Large-batch Contrastive Learning (CL), the foundation of modern representation learning, is fundamentally incompatible with the volatile resource constraints of edge devices. This conflict creates a dilemma: small on-device batches degrade model fidelity, while offloading to the cloud incurs unacceptable latency and bandwidth costs. Existing solutions often resort to static model compression, which fails to adapt to the runtime volatility of edge environments. To bridge this gap, we present StreamSplit, a novel framework that makes streaming CL practical across heterogeneous ARM client platforms. StreamSplit resolves the conflict between the continuous nature of ambient audio and the discrete batch requirements of models like CLAP and COLA. We introduce: (1) A distribution-based streaming framework that decouples representation quality from local batch size, using a tractable Hybrid Loss to maintain fidelity despite sparse updates; and (2) An Uncertainty-Guided Adaptive Splitter that uses a lightweight Reinforcement Learning (RL) policy to dynamically partition computation. Uniquely, this policy integrates real-time resource monitoring with embedding ambiguity to optimize the accuracy-latency trade-off on the fly. We evaluate StreamSplit on diverse hardware, from the resource-constrained Raspberry Pi 4 to the high-performance Apple M2. Results demonstrate that StreamSplit reduces per-sample latency by up to 4.7× and cuts bandwidth by 77.1% and energy by 52.3% compared to server-centric baselines. Crucially, it maintains accuracy within 2.2% of server-centric models, proving that adaptive, distributed learning is a viable path for the modern edge ecosystem.

Abstract In recent years, spatio-temporal graph neural networks (GNNs) have successfully been used to improve traffic prediction by modeling intricate spatio-temporal dependencies in irregular traffic networks. However, these approaches may not capture the intrinsic properties of traffic data and can suffer from overfitting due to their local nature. This paper introduces the Implicit Sensing Self-Supervised learning model (ISSS), which leverages a multi-pretext task framework for traffic flow prediction. By transforming data into an alternative feature space, ISSS effectively captures both specific and general representations through self-supervised tasks, including contrastive learning and spatial jigsaw puzzles. This enhancement promotes a deeper understanding of traffic features, improved regularization, and more accurate representations. Comparative experiments on six datasets demonstrate the effectiveness of ISSS in learning general and discriminative features in both supervised and unsupervised modes. ISSS outperforms existing models, demonstrating its capabilities in improving traffic flow predictions while addressing challenges associated with local operations and overfitting. Comprehensive evaluations across various traffic prediction datasets, have established the validity of the proposed approach. Unsupervised learning scenarios have shown the improvements in RMSE for the METR-LA and PEMSBAY datasets of 0.39 and 0.35 for location-dependent and location-independent tasks, respectively. In supervised learning scenarios, for the same datasets, the improvements were 1.16 for location-dependent tasks and 0.55 for location-independent tasks.

Mobile Crowd Computing (MCC) leverages the collaborative power of nearby devices to solve resource-intensive tasks, offering transformative potential across various fields. However, ensuring device reliability—which directly impacts the trustworthiness of devices in MCC environments—poses a significant challenge. Balancing validation accuracy with performance and energy efficiency is particularly difficult due to MCC’s dynamic and resource-constrained decentralized nature. Existing validation methods are not feasible in MCC, as they can negatively affect speed and energy consumption. This paper introduces the Honeybee-RS framework, a novel approach for validating offloaded computational results in MCC environments. Honeybee-RS provides delegator-based validation mechanisms and derives reliability scores from the validation process. Experiments demonstrate the effectiveness of these mechanisms, significantly enhancing reliability in MCC while maintaining performance.

Federated learning (FL) has been envisioned to enable many Internet of Things (IoT) devices to perform large-scale machine learning without sharing raw data, resulting in significant privacy improvements. In a wireless IoT system, FL helps clients to secure their confidential information and achieve improved learning performance. However, the conventional FL architecture is vulnerable to Byzantine workers, possessing the potential to send malicious updates that compromise the accuracy of the global model. Previous studies have proposed various secure aggregation rules and attacker detection techniques to address this issue. However, these techniques exhibit limited effectiveness and may lead to a decrease in accuracy. To overcome these limitations, we propose a Byzantine client detection algorithm called FedXPro by combining the PC/BC-DIM neural network and Geometric Median (GM). Predictive coding (PC) is the core of the PC/BC-DIM architecture, which can perform Bayesian inference by fusing priors and likelihoods to determine posterior distributions. The GM is employed to determine the prior knowledge of legitimate clients to execute the PC/BC-DIM algorithm. During training, the framework calculates the probability distribution for a set of valid clients chosen from the GM. In testing, it attempts to reconstruct the same distribution from other clients concerning prior knowledge, and ultimately, the reconstruction power is utilized to filter the malicious clients. Our extensive simulations demonstrate the superiority of our FedXPro approach over other state-of-the-art methods in terms of accuracy, a guaranteed faster convergence rate, and attack detection under different network settings.

Diffuse waste data and associated privacy concerns present significant challenges for effective waste classification in the Internet of Things (IoT) realm. This research introduces a novel approach that leverages differential privacy (DP) and federated transfer learning (FTL) to address the issues, enabling waste classification while preserving privacy within the IoT ecosystem. By integrating Federated Learning (FL), Transfer Learning (TL), and DP, our proposed method facilitates collaborative training while ensuring data privacy. In this methodology, a pre-trained model, initially trained on the ImageNet dataset, is disseminated to IoT devices. Subsequently, these devices perform local training using the TrashNet and Garbage Classification datasets. This process allows devices to capture waste characteristics unique to their individual environments. Through the fusion of general knowledge pertaining to the trained model and local insights, the proposed approach achieves efficient waste classification. The study critically examines the implications for privacy, biases resulting from limited local data, and trade-offs between privacy and model performance. The experimental evaluation demonstrates the effectiveness of the approach and underscores the importance of ensuring privacy-sensitive waste classification. This research contributes to the discourse on FTL and encourages further research into privacy-preserving waste classification within the IoT.

Deep learning (DL) has become a powerful tool in many areas of research and industry, ranging from computer vision to natural language processing. Nonetheless, as DL models are trained on large amounts of sensitive data, concerns about data privacy have emerged. In light of this, differential privacy (DP) has emerged as a promising technique that provides strong privacy guarantees while allowing useful information to be extracted from the data. DP involves adding random noise to the training data or model parameters, which makes it difficult for an attacker to identify the contribution of any single data point to the final model. Despite the promising results, DP can significantly degrade the performance of DL models, especially when dealing with large datasets or complex models. To improve the balance between privacy and utility, this paper proposes a novel modification to the vanilla DP algorithm that uses a Haar wavelet transform. The proposed method achieves better utility while maintaining the same ($\varepsilon, \delta$) privacy guarantees as vanilla DP algorithms. The paper provides an analytical demonstration of the improved noise variance bounds compared to previous methods. The paper also provides a detailed analysis of the convergence performance of the proposed algorithm and shows that the Haar wavelet transform improves the accuracy and efficiency of the training process. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art algorithms on four widely used scientific benchmark datasets making this a significant contribution to DP techniques’ practical applications in DL.

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.

Mobile devices are ubiquitous, heterogeneous and resource constrained. Execution of complex tasks in mobile devices are resource demanding and time-consuming, forcing developers to offload portions of the complex task to cloud or edge computing resources. Task offloading becomes increasingly challenging due to intermittent Internet connectivity, remote resource unavailability, high costs, latency, and limited energy of the mobile device. A mobile device user is typically surrounded by other mobile devices, which can be leveraged to collaboratively compute a resource-intensive task. With the help of a work sharing framework, it is feasible for devices to communicate and collaborate. However, some mobile devices are incapable of computing complex portions of the task, and some can compute in accelerated mode. In this demonstration, we introduce Honeybee-T a collaborative mobile crowd computing framework that uses a work-stealing algorithm. The algorithm allows work sharing with collaborating devices based on devices' computational ability and task-dependencies. The experiments show that by employing Honeybee-T framework, when compared to monolithic execution of a large compute-intensive task, there is a considerable performance gain, as well as energy savings.

In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features, such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, calledBEoTthat has been regarded as a promising enabler for future services and applications. In this article, we present a state-of-the-art review of recent developments in the BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home, and smart grid. Security challenges in the BEoT paradigm are also discussed and analyzed, with some key services, such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

The sixth-generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) toward a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space–air–ground–underwater communications, Terahertz communications, massive ultrareliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely, healthcare IoTs, Vehicular IoTs and Autonomous Driving, Unmanned Aerial Vehicles, Satellite IoTs, and Industrial IoTs. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.

COVID-19 has spread rapidly across the globe and become a deadly pandemic. Recently, many artificial intelligence-based approaches have been used for COVID-19 detection, but they often require public data sharing with cloud datacentres and thus remain privacy concerns. This paper proposes a new federated learning scheme, called FedGAN, to generate realistic COVID-19 images for facilitating privacy-enhanced COVID-19 detection with generative adversarial networks (GANs) in edge cloud computing. Particularly, we first propose a GAN where a discriminator and a generator based on convolutional neural networks (CNNs) at each edge-based medical institution alternatively are trained to mimic the real COVID-19 data distribution. Then, we propose a new federated learning solution which allows local GANs to collaborate and exchange learned parameters with a cloud server, aiming to enrich the global GAN model for generating realistic COVID-19 images without the need for sharing actual data. To enhance the privacy in federated COVID-19 data analytics, we integrate a differential privacy solution at each hospital institution. Moreover, we propose a new blockchain-based FedGAN framework for secure COVID-19 data analytics, by decentralizing the FL process with a new mining solution for low running latency. Simulations results demonstrate the superiority of our approach for COVID-19 detection over the state-of-the-art schemes.

In this paper, we study a new latency optimization problem for blockchain-based federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to handle both machine learning (ML) model training and block mining simultaneously. To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs. We then propose a new decentralized ML model aggregation solution at the edge layer based on a consensus mechanism to build a global ML model via peer-to-peer (P2P)-based blockchain communications. Blockchain builds trust among MDs and ESs to facilitate reliable ML model sharing and cooperative consensus formation, and enables rapid elimination of manipulated models caused by poisoning attacks. We formulate latency-aware BFL as an optimization aiming to minimize the system latency via joint consideration of the data offloading decisions, MDs’ transmit power, channel bandwidth allocation for MDs’ data offloading, MDs’ computational allocation, and hash power allocation. Given the mixed action space of discrete offloading and continuous allocation variables, we propose a novel deep reinforcement learning scheme with a parameterized advantage actor critic algorithm. We theoretically characterize the convergence properties of BFL in terms of the aggregation delay, mini-batch size, and number of P2P communication rounds. Our numerical evaluation demonstrates the superiority of our proposed scheme over baselines in terms of model training efficiency, convergence rate, system latency, and robustness against model poisoning attacks.

Mobile Edge Computing (MEC) has been envisioned as an emerging paradigm to handle the overwhelming explosion of mobile applications and services, by allowing edge devices (EDs) to offload their computationally-intensive tasks to heterogeneous MEC servers. Most of the existing works focus mostly on a centralized agent or an independent multi-agent setting which cannot work well in distributed edge networks with heterogeneous computation tasks. This paper considers a more realistic setting consisting of multiple cooperative EDs and multiple MEC servers in heterogeneous edge networks (HENs). We propose a new collaborative offloading framework in a HEN where each ED acts as an intelligent agent to make offloading decisions collaboratively, aiming to achieve the optimal system utility. To this end, we formulate the collaborative offloading problem as a Markov game which is then solved by a novel multi-agent deep reinforcement learning (MADRL) approach based on a multi-agent deep deterministic policy gradient (MA-DDPG) algorithm. Numerical simulations with real-life mobile wireless datasets show that the proposed cooperative multi-agent scheme can improve the system utility by 43.6% compared to the non-cooperative offloading schemes.

The combination of mobile edge computing (MEC) and blockchain is transforming the current computing services in Internet of Things networks, by offering task offloading solutions with security enhancement enabled by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme to optimize the system utility in blockchain-empowered MEC. Herein, each edge device (ED) not only handles data tasks but also deals with block mining which makes the system design and optimization highly complex. Therefore, we develop a novel cooperative deep reinforcement learning (DRL) approach which allows EDs to cooperatively offload their data tasks to the MEC server and perform block mining based on a Proof-of-Reputation consensus mechanism. Simulation results demonstrate that the proposed scheme significantly improves offloading utility, reduces blockchain mining latency, and achieves better system utility, compared to other non-cooperative and cooperative schemes.

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high overhead of raw data communications. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without directly exposing their underlying data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main issues in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions and the lessons learned along with the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

The healthcare industry has witnessed significant transformations in e-health services by using mobile-edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low Quality of Services (QoS), data privacy, and system security vulnerabilities. In this article, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data-sharing scheme, which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contract-based authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.

Effective monitoring of the progression of neurodegenerative conditions can be significantly improved by objective assessments. Clinical assessments of conditions such as Friedreich's Ataxia (FA), currently rely on subjective measures commonly practiced in clinics as well as the ability of the affected individual to perform conventional tests of the neurological examination. In this study, we propose an ataxia measuring device, in the form of a pressure canister capable of sensing certain kinetic and kinematic parameters of interest to quantify the impairment levels of participants particularly when engaged in an activity that is closely associated with daily living. In particular, the functional task of simulated drinking was utilised to capture characteristic features of disability manifestation in terms of diagnosis (separation of individuals with FA and controls) and severity assessment of individuals diagnosed with the debilitating condition of FA. Time and frequency domain analysis of these biomarkers enabled the classification of individuals with FA and control subjects to reach an accuracy of 98% and a correlation level reaching 96% with the clinical scores.

Blockchain and Mobile Edge Computing (MEC) are newly emerging technologies with great potential to revolutionize healthcare. This paper proposes a new decentralized healthcare architecture for distributed Electronic Medical Records (EMRs) sharing among federated hospitals based on blockchain and MEC. Unlike the existing schemes that often rely on a third-party for healthcare management, we focus on a fully decentralized access control solution by using smart contracts that enable EMRs access verification at the edge of the network without requiring any central authority. Moreover, a decentralized interplanetary file system (IPFS) platform is also integrated with smart contracts over the MEC network, which significantly reduces data retrieval latency and enhances security for EMRs sharing. The experimental results and analysis show the superior performance of the proposed scheme over the existing ones in terms of reduced data retrieval latency, enhanced blockchain performance, and security guarantees.

Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user's power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.

Quantitative identification of dysarthria plays a major role in the classification of its severity. This paper quantitatively analyses several components of cerebellar dysarthria. The methodology described in this study will be extended to other types of dysarthria via systematic analysis. The speech production model is characterized as a second-order single-input and single-output (SISO), linear, time-invariant (LTI) system in our study. A comparative study on the behavior of the damping ratio and resonant frequency for dysarthric and non-dysarthric subjects is presented. The results are further analyzed using the Principal component analysis (PCA) technique to emphasize the variation and uncover strong patterns in the selected features. The effects of some other related factors like decay time and Q-factor are also highlighted.

The aim of this research was to analyse the characteristic movements of patients with Parkinsons disease (PD) during the pull test. In this experiment, flexibility of participants were measured from two wearable sensors attached on their upper and lower back. In particular, as Bradykinesia and axial Bradykinesia are vital characteristics which are challenging to measure, we designed a test system engaging a minimal number of wearable sensors to capture the characteristic movements of the back. We utilised a time delay between two sensors to analyse rigidity of human back. In order to measure the characteristics of patient and control groups, the principal component analysis (PCA) was applied to extract the significant features to distinguish the two groups. Consequently, their differences were shown in PCA with a satisfactory separation of controls and patients.

Human actions have been widely studied for their potential application in various areas such as sports, pervasive patient monitoring, and rehabilitation. However, challenges still persist pertaining to determining the most useful ways to describe human actions at the sensor, then limb and complete action levels of representation and deriving important relations between these levels each involving their own atomic components. In this paper, we report on a motion encoder developed for the sensor level based on the need to distinguish between the shape of the sensor's trajectory and its temporal characteristics during execution. This distinction is critical as it provides a different encoding scheme than the usual velocity and acceleration measures which confound these two attributes of any motion. At the same time, we eliminate noise from sensors by comparing temporal and spatial indexing schemes and a number of optimal filtering models for robust encoding. Results demonstrate the benefits of spatial indexing and separating the shape and dynamics of a motion, as well as its ability to decompose complex motions into several atomic ones. Finally, we discuss how this specific type of sensor encoder bears on the derivation of limb and complete action descriptions.

This letter looks at the theoretical conditions underpinning unique localization of an emitter using Time-Delay-of-Arrival(TDoA) from minimum number of sensors in 2-D and 3-D space. A discussion is carried out on the unique localization region with the TDoA measurements subjected to a bounded error. For both 2-D and 3-D, error bounds have been found, beyond which, there is no existence of the unique solution region.

Dynamic variations in channel behavior is considered in transmission power control design for cellular radio systems. It is well known that power control increases system capacity, improves Quality of Service (QoS), and reduces multiuser interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss dynamics of the fading channel is presented. Formulating power control decisions based on the measured received power levels allows modeling the fading channel pathloss dynamics in terms of a Hidden Markov Model (HMM). Applying the online HMM identification algorithm enables accurate estimation of the real pathloss ensuring efficient performance of the suggested power control scheme.

Tracking a target from a video stream (or a sequence of image frames) involves nonlinear measurements in Cartesian coordinates. However, the target dynamics, modeled in Cartesian coordinates, result in a linear system. We present a robust linear filter based on an analytical nonlinear to linear measurement conversion algorithm. Using ideas from robust control theory, a rigorous theoretical analysis is given which guarantees that the state estimation error for the filter is bounded, i.e., a measure against filter divergence is obtained. In fact, an ellipsoidal set-valued estimate is obtained which is guaranteed to contain the true target location with an arbitrarily high probability. The algorithm is particularly suited to visual surveillance and tracking applications involving targets moving on a plane.

This paper investigates the problem of localizing a wireless transmitter using a minimum number of receivers and other readily available means in a time difference of arrival (TDOA) setting. Using the necessary and sufficient conditions for unique solution, we use power measurements to compensate for the reduced number of receivers. In other words, if the transmitter is not located in the unique solution area, we provides a technique to find the true location via the measured received signal power. Our approach neither requires the knowledge of the transmission power nor the path loss exponent.

This paper investigates the problem of estimating the location and velocity of a mobile agent using the received signal strength (RSS) measurements. Typical power measurements are inherently nonlinear and in this approach we derive a linear measurement scheme using an analytical measurement conversion technique which can readily be used with RSS measuring sensors. Power measurements are hence used in our robust version of a linear Kalman filter to estimate the dynamic parameters of the moving transmitter.

Fully-connected mesh networks that can potentially be employed in a range of applications, are inherently associated with major deficiencies in interference management and network capacity improvement. The tree-connected (routing based) mesh networks used in today's applications have major deficiencies in routing delays and reconfiguration delays in the implementation stage. This paper introduces a CDMA based fully-connected mesh network, which controls the transmission powers of the nodes in order to ensure that the communication channels remain interference-free and minimizes the energy consumption. Moreover, the bounds for the number of nodes and the spatial configuration are provided to ensures that the communication link satisfies the QoS (Quality of Service) requirements at all times.

The paper presents a new fully distributed uplink power control method for CDMA systems. The power control algorithm calculates explicitly and assigns directly the desired mobile transmit powers achieving both maximum carrier-to-interference ratio at the base station and minimum mobile energy consumption. Compared with the commonly known iterative power control algorithms, the direct assignment method is easier to implement and more power efficient.

The paper presents a framework to design robust transmit power controllers in cellular radio systems. The robust controllers designed are able to guarantee the quality of service (QoS) by keeping the carrier-to-inference-plus-noise ratio (CIRN) above a desired level in face of network link gain variations. The controller design problem is solved by solving a noncooperative dynamic game between the controller and unknown link gain variations.

In this letter, we provide a robust version of a linear Kalman filter for target tracking based on a measurement conversion technique on the nonlinear radar measurements. We prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement-based linear filtering.

In this paper we develop a robust method of target/mobile agent tracking involving two independent estimators with separate measurement systems. The outputs of the two estimators are combined using simple trigonometry (post-estimation data fusion) and provide a robust and reliable tracking path. We demonstrate that through the use of recent advances in robust set-value state estimation, our robust parallel filter approach performs well even when the individual filters do not. Brief comparisons with common data fusion methods are conducted in order to demonstrate the advantages of our parallel (post-estimation fusion) approach

In this paper we discuss the ghost node problem found when triangulation of 2 or more nodes is required. We present and discuss a simple algorithm, termed ABLE (Angle Based Location Estimation), that will position randomly placed emitters in a wireless sensor network using a mobile antenna array. The individual nodes in the network are relieved of the localization task by the mobile antenna system and require no modifications to account for location determination. Furthermore, no beacon nodes (i.e. nodes that know their own position) are required. We provide analysis that indicates a reasonably small number of measurements are required to guarantee the successful localization of the emitting nodes and demonstrate our results through simulation.

We present a novel scheme for node localization in a delay-tolerant sensor network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a robust extended Kalman filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1 m in a large indoor setting.

Localization of small wireless sensor devices, with the deployment of the minimal infrastructure or hardware, has been the topic of significant research over the past few years. We have developed the Robust Extended Kalman Filter (REKF) localization system [1], which enables a mobile, data gathering robot to localize static sensor devices, by combining the RSSI data received from the motes, with estimates of its trajectory. The REKF localization system is particularly well suited to delay-tolerant sensor networks, where node positions need not be known in real time. We have observed accuracies ranging from approximately 30cm to 1m in practice.

This paper provides mobility estimation and prediction for a variant of GSM network which resembles an adhoc wireless mobile network where base stations and users are both mobile. We propose using Robust Extended Kalman Filter (REKF)as a location heading altitude estimator of mobile user for next node (mobile-base station)in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm can successfully track the mobile users with less system complexity as it requires either one or two closest mobile-basestation measurements. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.