mechanism.

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i.e., workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker's reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.

Emotions have a direct influence on an individual's physical and cognitive behaviour, as well as their performance, a student with a positive emotional state will learn and perform better. This paper presents an agent framework that addresses the relationship between user's state of emotion during learning and the modification of learning pace and feedback-type in a virtual environment for learning effectiveness. The technique exploits the structure of emotion-evaluation from user's current interaction to dynamically regulate learning pace within a Virtual Learning Enviornment (VLE) via a proposed Emotion Regulation Agent (ERA) system to derive meaningful emotion information from user's interactions based on a simplified version of the Pleasure, Arousal and Dominance (PAD) emotion model. This information then regulates learning by adjusting the type of task and learning information to promote a beneficial and sustainable learning experience by optimizing learning emotion.

Weak supervisory information of web images, such as captions, tags, and descriptions, make it possible to better understand images at the semantic level. In this paper, we propose a novel online multimodal co-indexing algorithm based on Adaptive Resonance Theory, named OMC-ART, for the automatic co-indexing and retrieval of images using their multimodal information. Compared with existing studies, OMC-ART has several distinct characteristics. First, OMC-ART is able to perform online learning of sequential data. Second, OMC-ART builds a two-layer indexing structure, in which the first layer co-indexes the images by the key visual and textual features based on the generalized distributions of clusters they belong to; while in the second layer, images are co-indexed by their own feature distributions. Third, OMC-ART enables flexible multimodal search by using either visual features, keywords, or a combination of both. Fourth, OMC-ART employs a ranking algorithm that does not need to go through the whole indexing system when only a limited number of images need to be retrieved. Experiments on two published data sets demonstrate the efficiency and effectiveness of our proposed approach.

An energy efficient collaborative spectrum sensing (EE-CSS) protocol, based on trust management, is proposed. The protocol achieves energy efficiency by reducing the total number of sensing reports exchanged between the honest secondary users (HSUs) and the secondary user base station (SUBS) in a traditional collaborative spectrum sensing (T-CSS) protocol. It is shown that the minimum total number of sensing reports required to satisfy a target global false alarm (FA) and missed detection (MD) probabilities in T-CSS is higher than that in EE-CSS. Expressions for the steady-state average SU trust value τ̅ and total number N̅ of SU sensing reports transmitted are derived, as is an expression for the energy consumption, in EE-CSS and T-CSS. The global FA and detection probabilities Qfand Qdare obtained for a commonly used decision fusion technique. The impact of link outages on τ̅, N̅, Qf, and Qdis also analyzed. The results show that the energy consumption in EE-CSS can be much lower compared to that in T-CSS for long range communications where the transmit energy is dominant.

With the world population aging rapidly, improving the quality of life for senior citizens has become an important societal issue. Parkinson's Disease (PD) is one of the most debilitating neuro-degenerative disorders that seriously affect the seniors' quality of life. In recent years, video games have been shown to be a viable way through which partial rehabilitation for PD can be carried out in a fun and low cost manner. Earlier research has shown that both patients' physical and mental conditions can be improved by playing video games. However, so far, the available games developed for PD are mostly intended for rehabilitation purposes. PD diagnosis still depends on the traditional neurological exams and experience of doctors, which require the patients to become self-aware of the symptoms and are usually too late for the patients to delay the progression of PD. To support the early detection of PD symptoms, we propose a fuzzy logic based PD risk predictor that has been implemented in a tablet game platform. The player's behavior data in the game environment are captured unobtrusively and analyzed in real-time. The player's current risk of developing PD is estimated using the proposed fuzzy logic based approach, which will help the player to be aware of high risk of having PD at an earlier stage. A pilot evaluation has been conducted to demonstrate the effectiveness of the proposed approach.

A mobile, autonomous searcher is tasked with finding the source node of a broadcast message in a randomly deployed network of location-agnostic wireless sensor nodes. Messages are assumed to propagate by flooding, with random node-to-node delays. In networks of this type, the hop count of the broadcast message, given the distance from the source node, can be approximated by a simple parametric distribution. The mobile searcher can interrogate a nearby sensor node to obtain, with a given success probability, the hop count of the broadcast message. We model the search as an infinite-horizon, undiscounted cost, online POMDP and solve it approximately through policy rollout. The cost-to-go at the rollout horizon is approximated by a heuristic based on an optimal search plan in which path constraints and assumptions about future information gain are relaxed. This cost can be computed efficiently, which is essential for the application of Monte Carlo methods, such as rollout, to stochastic planning problems. Finally, we demonstrate that our rollout approach outperforms a popular method of target search based on a myopic, mutual information utility.

A centralized trust-based collusion attack strategy in conjunction with an integer linear programming is proposed to compromise the decision of the fusion center in a two-phase energy efficient collaborative spectrum sensing (CSS) strategy, namely EE-CSS, which was discussed in [1]. The proposed strategy aims to attack only when it is likely to compromise the decision of the fusion center (FC). The results show that the proposed collusion attack can impact the global false alarm and miss detection probabilities drastically. A mitigating strategy based on cross-correlation of sensing reports is proposed to identify the secondary users with unusual behavior and to eliminate them from the decision making process at the FC.

A wireless energy harvesting protocol is proposed for a decode-and-forward relay- assisted secondary user (SU) network in a cognitive spectrum sharing paradigm. An expression for the outage probability of the relay-assisted cognitive network is derived subject to the following power constraints: 1) the maximum power that the source and the relay in the SU network can transmit from the harvested energy, 2) the peak interference power from the source and the relay in the SU network at the primary user (PU) network, and 3) the interference power of the PU network at the relay-assisted SU network. The results show that as the energy harvesting conversion efficiency improves, the relay- assisted network with the proposed wireless energy harvesting protocol can operate with outage probabilities below 20% for some practical applications.

A two-phase energy efficient collaborative spectrum sensing (CSS) strategy, namely EE-CSS, was proposed in [1], and expressions were derived for the steady-state (long term) average trust values and number of transmitted reports for honest as well as malicious secondary users. In this paper, we study the transient behavior of these quantities. A closed form expression is derived for the transient behavior of the average trust values. An event-based method for computing the average number of transmitted reports is described and its computational complexity is discussed. An approximate method as well as upper and lower bounds are proposed for the average number of transmitted reports. The results are verified using computer simulations.

An energy efficient collaborative spectrum sensing (EE-CSS) protocol, based on trust management, is proposed. The protocol reduces the total number of sensing reports exchanged between the secondary users (SUs) and the secondary user base station (SUBS) when compared to a traditional collaborative spectrum sensing (T-CSS) protocol in which each SU transmits a sensing report to the SUBS. In addition, the minimum total number of sensing reports required to satisfy a target global false alarm and miss detection probabilities in T-CSS is shown to be higher than that in EE-CSS. Expressions for the average steady-state trust values of SUs and the average total number of sensing reports transmitted by the SUs to the SUBS in EE-CSS are derived. The global false alarm (FA) and miss detection (MD) probabilities are analyzed for a commonly used decision fusion technique.

We propose transmit antenna selection (TAS) with decode-and-forward relaying as an effective approach to reduce interference in cognitive multiple-input multiple-output (MIMO) relay networks. To demonstrate this, we derive new closed-form expressions for the exact and asymptotic outage probability of TAS/MRC with multiple antennas at the primary and secondary users. We consider underlay spectrum sharing where the secondary users (SUs) transmit in the presence of multiple primary users (PUs). We consider independent Nakagami-m fading in both the primary and secondary networks. Several important design insights are revealed. We find that TAS/MRC achieves a full diversity when the transmit power at the SUs is proportional to the peak interference power at the PUs. Furthermore, we highlight that this diversity gain is completely independent of the number of antennas at the PUs.

An expression is derived for the probability mass function (PMF) of the relay transmit power in a variable gain amplify-and-forward (VG-AF) opportunistic wireless relay network (OWRN). The PMF is used to calculate the average relay transmit power. An expression is also obtained for evaluating the transition probabilities between energy states in a Markov chain model of the OWRN. This model is used to compute the average OWRN lifetime for a small number of relays, allowable transmit power levels, and low initial relay energy levels. Unfortunately, the computational complexity of this approach becomes prohibitive as the number of relays, transmit power levels, and initial energy levels increase. A low-complexity method, based on an existing expression and the average relay transmit power, is used to estimate the average network lifetime. The method is shown to yield very accurate results for practical initial relay energy levels.

Radio resource management (RRM) is widely regarded as a critical component in improving spectral efficiency of beyond third generation (3G) cellular air interfaces. Much of the published work in Orthogonal Frequency Division Multiplexing (OFDM) RRM focuses on exploiting multi-user, multi-channel and multi-application diversity. In this paper, we propose a Quality of Service (QoS)-aware bit scheduling framework to increase the flexibility and granularity of the resource allocation algorithms by adaptively matching the QoS requirements of the application bits of users to the characteristics of the OFDM subcarriers in a mixed-traffic environment. We show that with the finesse control of QoS-aware bit scheduling, it is possible to simultaneously achieve both an increase in user throughput and a reduction in user packet drop probability by accepting a within packet drop threshold increase in user latency. The performance gains obtainable are quantified in terms of user throughput, user latency, user jitter and user packet drop probability.

Trust is an important concept in human interactions which facilitates the formation and continued existence of functional human societies. In the first decade of the 21st century, computational trust models have been applied to solve many problems in wireless communication systems. This cross-disciplinary research has yielded many innovative solutions. In this paper, we examine the latest methods which have been proposed by researchers to manage trust and reputation in wireless communication systems. Specifically, we survey the state of the art in the application of trust models in the fields of mobile ad hoc networks (MANETs), wireless sensor networks (WSNs), and cognitive radio networks (CRNs). We classify the mainstream methods into natural categories and illustrate how they complement each other in achieving design goals. Major research directions are also outlined.

Usually, agents within multiagent systems represent different stakeholders that have their own distinct and sometimes conflicting interests and objectives. They would behave in such a way so as to achieve their own objectives, even at the cost of others. Therefore, there are risks in interacting with other agents. A number of computational trust models have been proposed to manage such risk. However, the performance of most computational trust models that rely on third-party recommendations as part of the mechanism to derive trust is easily deteriorated by the presence of unfair testimonies. There have been several attempts to combat the influence of unfair testimonies. Nevertheless, they are either not readily applicable since they require additional information which is not available in realistic settings, or ad hoc as they are tightly coupled with specific trust models. Against this background, a general credibility model is proposed in this paper. Empirical studies have shown that the proposed credibility model is more effective than related work in mitigating the adverse influence of unfair testimonies.

The problem of allocating resources for user transmissions on the downlink of a Long Term Evolution (LTE) cellular communication system is studied. A novel optimal multiuser scheduler is proposed and its performance is evaluated. Numerical results show that the system performance improves with increasing correlation among OFDMA sub-carriers. It is found that a limited amount of feedback information can provide a relatively good performance. A sub-optimal scheduler with a lower computational complexity is also proposed, and shown to provide good performance. The sub-optimal scheme is especially attractive when the number of users is large, as the complexity of the optimal scheme may be unacceptably high in many practical situations.

A diversity scheme which exploits the existence of a source-destination path to improve the lifetime and outage probability of a wireless selective relay network with Amplify-and-Forward (AF) relays is proposed and studied. The destination obtains a portion of the required signal to noise ratio (SNR) during the broadcast phase (Phase I) from the source and the remainder of the required SNR from a selected relay in the second phase (Phase II) of the transmission protocol. An algorithm based on an energy conserving dynamic transmit power threshold is proposed to improve the network lifetime. It is shown that the proposed scheme improves network lifetimes for the following four different relay selection strategies: Minimum Transmit Power (MTP), Maximum Residual Energy (MRE), Maximum residual-Energy Index (MEI), and Minimum Outage Probability (MOP).

Multiuser scheduling is an important aspect in the performance optimisation of a wireless network as it allows multiple users to efficiently access a shared channel by exploiting multiuser diversity. For example, the 3GPP cellular standard supports multiuser scheduling in the high speed downlink packet access (HSDPA) feature. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. Multiuser scheduling is studied assuming the availability of perfect CSI, which would require a high bandwidth overhead. A more realistic imperfect CSI feedback in the form of a finite set of channel quality indicator values is assumed, as specified in the HSDPA standard. A global optimal approach and a simulated annealing (CSA) approach are used to solve the optimisation problem. Simulation results suggest that the performances of the two approaches are very close even though the complexity of the simulated annealing (SA) approach is much lower. The performance of a simple greedy approach is found to be significantly worse.

The challenge of scheduling user transmissions on the downlink of a long term evolution (LTE) cellular communication system is addressed. A maximum rate algorithm which does not consider fairness among users was proposed in . Here, a multiuser scheduler with proportional fairness (PF) is proposed. Numerical results show that the proposed PF scheduler provides a superior fairness performance with a modest loss in throughput, as long as the user average SINRs are fairly uniform. A suboptimal PF scheduler is also proposed, which has a much lower complexity at the cost of some throughput degradation.

The problem of subcarrier, bit and power allocation for an OFDM based cognitive radio system in which one or more spectrum holes exist between multiple primary user (PU) frequency bands is studied. The cognitive radio user is able to use any portion of the frequency band as long as it does not interfere unduly with the PUs' transmissions. We formulate the resource allocation as a multidimensional knapsack problem and propose a low-complexity, greedy max-min algorithm to solve it. The proposed algorithm is simple to implement and simulation results show that its performance is very close to (within 0.3% of) the optimal solution.

Cognitive radio (CR) is a novel concept for improving spectrum utilization in wireless communication systems by permitting secondary (unlicensed) users to access those frequency bands which are not currently being used by primary (licensed) users. A CR user has the ability to change its transmit parameters rapidly according to the environment it senses. Orthogonal frequency division multiplexing (OFDM) modulation is a good candidate for CR systems due to its flexibility in allocating resources among secondary users. In this paper, the design of a fast and efficient method for dynamically allocating subcarriers, transmit powers and bits to secondary users in a multiuser (MU) OFDM-based CR system is considered. A memetic algorithm (MA) is proposed and shown to provide an improved performance over previously reported algorithms.

Bit or frame error rates are commonly used as performance measures in wireless communication systems. However, in emerging applications such as voice over IP (VoIP), the bit rate outage probability is often a more useful performance measure. In this paper, an accurate method is proposed for approximating the probability distribution of the downlink received signal-to-interference ratio (SIR) in the high speed downlink packet access (HSDPA) channel of a 3GPP network. Based on this distribution, a power adjustment scheme is proposed to minimize the weighted sum of bit rate outage probabilities for multiple users. It is shown that the proposed method can greatly improve outage probability fairness without incurring a very large degradation in throughput.

The problem of efficiently allocating resources to high speed uplink packet access (HSUPA) users in a 3GPP network is studied. A discrete optimization problem is formulated in which user channel qualities and buffer sizes are used at the base station to jointly allocate uplink resources. To circumvent the possibility of local optima, a linearized version of this formulation is also presented. It is shown that knowledge of user buffer sizes can significantly improve the overall achievable bit rate.

We study the subcarrier, bit and power allocation problem for multiuser OFDM-based multi-cell cognitive radio (CR) systems in which one or more spectrum holes exist between multiple primary user (PU) frequency bands. The cognitive radio users (CRUs) are able to share any portion of the frequency band with other CRUs and the PUs as long as this does not interfere unduly with the PUs' transmissions. Both cochannel interference (CI) from other CRUs as well as mutual interference (MI) between the CRUs and the PUs are considered. The resource allocation problem is formulated as a multi-dimensional knapsack problem and a relatively simple, greedy max-min algorithm is proposed to solve it. Simulation results show that the max-min algorithm yields solutions which are close to (within 5% of) optimal. Sharing of the whole band can provide a substantial performance improvement over schemes which use guard bands to protect PU frequency bands and do not allow CRUs to use the PU bands.

We study the problem of allocating subchannels, bits, and powers for variable rate services in an OFDM based cognitive radio (CR) system, in which available system resources are highly dynamic. In a resource-limited situation under which the nominal rate requirements of users cannot be satisfied, it is desirable to provide fair degradation among users. In a situation with abundant resources, we may choose to maximize system throughput while ensuring that user nominal rate requirements are met. The problem is formulated as a single objective nonlinear optimization problem using techniques from goal programming. A distributed resource allocation algorithm is proposed and simulation results are obtained which show that the proposed distributed algorithm provides good fairness and aggregate bit rates close to (within 8% of) optimal values.

Wireless communications has emerged as one of the largest sectors of the telecommunications industry and one of the most promising growth areas into the next decade. To meet the challenges of deploying an efficient wireless multimedia network, it is useful to consider the various open systems interconnection (OSI) layers together when designing the network to take into account quality of service (QoS) requirements at the medium access control (MAC) layer where the scheduling and resource allocation algorithms reside. In this paper, we propose and evaluate an adaptive cross layer (physical, MAC and application) scheduling policy with flow multiplexing to fairly meet intra-user and inter-user QoS requirements.

A procedure for determining the probability distribution of the rth order statistic, Gr:L, r=1, 2, , L, among a set of L correlated Nakagami diversity branch gains G1, G2, , GL has been described in David and Nagaraja (2003) and Elkashlan et al. (2008). The results are used to evaluate the bit error rate (BER) of general order selection (GOS), a diversity method in which the rth order branch is selected for transmission, over correlated Nakagami fading branches. GOS can be used to improve system throughput and provide various levels of services, both of which are highly desirable in high-speed communication systems. Numerical and simulation results are presented and used to illustrate the effects of fading correlation on the BER associated with the rth order gain branch.

In this letter, the cumulative distribution function (and hence outage probability) of the r-th order signal-to-noise ratio from a set of n correlated Nakagami fading branches is studied. Numerical results are presented to illustrate the effect of fading correlation and the fading severity parameter. The accuracy of a simple exchangeable approximation is also examined.

The impact of channel state estimation errors in a CDMA system employing adaptive modulation and coding in conjunction with multicodes is studied. The channel is modelled as a finite-state Markov chain and the performances using (1) a simple moving average (SMA) filter (2) a hidden Markov model (HMM) filter to estimate the channel state are compared. The results show that the HMM filter is more robust and provides a significant throughput improvement over the SMA filter, especially when the channel estimate is quite noisy or the normalized Doppler rate is small.

In this paper, an expression for the joint probability density function (pdf) of the order statistics for a set of arbitrarily correlated Nakagami-m fading channels is derived. This result is useful in analyzing the performance of a variety of diversity schemes which involve branch selections. The derivation also yields a computationally efficient method for obtaining the marginal pdf of the p-th order statistic for a set of arbitrarily correlated Nakagami-m random variables. As an illustration, the derived result is applied to the performance analysis of the generalized selection combining (GSC) scheme with arbitrarily correlated branches.

In performance analyses of wireless communication systems, expressions for the probability distributions of certain random variables are often needed. While exact efficiently computable closed-form expressions are desirable, they are often very difficult, if at all possible, to obtain. In this paper, the Pearson system of distributions is studied as a means to obtain approximate expressions for such distributions. The usefulness of this approach is illustrated for a number of diversity techniques employed on Nakagami and Weibull fading channels. The resulting relatively simple approximations are shown to be quite accurate.

Ultra-wideband wireless transmission has attracted considerable attention both in academia and industry. For high-rate and short-range transmission, direct sequence based ultra-wideband (DS-UWB) systems are a strong contender for consumer market applications. Due to the large transmission bandwidth, the UWB channel is characterized by a long root-mean-square delay spread and the RAKE receiver cannot always overcome the resulting intersymbol interference. We therefore study equalization for DS-UWB systems. This paper is comprised of two parts. In this first part, we consider DS-UWB with binary phase-shift keying (BPSK) modulation, which is the mandatory transmission mode for DS-UWB systems promoted by the UWB Forum industry alliance. We derive matched filter bounds for optimum equalization taking into account practical constraints like receiver filtering, sampling, and the number of RAKE fingers when RAKE preprocessing is applied at the receiver. Our results show that chip-rate sampling is sufficient for close-to-optimum performance. For analysis of suboptimum equalization strategies we further study the distribution of the zeros of the channel transfer function including RAKE combining. Our findings suggest that linear equalization is well suited for the lower data rate modes of DS-UWB systems, whereas nonlinear equalization is preferable for high-data rate modes. Moreover, we devise equalization schemes with widely linear processing, which improve performance while not increasing equalizer complexity. Simulation and numerical results confirm the significance of our analysis and equalizer designs and show that low-complexity (widely) linear and nonlinear equalizers perform close to the pertinent matched filter bound limit.

Direct-sequence ultra wideband (DS-UWB) transmission is a strong contender for the physical layer of high data-rate short-range UWB systems. Since long delay spreads in UWB channels cause significant intersymbol interference, DS-UWB systems require equalization. In this second part of two papers, we investigate equalization for DS-UWB with 4-ary biorthogonal keying (4BOK), which is one of the two modulation formats that was proposed for standardization by the IEEE 802.15.3a task group. To this end, we first derive expressions for the bit error rate (BER) according to the matched-filter bound for 4BOK DS-UWB, which serve as theoretical performance limits for equalization. We then devise structures and methods for filter optimization for low-complexity linear and nonlinear equalization schemes. In this context, we develop a new equivalent multiple-input multiple-output (MIMO) description of 4BOK DS-UWB, which facilitates the design of efficient equalizers using MIMO filter optimization techniques. Furthermore, we propose the application of widely linear processing to these equalizers. Simulation and semianalytical results show that MIMO equalization is greatly advantageous over more obvious non-MIMO schemes and that the proposed MIMO equalizers allow for power-efficient 4BOK DS-UWB transmission close to the theoretical limits with moderate computational complexity.

In this paper, some analytical results for general order selection (GOS) over independent but not necessarily identically distributed (i.n.d.) Weibull and Nakagami fading channels are presented. The Weibull fading parameters are assumed to be equal whereas the Nakagami fading parameters are assumed to be integer-valued. It is shown that the pdf of the q-th order statistic can be expressed as a linear combination of Weibull and Nakagami pdf's respectively. Closed-form expressions for the moment generating functions and general moments are derived. In addition, exact closed-form expressions for the symbol error rate are obtained for a number of modulation schemes. Numerical results show that for the same average channel gains, the performance on i.n.d. channels may be better or worse than on i.i.d. channels.

For the problem of allocating radio resources in the downlink of a CDMA network studied in [1], the modulation and coding schemes, numbers of multicodes, and transmit powers used for all mobile stations (MS's) are jointly chosen so as to maximize the total transmission bit rate during each scheduling interval, subject to certain constraints. The approach employs discrete mathematical programming, which is potentially complex, and analytical solutions are not available. In this paper, sub-optimal approaches are examined. In particular, analytical expressions are derived for optimal resource allocation involving a single MS. Based on the single-MS solution, a sub-optimal, sequential optimization procedure for multiple MS's is presented. Numerical results show that when the order for allocating resources to MS's is judiciously chosen based on the MS channel conditions and/or traffic loads, the sequential solution is an attractive alternative to the more complex joint optimization. This is especially true when radio resources are scarce.

Direct-sequence spreading ultra-wideband (DS-UWB) is a strong contender for the standardization of the physical layer of wireless personal area networks (WPANs) by the IEEE 802.15.3a committee. Since long delay spreads in UWB channels cause significant intersymbol interference, equalization is required at the receiver of DS-UWB systems. In this paper, we investigate equalization for DS-UWB with 4-ary bi-orthogonal keying (4BOK), which is one of the two proposed modulation formats. We first derive the corresponding matched-filter bound (MFB), which is the theoretical performance limit. Considering a new equivalent multiple-input multiple-output (MIMO) description of 4BOK DS-UWB, we then devise linear and nonlinear MIMO equalizers. Furthermore, we propose the application of widely linear (WL) processing to these equalizers. Simulation and semi-analytical results show that the proposed MIMO equalizers allow for power-efficient 4BOK DS-UWB transmission close to the theoretical limits with moderate computational complexity.

In performance analyses of wireless communication systems, expressions for the probability distributions of certain random variables are often needed. While exact, efficiently computable closed-form expressions are desirable, they are often very difficult, if at all possible, to obtain. In this paper, the Pearson system of distributions is studied as a means to obtain approximate expressions for such distributions. The usefulness of this approach is illustrated for a number of diversity techniques employed on Nakagami and Weibull fading channels. The resulting relatively simple approximations are shown to be very accurate

In this paper, some analytical results for general order selection (GOS) over independent but not necessarily identically distributed (i.n.d.) Weibull and Nakagami fading channels are presented. The GOS model is important in multiuser scheduling. By transforming the probability density function (pdf) of the selected channel signal to noise ratio (SNR) into an appropriate form, exact closed-form expressions for the corresponding moment generating function (MGF) and general moments are derived for Weibull fading channels. Using the derived results together with those in R. Kwan and C. Leung (2005), exact closed-form expressions for the symbol error rate are obtained for a number of modulation schemes over the i.n.d. Weibull and Nakagami fading channels. Numerical results show that for the same average channel gains, the performance over i.n.d. channels may be better or worse than over i.i.d. channels

Adaptive modulation and coding (AMC) has been adopted in the 3GPP standard in order to improve spectral efficiency. In order to increase the granularity of the adaptation and to provide higher bit rates, multicode transmission is employed. Since the use of AMC requires knowledge of the channel state, the accuracy of this information is important. In practice, errors in estimating the channel state are inevitable, resulting in performance degradation. The average bit rate performance of AMC with multicodes is studied for a CDMA system experiencing Nakagami fading and channel estimation errors. The results are obtained in terms of the generalized Marcum Q-function. Numerical results are provided to illustrate the performance degradations due to inaccuracies in estimating the channel.

A channel aware multiple access scheme based on slow frequency-hopping code-division multiple-access (SFH/CDMA) is proposed for a cellular communication system. In contrast to conventional FH, which uses a channel state independent hopping sequence, a transmitter in the proposed scheme hops to an available frequency subband with the highest transmission gain. It is shown that the proposed scheme can offer large performance gains over the conventional FH scheme.

The problem of allocating radio resources in the downlink of a CDMA network is studied. The modulation and coding schemes, numbers of multicodes, and transmit powers used for all mobile stations (MS's) are jointly chosen so as to maximize the total transmission bit rate, subject to certain constraints. Based on the discrete and nonlinear nature of the proposed model, a mixed-integer nonlinear programming optimization problem is formulated. It is shown that the nonlinear relationship between bit rate and transmit power due to the use of different modulation and coding schemes and the maximum number of multicodes which can be assigned to an MS generally result in an optimal allocation which involves simultaneous transmission to several MS's. In addition, it is shown that the optimal scheduler can yield a significant improvement in throughput by taking the MS traffic loads into account.

This paper examines the bit error rate (BER) performance of a frequency-hopping multicarrier code division multiple-access (FH-MC-CDMA) system in frequency-selective slow fading channels. The performance of FH-MC-CDMA is compared to that of MC-CDMA on an uplink with a tapped delay line (TDL) correlated channel model. FH-MC-CDMA with performance enhancement techniques is proposed as an access technique for future generation broadband wireless networks. It is found that FH-MC-CDMA generally has a much better performance than conventional frequency hopping. In addition, the performance of FH-MC-CDMA with a much smaller number of subcarriers may outperform that of MC-CDMA.

Current wireless multimedia applications may require different quality of service (QoS) measures such as throughput, packet loss rate, delay, and delay jitter. In this paper, we propose a transmission rate scheduling scheme for code division multiple access (CDMA) networks that can provide absolute QoS guarantees. The scheduling scheme uses several M/D/1 queues, each representing a different service class, and allocates a transmission rate to each queue so as to satisfy the different QoS requirements. In addition, the scheduling scheme provides relative differentiated services among the different service classes so that the network operator is able to tune QoS ratios between these classes independent of the class loads. An optimization problem is proposed that maximizes the transmission rate allocations among mobile stations (MSs). Analysis and simulation results are used to illustrate the viability of the scheduling scheme. Such a scheme can be applied in the emerging DS (differentiated services) Internet.

An expression for the bit-error rate (BER) of noncoherent frequency-shift keying with a nonfaded desired signal in the presence of N Rayleigh-faded unsynchronized cochannel interferers (UCCIs) and additive white Gaussian noise is first derived. This result can be used to obtain the BER for a faded desired signal. For a large number of UCCIs, numerical evaluation of this expression can be quite time-consuming. An approximate method that yields fairly accurate results is thus described. Numerical results show that for a Rician-faded desired signal with a strong specular component in an interference-limited environment, the BER decreases slightly with N whereas for a Rayleigh-faded desired signal, the BER varies very little with N. A comparison to the BER performance with synchronized cochannel interferers is also provided.

The problem of optimally detecting a desired BPSK modulated bit in the presence of another bit unsynchronized BPSK modulated interfering signal and additive white Gaussian noise is considered. Given certain information about the desired and interferer signals, the minimum achievable bit error rates for two types of receiver models are obtained and compared with that for a conventional matched filter. It is found that knowledge of the interferer signal's carrier phase is important when the signal-to-interference ratio is low. The effect on bit error rate of a frequency offset between the desired and interferer signals is also studied. Finally, performance sensitivity to errors in estimating the interferer signal's carrier phase and the bit timing difference between the desired and interferer signals is examined.

In this paper, a model is developed for a slotted time-division multiplexing system used for assigning transmissions over a set of time-varying channels. Two schemes, a "round-robin" and an adaptive multiplexing scheme, are studied for use with three standard automatic-repeat-request (ARQ) protocols, namely, stop-and-wait (SW), go-back-N (GBN), and ideal selective repeat (ISR). In round-robin multiplexing, transmission slots are assigned periodically to each channel, With the adaptive scheme, the multiplexer selects at each time slot the channel whose state is estimated to have the lowest retransmission probability. The throughputs of these ARQ protocols under either multiplexing scheme are analyzed and compared. The present paper contains a description of the system model and an analysis of the ARQ protocols under both multiplexing schemes. In Part II, a modification to GBN and selective repeat ARQ for reducing the detrimental effects of feedback errors is discussed and analyzed, followed by a throughput performance comparison of all ARQ and multiplexing schemes.

For pt.I see ibid., vol.46, no.1, p.41-51, 1998. A "postponed retransmission" (PR) modification to both go-back-N (GBN) and ideal selective repeat (ISR) automatic-repeat-request (ARQ) is discussed and analyzed. A scheme for reducing the number of states involved in the calculation for multiplexed GBN ARQ is also presented. Numerical results are then shown for stop-and-wait (SW), GBN, and selective repeat (SR) ARQ (as well as the PR modified versions), under both round-robin and adaptive multiplexing.

Automatic-repeat-request (ARQ) protocols have been analyzed for quite some time, but the issue of errors in the feedback channel has not received much attention. In some applications, such as digital mobile communications, this issue can be important. Accordingly, this paper examines the effect of feedback errors on the throughputs of the stop-and-wait (SW), go-back-N (GBN), and selective repeat (SR) ARQ protocols for the ease of a point-to-point channel under some feedback information assumptions. It is shown that the deleterious effects of feedback errors on the throughputs of continuous (e.g., GBN and SR) ARQ protocols can be greatly reduced by a simple modification in the retransmission operation, provided that the "complete state" of the receiver is sent back with each acknowledgment.

This paper describes a method which uses "derangement" permutations as scrambling keys in a frequency-domain speech scrambling system. Subjective tests based on the number test confirm that such keys produce scrambled speech with virtually no residual intelligibility. A new derangement generation algorithm is introduced for key generation. This algorithm maps each integer g, in the range of 0/spl les/g<(n-1)!, to a distinct derangement of 2 elements,.

A number of authors have examined the problem of determining the codeword error probability, P/sub e/, in an asynchronous frequency-hopped spread spectrum multiple access (FH-SSMA) communication system. In this letter, we present a method for determining P/sub e/ in a time-slotted system which incorporates a guard time and study the effect of the guard time on P/sub e/ and some other performance measures.

It has been shown by Kasami and Lin (see IEEE Trans. Commun., vol.32, p.998, 1984) that (n,k) Reed-Solomon codes used over a q-ary symmetric channel are proper. In this correspondence, it is shown that the binary expansions of these codes and their extensions, when used on the binary-symmetric channel, are not necessarily proper. In particular, certain codes of rate less than [1-log/sub 2/m+{(m-1)/m}log/sub 2/(m-1)] where m=log/sub 2/ q are not proper.

At every hop in a conventional frequency-hopped spread spectrum multiple-access (FH-SSMA) system, each transmitter sends its data using a single frequency bin. In this paper, a scheme in which several frequency bins are simultaneously used by a transmitter is studied. It is found that such a code diversity scheme can yield a significant reduction in symbol error rate over that of a conventional FH-SSMA system. A priority system can also be implemented by assigning different diversity degrees to the transmitters.

Two methods for computing the probability of undetected error on the Gilbert (1960) channel are examined. First, using a method proposed by Kittel (1978), we study some standard cyclic redundancy codes and compare the results with those on the binary symmetric channel. Then we consider a general method of approximate code evaluation, proposed by Elliott, which involves P(m, n), the probability of m errors in a block of length n bits. A nonrecursive technique for computing P(m, n) on the Gilbert channel is described.

Six hard-decision combining schemes are considered. The probability of decoding failure P/sub F/ for the first two schemes can be easily obtained. For Scheme 3, an analytic expression for P/sub F/ is derived and for Schemes 4 to 6, a procedure is described which allows P/sub F/ to be evaluated numerically. A performance comparison of the six schemes is given.>

For pt.I see ibid., vol.39, no.5, p.783-93 (1991). The performance of an orthogonal frequency-division multiplexing (OFDM)/frequency modulation (FM) system for data communication over Rayleigh-fading mobile radio channels was analyzed in pt.I. The effects of forward error correction, switching diversity, automatic gain control (AGC), and squelch are studied. It is shown that OFDM/FM works well with switching diversity because OFDM can average out the transients created by switching between antennas. It is also found that the independent error assumption can be used to predict the distribution of the number of errors in a word. The use of squelch produced a small (about 1 dB) performance improvement, whereas the use of AGC provided negligible improvement.>

Several modifications of an efficient automatic repeat request (ARQ) scheme proposed by Weldon (see IEEE Trans. Commun., vol.COM-30, p.480, 1982) are studied. Unlike Weldon's scheme, in which all erroneous data packets are discarded, the present schemes make use of copies of the data packet which may contain errors. A number of channel models are considered, namely, a binary symmetric channel, a nonfading, and a Rayleigh fading channel with additive white Gaussian noise. In most cases, it is found that the throughput can be substantially increased. Under poor channel conditions, the use of forward error correction can lead to further improvement. A type-II ARQ scheme which does not suffer the throughput degradation under good channel conditions due to overhead parity bits associated with conventional forward error correction is also analyzed.>

The probability q/sub i/ of successful reception in a nonfading mobile radio channel with i contending mobiles transmitting to a central base station is studied for a number of different capture and spatial distribution models. It is shown that a generalized capture model can be used to estimate q/sub i/'s for a simplified example system which uses noncoherent frequency shift keying modulation. This model can be applied to other systems as well. An example of the use of the q/sub i/'s in the throughput evaluation of a finite population slotted ALOHA system is given. In most practical systems, the mobiles cannot get arbitrarily close to the base station. The effect of this constraint on q/sub i/ is examined. Finally, the dependence of the capture probability for a test mobile on its distance from the base station is obtained.>

The probability of q/sub i/ of successful packet reception when i users transmit simultaneously in a mobile packet radio system is shown to decrease monotonically with i for a number of commonly used capture and spatial distribution models, with no fading. Examples of both noiseless and noisy systems in which q/sub i/ is not monotonically decreasing with i are also given.>

The performance of OFDM/FM modulation for digital communication over Rayleigh-fading mobile radio channels is described. The use of orthogonal frequency division multiplexing (OFDM) over mobile radio channels was proposed by Cimini (1985). OFDM transmits blocks of bits in parallel and reduces the bit error rate (BER) by averaging the effects of fading over the bits in the block. OFDM/FM is a modulation technique in which the OFDM baseband signal is used to modulate an FM transmitter. OFDM/FM can be implemented simply and inexpensively by retrofitting existing FM communication systems. Expressions are derived for the BER and word error rate (WER) within a block when each subchannel is QAM-modulated. Several numerical methods are developed to evaluate the overall BER and WER. An experimental OFDM/FM system was implemented and tested using unmodified VHF FM radio equipment and a fading channel simulator. The BER and WER results obtained from the hardware measurements agree closely with the numerical results.>

To improve performance on the inbound (mobile-to-base-station) channel of a packet radio system consisting of a base station and a number of mobile users, the area around the base station is divided into M sectors. Signals originating from users in different sectors are received by different directional antennas at the base station. It is shown that, if the number of receivers at the base station is less than M, the selection of the antennas to be connected to the receivers becomes an issue. A number of antenna selection schemes are compared for three different channel models, assuming an ideal antenna pattern. It is found that the scheme that selects the antennas with the largest received signal powers is nearly optimum. The effects of a more practical nonideal antenna pattern are discussed.>

The probability of undetected error P/sub u/( epsilon ) for the primitive triple-error-correcting BCH codes of blocklength 2/sup m/-1 used solely for error detection on a binary symmetric channel with crossover probability epsilon>

The optimal partial decision combiner is derived, and its performance is analyzed and compared to majority voting and selection diversity using binary noncoherent frequency-shift keying on a Rayleigh-faded additive white Gaussian noise channel. The degradation in the probability of bit error is evaluated when the channel state estimate is imperfect due to parameter quantization, AWGN, and cochannel interference. It is demonstrated that the optimal partial decision combiner can provide a simple means for improving performance on channels with limited cochannel interference.>

A family of tests for improper codes is given. These tests can be used in cases where the complete weight distribution of the code is unknown. It was found that knowledge of the number of minimum weight codewords can be used to greatly increase the effectiveness of the asymptotic Varshamov-Gilbert test. Further improvement is possible as more is known about the number of other weight codewords.>

A number of digital techniques for detecting binary antipodal signals are based on examining the polarities of the received signal samples and ignoring their amplitudes. The structure of the optimum detector for the hard-limited samples is derived, and its performance is compared to those of some commonly used schemes in impulsive as well as Gaussian noise environments. The optimum receiver forM-ary signaling based on received signal samples quantized to an arbitrary number of levels is obtained and compared to other detectors.

An automatic repeat-request (ARQ) scheme with memory and soft error detectors has been recently proposed by Benelli. Its performance was studied mainly through computer simulation. In this paper, a generalized version of this ARQ scheme is examined. The selection of certain thresholds and weights to minimize the bit error rate in systems using a fixed number of packet repeats is considered. Finally, the evaluation of the average number of transmissions per packet in systems in which negatively acknowledged packets are retransmitted until successfully received is described.

The penalties incurred in using the sample-and-sum, weighted partial decision, and binary Partial decision detectors are analyzed. Even though these schemes are inferior to the digital matched filter detector, they could be used in systems with more modest computational capabilities. Analytic expressions are obtained for the losses when the number of samplesMis large. Examples are also given which indicate how these losses vary withM.

Many data communication systems make use of cyclic redundancy check (CRC) codes for error detection purposes. In this paper, an asymptotic result concerning the undetected error probabilityP(\epsilon)of CRC codes is derived. TheP(\epsilon)'s of a number of CRC codes which have been adopted as international standards are also examined.

In order to maximize the throughput in a new ARQ strategy proposed by Weldon, a number of parameters need to be selected optimally. An efficient method for choosing these parameters is obtained by exploiting the form of a simplified expression for the throughput. It is also shown that for noisy, long delay channels, the throughput of the Weldon scheme can be increased by sending multiple copies of each new data block.

In this paper, the calculation of the probabilityP_{f}(M, N)of more thanMbit errors in a block ofNbits transmitted over a Rayleigh fading channel using noncoherent frequency shift keying is considered. Accurate approximations toP_{f}(M, N)are derived under the assumption of very slow fading. Approximations are also given when a selection diversity system is used. An application of the results to the calculation of the block error rate when the assumption of very slow fading is relaxed is then discussed.

In this paper, an expression for the undetected error probability(P\epsilon)of single parity-check product (SPCP) codes used for error detection over a binary symmetric channel is derived. It is shown that square SPCP codes need not obey a certain commonly used bound. Approximate expressions for the maximum(P\epsilon)and the corresponding maximizing ε are given.

This paper considers the choice of the number of diversity branches for minimizing the bit error rate of a selection diversity system using noncoherent binary frequency-shift keying modulation for transmission over a Rayleigh fading Gaussian channel. An exact expression for the optimum number of branches,L_{sel}^{\ast}, is given. The resulting probability of bit error decreases exponentially with the square root of the energy per bit.

This paper examines the use of BCH error-correcting codes in improving the performance of a stop-and-wait automatic repeat-request (ARQ) scheme over random error and Rayleigh fading channels. Two models are analyzed. The first model considers the effect of forward error correction on the mean wasted time per message. The second model assumes a Poisson arrival process for the messages and examines the effect of forward error correction on the mean time between the arrival of a message and its successful transmission. In both models, our results indicate that the performance of the ARQ scheme can be substantially improved by the use of forward error correction.

An achievable rate regionR_{1} \leq I(X_{1};Y|X_{2},U), R_{2} \leq I(X_{2}; Y|X_{1},U), R_{1}+R_{2} \leq I(X_{1}, X_{2};Y), wherep(u,x_{l},x_{2},y)= p(u)p(x_{l}|u)p(x_{2}|u)p(y|x_{l},x_{2}), is established for the multiple-access channel with feedback. Time sharing of these achievable rates yields the rate region of this paper. This region generally exceeds the achievable rate region without feedback and exceeds the rate point found by Gaarder and Wolf for the binary erasure multiple-access channel with feedback. The presence of feedback allows the independent transmitters to understand each other's intended transmissions before the receiver has sufficient information to achieve the desired decoding. This allows the transmitters to cooperate in the transmission of information that resolves the residual uncertainty of the receiver. At the same time, independent information from the transmitters is superimposed on the cooperative correction information. The proof involves list codes and block Markov encoding.