Real-time anomaly detection in Open Radio Access Networks (O-RAN) demands high accuracy, low false alarms, and computational efficiency for resource-constrained edge deployment. Traditional methods struggle with computational overhead, inconsistent cross-domain performance, and suboptimal feature representations that miss subtle attacks on O-RAN's open interfaces. We present CoMeT-Net (Consensus Memory Template Network), a framework achieving state-of-the-art detection through three innovations: (1) structured memory banks enabling template-based consensus voting with $O(N \cdot C)$ complexity; (2) adaptive gating that downweights ambiguous features as a learned noise filter; (3) contrastive alignment unifying feature learning and classification. Deployed in O-RAN infrastructure via edge servers and Near-RT RIC xApp, CoMeT-Net enables dynamic threat mitigation through PRB throttling and RRC connection release. On network traffic datasets, CoMeT-Net achieves 99.35% F1 score with 10$\times$ lower false alarm rates than baselines while maintaining 0.3-3ms inference across hardware tiers from servers to Raspberry Pi 4. O-RAN testbed validation demonstrates effective isolation, degrading attacker latency to >1400ms while preserving 15-20ms for legitimate users.

The extreme and uncertain underwater environment presents significant challenges in achieving reliable acoustic communications, mainly due to limited bandwidth and time variability. To address these challenges, this work proposes a novel multiple-access technique for downlink multiuser networks, called Orthogonal Signal Division Multiple Access (OSDMA), which enables simultaneous transmission of different data streams from a single transmitter to the corresponding receivers/users by modulating the streams with an orthogonal Inverse Discrete Fourier Transform (IDFT) matrix. In a low-noise environment, each receiver/user can accurately estimate the channel and demodulate the data. Emulations and simulations are conducted based on at-sea channel measurements. The results show that OSDMA offers greater robustness against underwater time-varying channels than Orthogonal Frequency Division Multiple Access (OFDMA) and Non-Orthogonal Multiple Access (NOMA).

Large Language Models (LLMs) have demonstrated remarkable abilities in various language tasks, making them promising candidates for decision-making in robotics. Inspired by Hierarchical Reinforcement Learning (HRL), we propose Retrieval-Augmented Hierarchical in-context reinforcement Learning (RAHL), a novel framework that decomposes complex tasks into sub-tasks using an LLM-based high-level policy, in which a complex task is decomposed into sub-tasks by a high-level policy on-the-fly. The sub-tasks, defined by goals, are assigned to the low-level policy to complete. To improve the agent's performance in multi-episode execution, we propose Hindsight Modular Reflection (HMR), where, instead of reflecting on the full trajectory, we let the agent reflect on shorter sub-trajectories using intermediate goals to improve reflection efficiency. We evaluated the decision-making ability of the proposed RAHL in three benchmark environments, ALFWorld, Webshop, and HotpotQA, where the results show that RAHL can achieve an improvement in the performance of, respectively, 9%, 42%, and 10% in 5 execution episodes compared to state-of-the-art baselines. We also implemented RAHL on the Boston Dynamics SPOT robot, which is shown to effectively scan the environment, find entrances, and navigate to new rooms controlled by the LLM policy.

Previous studies on cooperative perception often assumed homogeneous sensors across vehicles, which is unrealistic due to the incremental deployment of technology and vehicle budgets. Heterogeneous sensors pose challenges, as they generate raw data with different modalities, resolutions, locations, and orientations. We propose a Location-Aware and Modality-Aware Data Fusion architecture (LAMAF) for heterogeneous data fusion in distributed cooperative perception. LAMAF’s Reduce Layer utilizes latent Bird’s Eye View (BEV) coordinates encoding and modality embedding to capture spatial dependencies and extract input data modality. Experiments on the Vehicle-to-Vehicle (V2V) perception dataset, OPV2V, demonstrate LAMAF’s outstanding performance and generalizability to different downstream tasks. Furthermore, we introduce the first configurable Communication-In-the-Loop (CommIL) V2V dataset, which significantly reduces the gap between real-world environments and cooperative V2V datasets, allowing researchers to develop cooperative V2V-based frameworks while considering realistic communication constraints. The dataset generation code and pre-generated data are available at: CommIL-Dataset.

Recent advances in segmentation models such as the Segment Anything Model (SAM) have made it possible to generate high-quality masks for arbitrary regions in an image. However, these models lack semantic reasoning and cannot assign meaningful labels without predefined class prompts. Similarly, models like YOLO-Seg are restricted to fixed vocabularies, and CLIP-based segmentors struggle with abstract or hierarchical concepts. In this paper, we propose a Large Vision-Language Model (LVLM)-assisted information-rich semantic segmentation framework that bridges this gap by integrating SAM with open-vocabulary segmentation and multimodal language understanding. Our approach begins by generating precise masks using SAM and querying an LVLM to extract semantically relevant object names via carefully designed prompts. We then use a CLIP-based segmentor to produce rough masks corresponding to the object list and apply a voting-based mechanism to align rough and fine masks, enriching the final output with detailed and hierarchically organized semantic information. Through extensive evaluations on the COCO 2017 dataset, we demonstrate that our method not only outperforms traditional models in abstract and implicit labeling tasks but also provides semantically rich outputs that support human-in-the-loop and open-world applications.

Open Radio Access Network (O-RAN) is expected to support a diverse range of cutting-edge, real-time, and heterogeneous dynamic traffic demands. In response, we formulate a network-slicing resource allocation optimization problem to enhance the Quality of Service (QoS) in O-RAN. The proposed problem is formulated as a Nonlinear Programming (NLP) problem, designed to efficiently meet User Equipment (UE) traffic demands in a dynamic environment while maintaining QoS and optimizing network radio resources. Given the combinatorial nature of this problem, finding an optimal solution is challenging and often impractical in a dynamic traffic environment. To address this, we propose decomposing the prime problem into two sub-problems: a Long-Term Resource Allocation (LTRA) problem focusing on resource allocation decisions, and a Short-Term Resource Scheduling (STRS) problem that manages the scheduling of demanded resources. We propose the Long Short-Term Memory Actor-Critic-based (LS-RLSlice) algorithm, a novel approach that modifies LSTM and Deep Deterministic Policy Gradient (DDPG) algorithms for efficiently solving LTRA and STRS. We perform simulations to show that our proposed algorithm outperforms state-of-the-art schemes and significantly reduces the utilization of network resources. Finally, our proposed solution is validated using the real-world POWDER testbed supporting the O-RAN stack and provides configuration setups for Non-Real-Time and Near-Real-Time RAN Intelligent Controllers (Non-RT RIC and Near-RT RIC).

Next-Generation Radio Access Networks (NG-RAN) aim to support diverse vertical applications with strict security, latency, and Service-Level Agreement (SLA) requirements. These demands introduce challenges in securing the infrastructure, allocating resources dynamically, and enabling real-time reconfiguration. This demo presents SnSRIC, a secure and intelligent network slicing framework that mitigates a range of Distributed Denial-of-Service (DDoS) attacks in Open RAN environments. SnSRIC incorporates an AI-driven xApp that dynamically allocates Physical Resource Blocks (PRBs) to active users while enforcing slice-level security. The system detects anomalous behavior, distinguishes between benign and malicious devices, and uses the E2 interface to throttle rogue signaling while maintaining service continuity for legitimate users.

To better enhance the network service for different user devices in various scenarios, autonomous aerial vehicles (AAVs) are increasingly used as aerial base stations (ABSs). However, optimizing coverage for user devices via AAV team control is an NP-hard problem and escalates exponentially in complexity with the growing number of user devices. To address this challenge, researchers have turned to reinforcement learning (RL) for a more practical solution. With the growing prevalence of the Internet of Things (IoT), the diversity of user devices increases, posing challenges for traditional RL, as 1) the spatial distribution of devices becomes more complex; 2) variations in device types and device mobility increase the training latency; 3) the high-speed movement of IoT devices can lead to performance deterioration in widely used RL algorithms with discrete action space; and 4) traditional RL struggles to adapt to new environments. To solve these problems, we propose a new meta-RL framework, Meta-RL with explicit task inference (Meta-ETI). Then, we apply this framework to efficiently train an energy-efficient AAV control policy for fair and effective coverage in 3-D dynamic environments. Meta-ETI is evaluated in both theoretical and application-related aspects and demonstrates superior performance compared to the baseline frameworks. The result shows that Meta-ETI demonstrates 2–3 times faster adaptation speed and a decent performance in sample efficiency. Furthermore, in the AAV-IoT coverage application, Meta-ETI shows 30%–50% better in energy efficiency and 40%–60% more served devices because of the fair coverage.

Federated learning (FL) has been popular recently as a framework for training machine learning (ML) models in a distributed and privacy-preserving manner. Traditional FL frameworks often struggle with model and statistical heterogeneity among participating clients, impacting learning performance and practicality. To overcome these fundamental limitations, we introduce Fed2KD+, a novel FL framework that leverages a set of tiny unified models and conditional variational auto-encoders (CVAEs) to enable FL training for heterogeneous models between network clients. Using forward and backward distillation processes, Fed2KD+ allows a seamless exchange of knowledge, mitigating data and heterogeneity problems of the model. Moreover, we propose a cosine similarity penalty in the loss function of CVAE+ to enhance the generalizability of CVAE for non-IID scenarios, improving the adaptability and efficiency of the framework. Furthermore, our framework design incorporates a co-design with radio access network (RAN) architecture, reducing the fronthaul traffic volume and improving scalability. Extensive evaluations of one image and two Internet of Things datasets demonstrate the superiority of Fed2KD+ in achieving higher accuracy and faster convergence compared to existing methods, including FedAvg, FedMD, and FedGen. Furthermore, we also performed hardware profiling on the Raspberry Pi and NVIDIA Jetson Nano to quantify the additional resources required to train the unified and CVAE+ models.

Wearable sensors are increasingly used for continuous health monitoring, but their small size limits battery capacity, affecting user experience and monitoring capabilities. To overcome this, we introduce an ultra-low power analog Folded Neural Network (FNN) for physiological signal processing in a batteryless fashion. Our proposed FNN, by serializing computation, provides several benefits over traditional analog implementations, such as lower space, lower power consumption, and lower peak-to-average power ratio. We evaluate our method extensively using a dataset designed for ECG-based screening and diagnosis. Our analysis considers factors such as thermal noise, spatial requirements, and power consumption. Additionally, we evaluate detection performance, investigating various parameters of the proposed FNN. This evaluation provides insights into the optimal configuration for accurate anomaly detection. We observe a good trade-off for accuracy around 6 layers and a hidden size of 30 and further demonstrate that such architecture could be implemented in a wearable device and executed in a batteryless fashion.

Advancements in Intelligent Transportation Systems (ITS) have led to innovative solutions for planning optimization, efficiency enhancement, and resource allocation in transportation networks, which are demonstrated in applications such as smart parking lot management and electric vehicle (EV) charging station allocation, where improved decision-making and system-wide optimization have been achieved. However, as these systems evolve, the demand for better adaptability and coordination continues to grow to maximize their overall effectiveness and efficiency. To achieve this, we propose the Multi-Personality Multi-Agent Meta-Reinforcement Learning (MPMA-MRL) framework. This approach incorporates multiple meta-trained, meta-tested explainable personality policies, which are deployed to each agent. A personality selector is trained and deployed on each agent to optimize the overall performance. MPMA-MRL is superior than traditional methods in terms of the adaptability and coordination in ITS by leveraging improved information from the environment, more practical coordination among agents, faster adaptation speed to intermediate tasks, and more appropriate allocation and planning. The proposed framework is evaluated in the applications of parking lot optimization and EV charging station allocation. Its broader impact on multi-agent smart systems is analyzed to demonstrate its generalizability. The results demonstrate that in parking lot optimization, MPMA-MRL significantly reduces the time required to direct all vehicles to available parking spots. In EV charging station allocation, MPMA-MRL effectively minimizes waiting times at charging stations. Moreover, in both applications, MPMA-MRL exhibits enhanced adaptability to previously unseen scenarios, improving its applicability.

Next Generation Radio Access Networks (NG-RANs) are a promising paradigm for meeting 6G and future application requirements. However, the practical implementation of NG-RAN systems faces significant challenges due to novel technologies, network densification, and more complex applications. Specifically, the limited capacity of front-haul links and privacy concerns have posed severe constraints that must be addressed. To overcome these obstacles, we present a novel approach, called FedBNG, which is a disaggregated and distributed Federated Learning (FL)-based algorithm for NG-RAN. This algorithm enables collaboration between User Equipment (UEs) and the NG-RAN infrastructure through a learning process and shared prediction models, ultimately improving privacy and alleviating the burden on the front-haul interface. Using a shared predictive model, our proposed approach facilitates cooperative learning between Radio Units (RUs) and Distributed Units (DUs). To accomplish this, we initially used the first-phase training models of RUs and DUs as input for local training. Subsequently, the suboptimal DU models are uploaded to the Central Unit (CU) for the next phase of global training. We present numerical results to evaluate the efficacy of our proposed approach in terms of accuracy, service latency, and traffic volume. Our algorithm’s convergence properties demonstrate that it outperforms the current state-of-the-art solution based on FedAvg.

This paper introduces a novel, lightweight, on-board approach to crowd pattern identification, ingeniously using the processes of existing video compression standards, particularly H.264 or MPEG-4. Piggy-backing on the H.264 video-encoding algorithm, we propose real-time crowd pattern recognition and identification methodologies that can identify macroscopic patterns in as low as 2 milliseconds on NVIDIA TX2, resulting in around 45 x execution time reduction compared to existing approaches. Furthermore, we introduce a temporally aware approach to pinpoint and adapt to crowd movement patterns, continuously recalibrating as a drone's Point Of View (POV) varies or observed motions diverge. Evaluating our method against publicly available datasets, we emphasize our system's performance and computational advantages, especially when faced with real-time observational shifts. In conclusion, our approach elegantly bridges the gap between crowd safety imperatives and the challenges of UAV monitoring, heralding a new era of real-time drone-centric crowd management intelligence.

We study the Traffic Steering (TS) problem in Open Radio Access Network (O-RAN), leveraging its RAN Intelligent Controller (RIC), in which RAN configuration parameters of cells can be jointly and dynamically optimized in near-real-time. To address the TS problem, we propose a novel Cascade Reinforcement Learning (CaRL) framework, where we propose state space factorization and policy decomposition to mitigate the need for large complex models and well-labeled datasets. For each sub-state space, an RL sub-policy is trained to optimize the Quality of Service (QoS). To apply CaRL to new network areas, we propose a knowledge transfer approach to initialize a new sub-policy based on knowledge learned by the trained policies. To evaluate CaRL, we build a data-driven and scalable RIC Digital Twin (DT) that is modeled using real-world data, including network setup, user geo-distribution, and traffic demand, among others, from a tier-1 RAN operator. We evaluated CaRL in two DT scenarios representing two different US cities and compared its performance with business-as-usual policy as a baseline and other competing optimization approaches (i.e., heuristic and Q-table algorithms). Furthermore, we have conducted a field trial with the RAN operator to evaluate the performance of CaRL in two areas in the Northeast US regions.

Underwater communications suffer from numerous challenges typically associated with relevant signal attenuation, long propagation delay, limited available bandwidth, and high error rates that severely affect underwater transmission performance. Therefore, it is crucial to apply adaptive or predictive techniques to ensure the best possible performance and guarantee reliability in underwater communication, especially in rapidly changing environments. Using adaptive (i.e., reactive) or predictive (i.e., proactive) methods, it is possible to avoid data retransmission, improve the lifetime of underwater nodes, reduce maintenance frequency and the necessary equipment replacement and recharge, and consequently optimize performance in general. In this regard, many works in the literature propose various adaptive or predictive techniques for UnderWater Acoustic (UWA) networks, which we critically classify and discuss in this qualitative survey.

Achieving reliable underwater acoustic communications is a challenging task due to the time-varying channels in the underwater acoustic environment. Scalable Video Coding (SVC) has been widely used in video transmissions. However, inappropriate SVC structures can lead to poorer received video quality than user requirements or resource waste, especially in underwater time-varying channels. In this work, an adaptive cross-layering solution is proposed and validated for video transmissions in underwater acoustic multicast networks, namely Receiver-Driven Adaptive Scalable Video Transmission (RD-ASVTuw). In RD-ASVTuw, the decision-making about transmission schemes takes place at the receiver through Machine Learning (ML). The transmitter collects over time the selected transmission scheme indexes and the user’s video quality requirements to transmit the SVC video adaptively. At-sea experiments were conducted to collect the required acoustic data. The data collected were then used in MATLAB simulations to validate RD-ASVTuw.

In this article, we introduce a groundbreaking approach for ultra-low-power hybrid analog-digital processing of multimodal physiological data at multiple locations, emphasizing EEG signals. We propose an innovative analog Convolutional Processing Unit (CvPU) that uniquely harnesses the properties of anisotropic diffusion in electrical circuits for convolution. This novel use of anisotropic diffusion-driven convolution sets our work apart. Additionally, we present a controller architecture that allows for the sequential execution of multiple consecutive convolutional layers using the same CvPU array. The proposed neural network architecture to detect seizures using EEG signals is evaluated on a publicly available clinical dataset. Our CvPU array-based convolution’s performance and feasibility metrics have been assessed using SPICE simulation software. Furthermore, we have delved deep into studying the scalability of our approach in terms of power and space and its feasibility for battery-less and implantable applications and have compared it with both digital and hybrid analog-digital methods.

Task offloading with Mobile-Edge Computing (MEC) is envisioned as a promising technique to prolong battery lifetime and enhance the computational capacity of mobile devices. In this paper, we consider a multi-user MEC system with a Base Station (BS) equipped with a computation server that assists users in executing computation-intensive tasks via offloading. Exploiting approximate computing in MEC, we can trade the output accuracy over a subset of offloading data instead of the entire dataset. We formulate the Energy-Latency-aware Task Offloading and Approximate Computing (ETORS) problem, aiming to optimize the trade-off between energy consumption and latency. Due to the mixed-integer nature of this problem, we employ the Dual-Decomposition Method (DDM) to decompose the original problem into three subproblems—namely the Task-Offloading Decision (TOD), the CPU Frequency Scaling (CFS), and the Quality of Computation Control (QoCC). Our approach consists of two iterative layers: in the outer layer, we adopt the duality technique to find the optimal value of the Lagrangian multiplier associated with the primal problem; and in the inner layer, we formulate the subproblems that can be solved efficiently using convex optimization techniques. Simulation results coupled with real-time experiments on a small-scale MEC testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

Unmanned Aerial Vehicles (UAVs) have become prevalent in Search-And-Rescue (SAR) missions. However, existing solutions to the control and coordination of UAV s are mostly limited to specific environments and are not robust to handle unreliable/unstable communications. To deal with these challenges, Hierarchical Multi-Agent Actor-Critic (HMAAC) framework is proposed where a high-level policy is placed on top of individual low-level actor-critic policies to relax the inter-dependency among the agents. The low-level policies are considered conditionally independent given the coordination action, which is generated by the high-level policy. A Central-ized Training Decentralized Execution (CTDE) would not work because it cannot be assumed that communication is always perfect during training and that the whole system can rely on stable communications during deployment. The proposed framework is evaluated in AirSim, a realistic multi-UAV simula-tor, and is compared against two existing algorithms, i.e., Multi- Agent Actor-Critic (MAAC) and decentralized REINFORCE, in two scenarios, (a) when packet drop is modeled as a Bernoulli process and (b) when shadow zones are created in the search space and communication will be lost if the agents are in these zones. Results show that HMAAC is scalable and robust to unreliable communication and outperforms the other algorithms in terms of exploration and coordination when the number of agents is large and communications are not stable.

Reliable and persistent water monitoring is a challenging problem in smart underwater Internet of Things (UW IoT) due to its harsh, unexplored, and unpredictable nature. Given the need for high-resolution spatio–temporal sensing in such environments, traditional digital sensors are not suitable due to their high-cost, high-power consumption, and nonbiodegradable nature. Further, reliable and low-latency communication techniques that avoid data packet retransmissions, if the feedback is available, are crucial for reconstructing the phenomenon being monitored in a timely manner at the fusion center, such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that enable persistent sensing and continually transmitting data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing analog joint source-channel coding (JSCC), a low-complexity compression-communication technique, using biodegradable field effect transistors (FETs). We, then, propose a correlation-aware hybrid automatic repeat request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages JSCC and the redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus, saving energy and time.

An underwater wireless optical communication link can provide high-speed data transfer for robotics applications in deep waters. However, optical links are limited in terms of coverage range because of the high attenuation of light in water caused by absorption and scattering effects. In this work, a new optical transceiver architecture is proposed to solve this coverage problem via a novel Orthogonal Frequency Division Multiplexing (OFDM)-based Polarized Pulse Position Modulation (in short, P-OFDM-PPM) with time-frequency spreading. The optical polarization diversity and multiplexing are utilized at the optical transmitter to improve the system’s robustness and the transmission data rate. This new scheme is able to boost the range coverage by several folds as verified via simulations using realistic models of optical channel propagation. The proposed architecture can be integrated into existing underwater robots to enable next-generation range-extended and high-speed optical links for oceanic explorations.

Multi-camera networks are increasingly becoming pervasive in many monitoring and surveillance applications, and have attracted much attention in distributed systems with collaborative, real-time decision-making capabilities. While in-network data compression brings significant energy savings in camera nodes, signal representation using sparse approximations and overcomplete dictionaries have been shown to outperform traditional compression methods. In this work, an end-to-end and real-time solution is designed and implemented to enable energy-efficient and robust dictionary learning in distributed camera networks by leveraging the spatial correlation of the collected multimedia data. Traditional distributed dictionary learning relies on consensus-building algorithms, which involve communicating with neighboring nodes until convergence is achieved. Existing methods, however, do not exploit spatial correlations in camera networks for improved energy efficiency. In contrast, low-computational-complexity metrics are employed in this work to quantify and exploit the spatial correlation across camera nodes in a wireless network for efficient distributed dictionary learning and in-network image compression. The performance of the proposed approach is validated through extensive simulations on public datasets as well as via real-world experiments on a testbed composed of Raspberry Pi nodes.

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in “Location X”) are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. Most of the existing solutions rely on context obtained directly from sensors which could be hacked, noisy or insufficient, which cannot be relied upon for security applications. In this article, we take a different approach by modeling mobile context based on past context data of related users and considering its unique challenges such as missing features. To this end, we propose three models for modeling mobile context based on symbolic time series data of feature-value pairs—two stochastic models based on the theory of Hidden Markov Models (HMMs) and one model based on deep learning—personalized model(HPContext),collaborative filtering model((HCFContext)), anddeep learning model(DeepContext) to eventually replaceHPContext.HPContextandDeepContextpredict the current context using sequential history of the user's own past context observations; whileHCFContextenhances the former with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in the company, gym friends, family members, etc.DeepContextmodels mobile context based on symbolic (i.e., categorical valued rather than continuous-valued) time series data using deep learning techniques. These models are then used to determine the context of the primary user at the current instant or some timesteps in to the future. Each of the proposed models can also be used to enhance or complement the context obtained from sensors. Finally, these models are thoroughly validated on a real-life dataset.

Achieving reliable acoustic wireless video transmissionsin the extreme and uncertain underwater environment is a challenge due to the limited bandwidth and the error-prone nature of the channel. Aiming at optimizing the received video quality and the user’s experience, an adaptive solution for underwater video transmissions is proposed that is specifically designed for Multi-Input Multi-Output (MIMO)-based Software-Defined Acoustic Modems (SDAMs). To keep the video distortion under an acceptable threshold and to keep the Physical-Layer Throughput (PLT) high, cross-layer techniques utilizing diversity-spatial multiplexing and Unequal Error Protection (UEP) are presented along with the scalable video compression at the application layer. Specifically, the scalability of the utilized SDAM with high processing capabilities is exploited in the proposed structure along with the temporal, spatial, and quality scalability of the Scalable Video Coding (SVC) H.264/MPEG-4 AVC compression standard. The transmitter broadcasts one video stream and realizes multicasting at different users. Experimental results at the Sonny Werblin Recreation Center, Rutgers University-NJ, are presented. Several scenarios for unknown channels at the transmitter are experimentally considered when the hydrophones are placed in different locations in the pool to achieve the required SVC-based video Quality of Service (QoS) and Quality of Experience (QoE) given the channel state information and the robustness of different SVC scalability. The video quality level is determined by the best communication link while the transmission scheme is decided based on the worst communication link, which guarantees that each user is able to receive the video with appropriate quality.

With the increase in the use of unmanned aerial vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or postincident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this work, we propose novel architectures based on deep convolutional and long short-term memory neural networks to detect (via autoencoder) and classify drone misoperations based on real-time sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone misoperations [with about 99% accuracy (simulation data) and up to 85% accuracy (experimental data)]. Furthermore, with the help of field programmable gate array-based hardware acceleration, we achieved a speedup of 40x ($\sim\!\text{2.6 ms}$) for detection, while consuming half the amount of power compared with onboard GPU devices, such as NVIDIA Jetson TX2.

The signal recovery performance of the rectangular-type Analog Joint Source-Channel Coding (AJSCC) is analyzed in this work for high and medium/low Signal-to-Noise Ratio (SNR) scenarios in a wireless sensing system with the Doppler channel. The analytical formulations of the Mean Square Error (MSE) performance are derived based on the geometry of the rectangular-type AJSCC on analog sensing and system with Doppler effects. The comprehensive listing of all cases in the three-dimensional geometric signal mapping curve is provided to obtain the theoretical formulations for the medium/low SNR scenario. Evaluation results indicate that there are optimal parameters in the rectangular-type AJSCC to minimize the signal recovery MSE for analog sensing and system with Doppler effects in both high and medium/low SNR scenarios. The analysis of the work provides insights on the optimization of the rectangular-type AJSCC in practical wireless sensing systems.

Mobile devices aggregate various types of data from sensitive corporate documents to personal content. While users desire to access this content on a single device via a unified user experience and through any mobile app, protecting this data is challenging. Even though different data types have different security and privacy needs, mobile operating systems include only a few, if any, functionalities for fine-grained data protection. We present SWIRLS, an Android-based mobile OS that provides a policy-based information-flow data protection abstraction for mobile apps to support BYOD (bring-your-own-device) use cases. SWIRLS attaches security policies to individual pieces of data and enforces these policies as the data flows through the device. Unlike current BYOD solutions like VMs that create duplication overload, SWIRLS provides a single environment to access content from different security contexts using the same applications while monitoring for malicious data leakage. SWIRLS leverages a two-level hybrid information flow tracking (IFT) mechanism to track both intra-application flows and a higher level IFT based on processes for application isolation. Our evaluation presents BYOD data protection use-cases such as limiting document sharing, preventing leakage based on document classification and security policies based on geo-fencing. SWIRLS only imposes a low battery consumption and performance overhead.

With the rapid growth of the use of Machine Learning (ML) techniques in Unmanned Aerial Vehicles (UAVs), there is an opportunity to use ML techniques to detect and prevent anomalous behavior in drones. However, limited drone power thwarts successful implementation of contemporary power-hungry ML techniques. Therefore, we propose a hybrid analog-digital system to solve the problem of continuous anomaly detection. In this paper, a series of pure analog ML methods including SVMs (linear, polynomial, Radial Basis Function (RBF) and Sigmoid kernels) as well as pure analog fully-connected Neural Network (NN) are presented. We validate our method with sensor data from a series of drone experiments to detect and identify causes of failure in real-time. The results show that RBF kernel provides at least 88.17 % and at most 99.99 % accuracy under different time window and crash-like scenarios with an extremely low False Negative (FN) ratio with sensor data especially in the z-axis.

Mobile location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed-the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from 2.5 m to ≈1 m, a 60 percent improvement.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on real-time sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and realworld experiments on a drone. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 85% accuracy (experimental data)).

Recently, much progress has been made in using Neural Networks (NNs) for important yet narrowly focused tasks such as image classification (e.g., VGG-Net, ResNet), playing complex games like GO or other Computer Vision (CV) tasks. While these achievements are impressive, they are either achieved on computers with virtually unlimited resources or with little regard to real-time actionability. In this paper, we propose to combine the ubiquity of low-resource mobile devices, e.g., drones, with approximate- and distributed-computing techniques in order to make these NN techniques deployable on resource-constrained devices as well as to provide realtime information about the environment. We target situational awareness, which involves sensing the crucial factors in a new environment on a real-time basis. Specifically, we introduce intelligence to a team of drones in the form of real-time detection of a suspect/weapon using local resources and suspect identification in an emergency situation. We validate our proposed methods using Microsoft AirSim simulator via both simulations and hardware-in-the-loop emulations.

Traditionally, underwater acoustic communications is half duplex, i.e., the hydrophones and transducers operate in non-overlapping time-slots/frequency-bands in one direction at a time or frequency. To double the spectral efficiency and allow simultaneous transmission and reception in Full-Duplex mode (FD), a Self-Interference Cancellation (SIC) technique in space is introduced and deployed. Specifically, a novel underwater acoustic system is proposed to perform FD-SIC efficiently via an integrated design combining underwater Acoustic Vector Sensor (AVS) and Phased Array Transducer (PAT) to realize spatial SIC. The energy focusing function of the Beamformer (BF) helps PAT avoid self and mutual spatial interference. The AVS keeps updating the direction of arrival information to let BF adjust the steering angle via an adaptive protocol. The proposal is evaluated and verified via simulations in realistic underwater acoustic channels and is able to achieve 59 dB SIC at 80 kHz steering angle at -5 ° and at least 37 dB within the steering angle region before the input of digital SIC. This indicates that the design is a promising solution for the chosen angle region to perform spatial SIC as well as to prevent the grating lobe interference. The design is being experimentally validated using the data collected from an underwater testbed and implemented on an Field Programmable Gate Array (FPGA) board that provides energy efficiency and real-time processing capabilities.

Search and Rescue (SAR) is an important part of several applications of national and social interest. Existing solutions for search missions in both terrestrial and aerial domains are mostly limited to single agent and specific environments; however, search missions can significantly benefit from the use of multiple agents that can quickly adapt to new environments. In this paper, we propose a framework based on Multi-Agent Deep Reinforcement Learning (MADRL) that realizes the actor-critic framework in a distributed manner for coordinating multiple Unmanned Aerial Vehicles (UAVs) in the exploration of unknown regions. One of the original aspects of our work is that the actors represent simulated or actual UAVs exploring the environment in parallel instead of traditional computer threads. Also, we propose addition of Long Short Term Memory (LSTM) neural network layers to the actor and critic architectures to handle imperfect communication and partial observability scenarios. The proposed approach has been evaluated in a grid world and has been compared against other competing algorithms such as Multi-Agent Q-Learning, Multi-Agent Deep Q-Learning to show its advantages. More generally, our approach could be extended to image-based/continuous action space environments as well.

Mobile Augmented Reality (AR) has become a reality thanks to improvements in mobile hardware. Still, mobile AR lags behind its desktop counterpart in both latency and performance. Simple offloading to external computers has been attempted, but is not practical due to high communication latency and adverse user experience. In this paper, we propose a novel Mobile Edge Computing framework for Augmented Reality applications (MEC-AR). MEC-AR is designed to take advantage of 5G cellular networks and make optimized computation-offloading decisions in a multi-tiered hierarchy. A three-layered architecture involving the end user, the mobile edge, and finally the cloud is envisioned. In the context of MEC resource management, we cast a Mixed Integer Linear Program (MILP) that aims at finding an efficient application placement on the MEC-AR layers to minimize the network latency. We evaluate the performance of our proposed MEC-AR framework by conducting extensive experimental analysis using images taken around Rutgers University. Simulation results coupled with real-time experiments on a small-scale MEC testbed show that our hierarchical computation mechanism improves the performance of mobile AR applications in terms of both energy consumption and network latency.

The research challenge of current wireless sensor networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single power charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities is proposed. Sensing is exclusively the responsibility of analog substrate-consisting of low-power, low-cost all-analog sensors-that sits beneath the traditional WSN comprising of digital nodes, which does all the processing of the sensor data received from analog sensors. A low-power and low-cost solution for substrate sensors has been proposed using analog joint source-channel coding (AJSCC) realized via the characteristics of metal-oxide-semiconductor field-effect transistor (MOSFET). Digital nodes (receiver) also estimate the source distribution at the analog sensors (transmitter) using machine learning techniques so as to find the optimal parameters of AJSCC that are communicated back to the analog sensors to adapt their sensing resolution as per the application needs. The proposed techniques have been validated via simulations from MATLAB and LTSpice to show promising performance and indeed prove that our framework can support large-scale high density and persistent WSN deployment.

Inter-cell interference is a major issue for next-generation wireless cellular networks, due to the increased user mobility, high user density, and backhaul bandwidth constraint. In this work, a unified approach is proposed to address these challenges. The proposal firstly performs a per-cell signal spreading by Zadoff-Chu (ZC) sequence, then the spread signal is precoded by a modified coordinated beamforming scheme. The proposal is robust to Doppler caused by user mobility, supports a high co-channel user capacity for ultra-dense networks, and requires only limited backhaul signaling exchange between basestations thus being suitable for massive MIMO deployment. The advantages of the proposal are firstly analyzed in theory, then evaluation results are presented to validate the proposal for MIMO and massive MIMO setups. It is found that the proposed scheme consistently outperforms the traditional scheme under various scenarios. The practical issues related to synchronization in coordinated beamforming are discussed.

Deploying Autonomous Underwater Vehicles (AUVs) is a necessity to enable a range of civilian/military underwater applications; yet, achieving a reliable coordination among the vehicles is a challenging issue due to the time- and space-varying characteristics of the acoustic communication channel. The design of a Medium Access Control (MAC) based on a probabilistic Space Division Multiple Access (SDMA) method for short/medium distances (less than 2 km) is presented. This method considers the inherent vehicle position uncertainty due to the inaccuracies in models and the drift of the vehicles. It minimizes the acoustic interference statistically by considering the angular position of neighboring vehicles via a two-step estimation and by keeping the transmitter antenna's beamwidth of each vehicle at an optimal value. Such value is chosen considering three contrasting goals, i.e.: (i) spreading the signal beam towards the vehicle to combat position uncertainty using a coarse estimation; (ii) focusing the beam to reduce acoustic energy dispersion through a fine estimation; and (iii) minimizing interference to other vehicles. Simulation results in a sparse underwater network show that this approach mitigates interference, reduces the probability of retransmission, and achieves higher data rates over conventional underwater MAC techniques.

Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection considering the imbalance of normal situations: In particular, driving data consists of multiple normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous ones. Existing machine learning based anomaly detection approaches do not fare sufficiently well when applied to such imbalanced data. In this paper, we present a novel multi-task learning (LSTM autoencoder and predictor) based approach that leverages domain-knowledge (maneuver labels) for anomaly detection in driving data. We evaluate the proposed approach both quantitatively and qualitatively on 150 hours of real-world driving data and show improved performance over baseline/existing approaches.

Certain sensing applications such as Internet of Things (IoTs), where the sensing phenomenon may change rapidly in both time and space, requires sensors that consume ultra-low power (so that they do not need to be put to sleep leading to loss of temporal and spatial resolution) and have low costs (for high density deployment). A novel encoding based on Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) is proposed to realize Analog Joint Source Channel Coding (AJSCC), a low-complexity technique to compress two (or more) signals into one with controlled distortion. In AJSCC, the y-axis is quantized while the x-axis is continuously captured. A power-efficient design to support multiple quantization levels is presented so that the digital receiver can decide the optimum quantization and the analog transmitter circuit is able to realize that. The approach is verified via Spice and MATLAB simulations.

Underwater imagery has enabled numerous civilian applications in various domains, ranging from academia to industry, and from industrial surveillance and maintenance to environmental protection and behavior of marine creatures studies. The accumulation of litter and plastic debris at the seafloor and the bottom of rivers are extremely harmful for the aquatic life. We propose a solution for monitoring this problem using a team of Autonomous Underwater Vehicles (AUVs) to exchange the recorded video in order to reconstruct the map of regions of interest. However, underwater video transmission is a challenge in the harsh environment in which radio-frequency waves are absorbed for distances above a few tens of meters, optical waves require narrow laser beams and suffer from scattering and ocean wave motion, and acoustic waves-while long range-provide a very low bandwidth and unreliable channel for communication. In our solution, the scalable coded video of each vehicle is shared in-network with a selected group of receiving vehicles through the underwater acoustic channel. Presented evaluations, including both simulations and experiments, confirm the efficiency and flexibility of the proposed solution using acoustic software-defined modems.

Task offloading with Mobile-Edge Computing (MEC) is envisioned as a promising technique for prolonging battery lifetime and enhancing the computation capacity of mobile devices. In this paper, we consider a multi-user MEC system with a Base Station (BS) equipped with a computation server assisting mobile users in executing computation-intensive real-time tasks via offloading technique. We formulate the Energy-Latency-aware Task Offloading and Approximate Computing (ETORS) problem, which aims at optimizing the trade-off between energy consumption and application completion time. Due to the centralized and mixed-integer natures of this problem, it is very challenging to derive the optimal solution in practical time. This motivates us to employ the Dual-Decomposition Method (DDM) to decompose the original problem into three subproblems-namely the Task-Offloading Decision (TOD), the CPU Frequency Scaling (CFS), and the Quality of Computation Control (QoCC). Our approach consists of two iterative layers: in the outer layer, we adopt the duality technique to find the optimal value of Lagrangian multiplier associated prime problem; and in the inner layer, we formulate the subproblems that can be solved efficiently using convex optimization techniques. We show that the computation offloading selection depends not only on the computing workload of a task, but also on the maximum completion time of its immediate predecessors and on the clock frequency as well as on the transmission power of the mobile device. Simulation results coupled with real-time experiments on a small-scale MEC testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in "Location X") are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. To this end, two stochastic models based on the theory of Hidden Markov Models (HMMs) to obtain mobile context are proposed-personalized model (HPContext) and collaborative filtering model (HCFContext). The former predicts the current context using sequential history of the user's past context observations; the latter enhances HPContext with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in company, gym friends, family members, etc. Each of the proposed models can also be used to enhance/complement the context obtained from sensors. Furthermore, since privacy is a concern in collaborative filtering, a privacy-preserving method is proposed to derive HCFContext model parameters based on the concepts of homomorphic encryption. Finally, these models are thoroughly validated on a real-life dataset.

Achieving reliable and persistent environmental field estimation in Underwater Internet of Things (UW IoT) is a challenging problem. Given the need for high-resolution spatio-temporal sensing in such environment, traditional digital sensors are not suitable due to their high cost, high power consumption, and non-biodegradable nature. Further, reliable communication techniques that avoid retransmissions are crucial for reconstructing the phenomenon in a timely manner at the fusion center such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that continuously transmit data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing Analog Joint Source Channel Coding (AJSCC), a low-complexity compression-communication technique, using biodegradable Field Effect Transistors (FETs). We then propose a correlation-aware Hybrid Automatic Repeat Request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus saving energy and time. The performance of the proposed analog sensor design and of the correlation-aware HARQ communication technique has been evaluated via simulations and shown to achieve the desired behavior.

Certain sensing applications such as Internet of Things (IoTs), where the sensing phenomenon may change rapidly in both time and space, require sensors that consume ultra-low power devices (so as to be able to collect data continuously and not lose temporal and spatial resolution) and have low costs (for high density deployment). A novel encoding based on Metal Oxide Semiconductor Field Effect Transistors (MOS-FETs) is proposed to realize Analog Joint Source Channel Coding (AJSCC), a low-complexity technique to compress two (or more) signals into one with controlled distortion. The approach is verified via Spice simulations and breadboard implementation.

This paper considers a Mobile-Edge Computing (MEC) enabled wireless network where the MEC-enabled Base Station (MBSs) can host application services and execute computation tasks corresponding to these services when they are offloaded from resource-constrained mobile users. We aim at addressing the joint problem of service caching—the provisioning of application services and their related libraries/database at the MBSs—and task-offloading assignment in a densely-deployed network where each user can exploit the degrees of freedom in offloading different portions of its computation task to multiple nearby MBSs. Firstly, an offloading cost model is introduced to capture the user energy consumption, the service caching cost, and the cloud usage cost. The underlying problem is then formulated as a Mixed-Integer Linear Programming (MILP) problem, which is shown to be NP-hard. Given the intractability of the problem, we exploit local-search techniques to design a polynomial-time iterative algorithm, named COSTA. We prove that COSTA produces a locally optimal solution with cost of at most a constant approximation ratio compared to the optimum. Trace-driven simulations using the workload records from a Google cluster show that COSTA can significantly reduce the offloading cost over competing schemes while achieving a very small optimality gap.

Our goal is to design, develop, and validate via computer simulations and testbed experiments novel resource allocation and computation offloading algorithms aimed at improving the spectral and energy efficiency in next generation cellular networks. To achieve this goal, we exploit the high degree of cooperation provided by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) for cloud-assisted wireless networks, including Cloud Radio Access Network (C-RAN) and Mobile Edge Computing (MEC) infrastructures.

Mobile Edge Computing (MEC) has recently emerged as a promising paradigm to enhance mobile networks' performance by providing cloud-computing capabilities to the edge of the Radio Access Network (RAN) with the deployment of MEC servers right at the Base Stations (BSs). Meanwhile, in-network caching and video transcoding have become important complementary technologies to lower network cost and to enhance Quality of Experience (QoE) for video-streaming users. In this paper, we aim at optimizing the QoE for dynamic adaptive video streaming by taking into account the Distortion Rate (DR) characteristics of videos and the coordination among MEC servers. Specifically, a novel Video-streaming QoE Maximization (VQM) problem is cast as a Mixed-Integer Nonlinear Program (MINLP) that jointly determines the integer video resolution levels and video transmission data rates. Due to the challenging combinatorial and non-convex nature of this problem, the Dual-Decomposition Method (DDM) is employed to decouple the original problem into two tractable subproblems, which can be solved efficiently using standard optimization solvers. Real-time experiments on a wireless video streaming testbed have been performed on a FDD-downlink LTE emulation system to characterize the performance and computing resource consumption of the MEC server under various realistic conditions. Emulation results of the proposed strategy show significant improvement in terms of users' QoE over traditional approaches.

Cloud Radio Access Network (C-RAN) is emerging as a transformative paradigmatic architecture for the next generation of wireless cellular networks. In this demo, a programmable C-RAN testbed is implemented where the Base Band Unit (BBU) is virtualized using the OpenAirInterface (OAI) software platform, and the eNodeB and User Equipment (UEs) are implemented using Software-Defined Radio (SDR) USRP boards. Based on our testbed architecture, we further develop a novel hierarchical computation mechanism to improve the performance of mobile Augmented Reality (AR) applications.

Mobile-Edge Computing (MEC) is a promising paradigm that provides storage and computation resources at the network edge in order to support low-latency and computation-intensive mobile applications. In this article, we propose a joint collaborative caching and processing framework that supports Adaptive Bitrate (ABR)-video streaming in MEC networks. We formulate an Integer Linear Program (ILP) that determines the placement of video variants in the caches and the scheduling of video requests to the cache servers so as to minimize the expected delay cost of video retrieval. The considered problem is challenging due to its NP-completeness and to the lack of a-priori knowledge about video request arrivals. Our approach decomposes the original problem into a cache placement problem and a video request scheduling problem while preserving the interplay between the two. We then propose practically efficient solutions, including: (i) a novel heuristic ABR-aware proactive cache placement algorithm when video popularity is available, and (ii) an online low-complexity video request scheduling algorithm that performs very closely to the optimal solution. Simulation results show that our proposed solutions achieve significant increase in terms of cache hit ratio and decrease in backhaul traffic and content access delay compared to the traditional approaches.

Most of the current solutions for autonomous flights in indoor environments rely on purely geometric maps (e.g., point clouds). There has been, however, a growing interest in supplementing such maps with semantic information (e.g., object detections) using computer vision algorithms. Unfortunately, there is a disconnect between the relatively heavy computational requirements of these computer vision solutions, and the limited computation capacity available on mobile autonomous platforms. In this paper, we propose to bridge this gap with a novel Markov Decision Process framework that adapts the parameters of the vision algorithms to the incoming video data rather than fixing them a priori. As a concrete example, we test our framework on a object detection and tracking task, showing significant benefits in terms of energy consumption without considerable loss in accuracy, using a combination of publicly available and novel datasets.

The growing importance, large scale, and high server density of high-performance computing datacenters make them prone to attacks, misconfigurations, and failures (of the cooling as well as of the computing infrastructure). Such unexpected events often lead to thermal anomalies - hotspots, fugues, and coldspots - which impact the cost of operation of datacenters. A model-based thermal anomaly detection mechanism, which compares expected (obtained using heat-generation and -extraction models) and observedthermal maps (obtained using thermal cameras) of datacenters, is proposed. In addition, a novel Thermal Anomaly-aware Resource Allocation (TARA) is designed to induce a time-varying thermal fingerprint (thermal map) of the datacenter so to maximize the detection accuracy of the anomalies. As shown via experiments on a small-scale testbed as well as via trace-driven simulations, such model-based thermal anomaly detection solution in conjunction with TARA significantly improves the detection probability compared to anomaly detection when scheduling algorithms such as random, round robin, and best-fit-decreasing are employed.

In this article, we propose a novel cooperative hierarchical caching framework in a Cloud Radio Access Network (C-RAN), in which a new cloud-cache at Cloud Processing Unit (CPU) is envisioned to bridge the storage-capacity/delay-performance gap between the traditional edge-based and core-based caching paradigms. A delay-cost model is introduced and the cache placement problem is formulated that aims at minimizing the average delay-cost of content delivery in the network. Given the NP-completeness of the cache placement problem, we propose a low-complexity heuristic cache-management strategy comprising of a proactive cache-distribution algorithm and a reactive cache-replacement algorithm. Furthermore, a Cache-Aware Request Scheduling (CARS) algorithm is devised in order to optimize online the tradeoff between content download rate and content access delay. Via extensive numerical simulations-carried out using both real-world YouTube video requests and synthetic content requests-it is demonstrated that the proposed cache-management strategy outperforms traditional caching strategies in terms of cache hit ratio, average content access delay, and backhaul traffic load. Additionally, it is shown that the proposed CARS algorithm achieves superior tradeoff performance over traditional approaches that optimize either users' rate or access delay alone.

Cloud radio access network (C-RAN) is emerging as a transformative paradigmatic architecture for the next generation of cellular networks. In this paper, a novel resource allocation solution that optimizes the energy consumption of a C-RAN is proposed. First, an energy consumption model that characterizes the computation energy of the base band unit (BBU) pool is introduced based on the empirical results collected from a programmable C-RAN testbed. Then, the resource allocation problem is split into two subproblems-namely the bandwidth power allocation (BPA) and the BBU energy-aware resource allocation (EARA). The BPA, which is first cast via mixed-integer nonlinear programming and then reformulated as a convex problem, aims at assigning a feasible bandwidth and power to serve all users while meeting their quality of service (QoS) requirements. The second subproblem, i.e., the BBU EARA, is defined as a bin-packing problem that aims at minimizing the number of active virtual machines in the BBU pool to save energy. Simulation results coupled with the real-time experiments on a small-scale C-RAN testbed show that the proposed resource allocation solution optimizes the energy consumption of the network while meeting practical constraints and QoS requirements, and outperforms competing algorithms, such as best fit decreasing, RRH-clustering, and SINR-based.

Mobile phones provide an excellent opportunity for building context-aware applications. In particular, location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed--the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. In this paper, a privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular RSSI, sound and light levels, that enables room-level localization as main application (though sub room level granularity is possible). The privacy is inherently built into the solution based on onion routing, and perturbation/randomization techniques, and exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The proposed solution has been analyzed in terms of privacy, accuracy, optimum parameters, and other overheads on location data collected at multiple indoor and outdoor locations.

A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power consumption of the sensor is achieved by an all-analog circuit implementing Analog Joint Source-Channel Coding (AJSCC) compression. The sensor is applicable to a wide range of biomedical applications that require real-time concurrent molecular biomarker and physiological signal monitoring.

Current Distributed Radio Access Networks (DRANs), which are characterized by a static configuration and deployment of Base Stations (BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity demands. At the same time, each BS's spectrum and computing resources are only used by the active users in the cell range, causing idle BSs in some areas/times and overloaded BSs in other areas/times. Recently, Cloud Radio Access Network (CRAN) has been introduced as a new centralized paradigm for wireless cellular networks in which-through virtualization-the BSs are physically decoupled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs). In this paper, a novel elastic framework aimed at fully exploiting the potential of C-RAN is proposed, which is able to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. Simulation and testbed experiment results are presented to illustrate the performance gains of the proposed elastic solution against the current static deployment.

Transmitting large amounts of data such as videos underwater is an important yet challenging problem in the harsh underwater environment in which radio-frequency waves are absorbed for distances above a few tens of meters, optical waves require narrow laser beams and suffer from scattering and ocean wave motion, and acoustic waves-while being able to propagate up to several tens of kilometers-lead to a communication channel that is very dynamic, prone to fading, spectrum limited with passband bandwidths of only a few tens of kHz, and affected by non-Gaussian noise. Notwithstanding these challenges, a hybrid solution that is capable of transmitting at high data rates underwater via acoustic waves at short/medium distances is proposed. The solution introduces a novel signaling method, called Signal-Space-Frequency Beamforming (SSFB), for a multiple antenna where each antenna consists of Uniform Circular Array (UCA) hydrophones mounted on an underwater vehicle to steer the beam in both azimuth and elevation planes; then, an array of Acoustic Vector Sensors (AVS)-hydrophones that are able to capture the acoustic particle velocity/direction of arrival in addition to measuring regular scalar pressure-are mounted on the surface buoy. Detection is performed based on the beam spatial separation and direction of arrival angles' estimation. Simulation results confirm that this solution outperforms stateof-the-art underwater acoustic transmission techniques, whose data rates are limited only to few tens of kbps.

A novel mobility-aware and energy-efficient coded caching provisioning strategy, Mobee, is proposed for a Cloud Radio Access Network (C-RAN). The placement of the Maximum-Distance Separable (MDS) encoded content at the Base Stations (BSs) is optimized to minimize the total energy consumption of the network comprising the transport and the caching energy consumptions. To account for user mobility, an estimation model for content-request rates at the BSs is derived-based on the long-term content popularity and user-mobility pattern. The mobility-aware cache placement problem is then formulated as a convex optimization problem, which can be efficiently solved using standard solvers. Simulation results show that the proposed Mobee strategy significantly reduces the network energy consumption compared to traditional approaches.

Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and/or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages-it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.

A brain-computer interface (BCI) for seizure prediction provides a means of controlling epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected but yet can be defined sufficiently to be selectively influenced by strategically implanted electrodes. Challenges remain in offering real-time solutions with such technology because of the immediacy of electrographic ictal behavior. The nonstationary nature of electroencephalographic (EEG) and electrocorticographic (ECoG) signals results in wide variation of both normal and ictal patterns among patients. The use of manually extracted features in a prediction task is impractical and the large amount of data generated even among a limited set of electrode contacts will create significant processing delays. Big data in such circumstances not only must allow for safe storage but provide high computational resources for recognition, capture and real-time processing of the preictal period in order to execute the timely abrogation of the ictal event. By leveraging the potential of cloud computing and deep learning, we develop and deploy BCI seizure prediction and localization from scalp EEG and ECoG big data. First, a new method for epileptic seizure prediction and localization of the seizure focus is presented. Second, an extended optimization approach on existing deep-learning structures, Stacked Auto-encoder and Convolutional Neural Network (CNN), is proposed based on principle component analysis (PCA), independent component analysis (ICA), and Differential Search Algorithm (DSA). Third, a cloud-computing solution (i.e., Internet of Things (IoT)), is developed to define the proposed structures for real-time processing, automatic computing and storage of big data. The ECoG clinical datasets on 11 patients illustrate the superiority of the proposed patient-specific BCI as an alternative to current methodology to offer support for patients with intractable focal epilepsy.

Clouds provide the abstraction of nearly-unlimited computing resources through the elastic use of federated resource pools (virtualized datacenters). They are being increasingly considered for HPC applications, which have traditionally targeted grids and supercomputing clusters. However, maximizing energy efficiency and utilization of cloud datacenter resources, avoiding undesired thermal hotspots (due to overheating of over-utilized computing equipment), and ensuring quality of service guarantees for HPC applications are all conflicting objectives, which require joint consideration of multiple pairwise tradeoffs. An innovative proactive thermal-aware virtual machine consolidation (involving allocations as well as migrations) technique is proposed to maximize computing resource utilization, to minimize data center energy consumption for computing, and to improve the efficiency of heat extraction. The capability to migrate virtual machines away from lightly-loaded servers in a thermal-aware manner opens up opportunity to improve resource consolidation over time and, hence, achieve the aforementioned goals. The effectiveness of the proposed technique is verified through experimental evaluations with HPC workload traces under single- as well as federated-datacenter scenarios.

A novel dynamic radio-cooperation strategy is proposed for a Cloud Radio Access Network (Cloud-RAN) consisting of multiple Remote Radio Heads connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of Cloud-RAN in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink Weighted Sum-Rate System Utility (WSRSU). Due to the combinatorial nature of the radio clustering process and to the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. The proposed approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP) and applying Sequential Convex Approximation (SCA) to derive a novel iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides near-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of Cloud-RAN over a traditional RAN with distributed computing resources.

Multi-cell interference coordination via Block Diagonalization (BD), which is widely used in LTE-Advanced interference management, has three main drawbacks: (1) limitation on the number of supported co-channel users; (2) performance degradation in Doppler channels with feedback delay and quantization; and (3) high signaling overhead to exchange Channel State Information (CSI) and to generate precoding matrix. In this paper, a new approach is proposed to overcome these drawbacks and to improve the multi-cell interference coordination. The core idea is to create a null space only to the users within one cell; to achieve this goal, Zadoff-Chu Sequence (ZCS) spreading is used to eliminate the multi-cell interference by leveraging the zero-correlation property of cyclic shifts of the ZCS. This way, the capacity of the cellular network is improved in Doppler channels with delayed and quantized feedback; moreover, signaling overhead and computational complexity are both reduced. The auxiliary sampling capability of the hardware is utilized to realize the higher sampling rate required by the proposed approach. The new interference-management solution is compared against the existing one via computer simulations and is shown to lead to significant capacity gains even in high-mobility scenarios.

A low-complexity and low-power all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC). The proposed idea is to adopt Voltage Controlled Voltage Source (VCVS) to realize the rectangular-type mapping in AJSCC. The proposal is verified by Spice simulations as well as via breadboard and Printed Circuit Board (PCB) implementations. Field testing results indicate that the design is feasible for low-complexity and low-power systems such as wireless sensor networks for environmental monitoring.

Recently, implementing Radio Access Network (RAN) functionality on cloud-based computing platform has become an emerging solution that leverages the many advantages of cloud infrastructure, such as shared computing resources and storage capacity, while lowering the operational cost. In this paper, we propose a novel caching framework aimed at fully exploiting the potential of such systems through cooperative hierarchical caching which minimizes the network costs of content delivery and improves users' Quality of Experience (QoE). In particular, the cloud-cache in the cloud processing unit (CPU) presents a new layer in the RAN cache hierarchy, bridging the capacity-performance gap between the traditional edge-based and core-based caching schemes. A delay cost model is introduced to characterize and formulate the cache placement optimization problem, which is shown to be NP-complete. As such, a low complexity, heuristic cache management strategy is proposed, constituting of a proactive cache distribution algorithm and a reactive cache replacement algorithm. Extensive numerical simulations are carried out using both real-world YouTube video requests and synthetic content requests. It is demonstrated that our proposed Octopus caching strategy significantly outperforms the traditional caching strategies in terms of cache hit ratio, average content access delay and backhaul traffic load.

Mobile computing is one of the largest untapped reservoirs in today's pervasive computing world as it has the potential to enable a variety of in-situ, real-time applications. Yet, this computing paradigm suffers when the available resources - such as device battery, CPU cycles, memory, I/O data rate - are limited. In this paper, the new paradigm of approximate computing is proposed to harness such potential and to enable real-time computation-intensive mobile applications in resource-limited and uncertain environments. A reduction in time and energy consumed by an application is obtained via approximate computing by decreasing the amount of computation needed by different tasks in an application; such improvement, however, comes with the potential loss in accuracy. Hence, a Mobile Distributed Computing framework, MobiDiC, is introduced to determine offline the `approximable' tasks in an application and a light-weight algorithm is devised to select the approximate version of the tasks in an application during run-time. The effectiveness of the proposed approach is validated through extensive simulation and testbed experiments by comparing approximate versus exact-computation performance.

A queue-aware robust (QuaRo) coordinated transmission strategy is proposed for Cloud Radio Access Networks (C-RANs) with a central BaseBand processing Unit (BBU) connected to multiple Remote Radio Heads (RRHs). Such QuaRo strategy is adaptive to both user-traffic urgency via Queue State Information (QSI) and wireless channel opportunity via the observed (yet imperfect) Channel State Information (CSI). This involves clustering the RRHs into virtual user-centric clusters and performing Coordinated Beamforming (CB) from each virtual cluster to the target user in the downlink. The underlying control policy is formulated via Lyapunov optimization to minimize the average total transmit power at the RRHs while ensuring the stability of the system. In particular, the designed control policy does not require a-priori knowledge of the probability distribution of data-traffic arrival and channel states, and is robust against the instantaneous channel estimation error in each time slot. Extensive simulation results are presented to illustrate performance gains and robustness of the proposed solutions.

Stress is one of the key factor that impacts the quality of our daily life: From the productivity and efficiency in the production processes to the ability of (civilian and military) individuals in making rational decisions. Also, stress can propagate from one individual to other working in a close proximity or toward a common goal, e.g., in a military operation or workforce. Real-time assessment of the stress of individuals alone is, however, not sufficient, as understanding its source and direction in which it propagates in a group of people is equally-if not more-important. A continuous near real-time in situ personal stress monitoring system to quantify level of stress of individuals and its direction of propagation in a team is envisioned. However, stress monitoring of an individual via his/her mobile device may not always be possible for extended periods of time due to limited battery capacity of these devices. To overcome this challenge a novel distributed mobile computing framework is proposed to organize the resources in the vicinity and form a mobile device cloud that enables offloading of computation tasks in stress detection algorithm from resource constrained devices (low residual battery, limited CPU cycles) to resource rich devices. Our framework also supports computing parallelization and workflows, defining how the data and tasks divided/assigned among the entities of the framework are designed. The direction of propagation and magnitude of influence of stress in a group of individuals are studied by applying real-time, in situ analysis of Granger Causality. Tangible benefits (in terms of energy expenditure and execution time) of the proposed framework in comparison to a centralized framework are presented via thorough simulations and real experiments.

Innovative mobile applications that rely on real-time in-situ processing of data collected in the fieldneedto tap into the heterogeneous sensing and computing capabilities of sensor nodes, mobile handhelds as well as computing and storage servers in remote datacenters. There is, however,uncertaintyassociated with thequalityandquantityof data from mobile sensors as well as with theavailabilityandcapabilitiesof mobile computing resources on the field. Data and computing-resource uncertainty, if unchecked, may propagate up the “raw data$\rightarrow$information$\rightarrow$knowledge” chain and have an adverse effect on the relevance of the generated results. A generalized workflow representation scheme that can represent a wide variety of data- and task-parallel ubiquitous mobile applications is presented. A unified uncertainty-aware framework for data and computing-resource management to enable real-time, in-situ processing of applications is proposed and evaluated. The framework employs a two-phase solution that captures the propagation of data uncertainty up the data-processing chain using interval arithmetic in the first phase and that employs multi-objective optimization for task allocation in the second phase. The results of a case study to assess effectiveness the proposed framework are discussed in detail. Results reaffirm that i) data-uncertainty awareness helps control the uncertainty in the final result and ii) multi-objective combinatorial approach for task allocation significantly outperforms the single-objective approaches in terms of makespan (15 percent improvement), fairness in battery drain (56 percent improvement), and network load (54 percent improvement).

Fractional Frequency Reuse (FFR) and Coordinated Multi Point (CoMP) processing are two of the conventional methods to mitigate the Inter-Cell Interference (ICI) and to improve the average Signal-to-Interference-plus-Noise Ratio (SINR). However, FFR is associated with low system spectral efficiency and CoMP does not take any action to mitigate the inter-cluster interference. In the context of Cloud Radio Access Network (C-RAN) -- a new centralized paradigm for broadband wireless access that addresses efficiently the fluctuation in capacity demand through real-time Virtual Base Station (VBS) cooperation in the Cloud -- in this paper an innovative uplink solution, called Cloud-CFFR, is proposed to address the aforementioned problems. With respect to both FFR and CoMP, Cloud-CFFR decreases the complexity, delay, and ICI while increasing the system spectral efficiency. Since the system performance in cell-edge regions relies on the cooperation of different VBSs, there is no service interruption in handling handovers, moreover, in order to address the unanticipated change in capacity demand, Cloud-CFFR dynamically changes the sub-band boundaries based on the number of active users in the clusters. Simulation results confirm the validity of our analysis and show the benefits of this novel uplink solution.

Current Distributed Radio Access Network (DRAN) architectures, which are characterized by a static configuration and deployment of Base Stations (BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity demand as well as the electromagnetic interference caused by the high band reuse, making them inadequate to support the ever-increasing users' data-rate requests. Cloud Radio Access Network (C-RAN) is a new centralized paradigm based on virtualization that has emerged as a promising architecture to address efficiently such fluctuations. C-RAN provides high energy efficiency and resource utilization across Software Defined Wireless Networks (SDWNs). A novel reconfigurable solution based on C-RAN is proposed to adapt dynamically and efficiently to fluctuations in per-user capacity demand. A real-time test bed is used to compare the proposed dynamic provisioning solution against the traditional static approach.

Combating interference is an important yet challenging issue for multiuser communications especially in the harsh underwater acoustic environment. In this paper, a novel Angle-Of-Departure (AOD)-based technique is proposed, which accounts for the inherent position uncertainty of underwater propeller-driven Autonomous Underwater Vehicles (AUVs) or buoyancy-driven gliders. A new probabilistic Space Division Multiple Access (SDMA) technique is studied using confidence interval estimation, and an effective approach to manage interference statistically is discussed. Also, an optimization problem is proposed to mitigate multiuser interference while keeping the transmitter antennae beam width at a desirable value so to find a trade off among (i) spreading the beam towards the receiver to combat position uncertainty, (ii) focusing such beam to minimize dispersion, and (iii) minimizing interference to other vehicles in the surrounding. Solutions and algorithms are proposed to overcome the multiuser interference via a hybrid SDMA-Time Division Multiple Access (TDMA) method. Simulation results show that the solution mitigates statistical interference, lessens packet retransmission rate, and obtains Signal-to-Interference-plus-Noise Ratio (SINR) gain and rate efficiency over conventional TDMA and SDMA methods.

A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beam forming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beam forming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beam forming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.

Coordinated Multi-Point (CoMP) processing is one of the promising methods to mitigate the intra- cluster interference in cellular systems, to improve the average Signal-to-Interference-plus- Noise Ratio (SINR), and to increase the overall spectral efficiency. Such method, however, does not take any action to mitigate the inter-cluster interference, which leads to poor performance for cluster-edge Mobile Stations (MSs) and, consequently, to unfairness in service provision. In the context of Cloud Radio Access Network (C- RAN) - a new centralized paradigm for wireless cellular networks in which Base Stations (BSs) are physically unbundled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs) - an innovative solution, called Dynamic Joint Processing (DJP), is proposed to mitigate both intra- and inter-cluster interference so to improve performance of cluster-edge MSs. A dynamic clustering approach is presented in which, for each subcarrier, a virtual cluster is defined, and its size is dynamically changed based on the position of the MSs. Simulation results confirm the validity of our approach.

A data-centric joint adaptive sampling and sleep scheduling solution, SILENCE, for autonomic sensor-based systems that monitor and reconstruct physical or environmental phenomena is proposed. Adaptive sampling and sleep scheduling can help realize the much needed resource efficiency by minimizing the communication and processing overhead in densely deployed autonomic sensor-based systems. The proposed solution exploits the spatiotemporal correlation in sensed data and eliminates redundancy in transmitted data through selective representation without compromising on accuracy of reconstruction of the monitored phenomenon at a remote monitor node. Differently from existing adaptive sampling solutions, SILENCE employs temporal causality analysis to not only track the variation in the underlying phenomenon but also its cause and direction of propagation in the field. The causality analysis and the same correlations are then leveraged for adaptive sleep scheduling aimed at saving energy in wireless sensor networks (WSNs). SILENCE outperforms traditional adaptive sampling solutions as well as the recently proposed compressive sampling techniques. Real experiments were performed on a WSN testbed monitoring temperature and humidity distribution in a rack of servers, and the simulations were performed on TOSSIM, the TinyOS simulator.

Underwater mobile sensor networks such as Autonomous Underwater Vehicles (AUVs) or robots are envisioned to enable applications for oceanographic data collection, environmental and pollution monitoring, offshore exploration, and distributed tactical surveillance. These applications require running compute- and data-intensive algorithms that go beyond the capabilities of the individual AUVs that are involved in a mission. To execute these task-parallel algorithms in resource- and time-constrained environments, dynamic and reliable collaboration between local networked robots (e.g., AUVs) and remote public Clouds is needed. To this end, the heterogeneous sensing, computing, communication, and storage capabilities of local and remote resources are exploited to form a “loosely coupled” mobile Cloud, and a novel resource provisioning engine that dynamically takes decisions on “what” and “where” the tasks should be executed in the mobile Cloud is introduced. Comparison of benefits of collaboration between local and Cloud resources with purely local and centralized approaches are presented through exhaustive computer simulations.

Mobile platforms are becoming the predominant medium of access to Internet services due to the tremendous increase in their computation and communication capabilities. However, enabling applications that require real-time in-the-field data collection and processing using mobile platforms is still challenging due to i) the insufficient computing capabilities and unavailability of complete data on individual mobile devices and ii) the prohibitive communication cost and response time involved in offloading data to remote computing resources such as cloud datacenters for centralized computation. A novel resource provisioning framework for organizing the heterogeneous sensing, computing, and communication capabilities of static and mobile devices in the vicinity in order to form an elastic resource pool—a hybrid static/mobile computing grid (also called a loosely-coupled mobile device cloud)—is presented. This local computing grid can be harnessed to enable innovative data- and compute-intensive mobile applications such as ubiquitous context-aware health and wellness monitoring of the elderly, distributed rainfall and flood-risk estimation, distributed object recognition and tracking, and content-based distributed multimedia search and sharing. In order to address challenges such as the inherent uncertainty in the hybrid grid (in terms of network connectivity and device availability), the proposed role-based resource provisioning framework is imparted with autonomic capabilities, namely, self-organization, self-optimization, and self-healing. A thorough experimental analysis aimed at verifying and demonstrating the benefits brought by autonomic capabilities of the framework is also presented in detail.

A reconfigurable, low-cost Cyber Physical System (CPS) that reuses the same hardware for multiple applications is presented in this demo. Grid Eye (high-precision, infrared-array sensor) is employed to support multiple applications and to demonstrate the CPS reconfiguration capability. The user interface of our system is designed to improve the accessibility of the CPS for those students, engineers, and practitioners who do not have deep knowledge about the hardware implementation in different domains such as medical, civil, and mechanical engineerings. A generic hardware and software design pattern is adapted to interface with any type of sensor.

Due to the rapid growing popularity of mobile Internet, broadband cellular wireless systems are expected to offer higher and higher data rates even in high-mobility environments. Cloud Radio Access Network (C-RAN) is a new centralized paradigm for broadband wireless access that addresses efficiently the fluctuation in capacity demand through real-time inter-Base Station (BS) cooperation. An innovative Blind Source Separation (BSS)-based cellular communication solution for CRANs, Cloud-BSS, which leverages the inter-BS cooperation, is proposed. Cloud-BSS groups contiguous cells into clusters - sets of neighboring cells inside which mobile stations do not need to perform handovers - and allows them to use all of the frequency channels. The proposed solution is studied under different network topologies, and a novel strategy, called Channel-Select, to improve the Signal-to-Noise Ratio (SNR) is introduced. Cloud-BSS enhances the cluster spectral efficiency, decreases handovers, eliminates the need for bandwidth-consuming channel estimation techniques, and mitigates interference. Simulation results, which are discussed along with concepts, confirm these expectations.

Underwater acoustic communications consume a significant amount of energy due to the high transmission power (10-50 W) and long data packet transmission times (0.1-1 s). Mobile Autonomous Underwater Vehicles (AUVs) can conserve energy by waiting for the `best' network topology configuration, e.g., a favorable alignment, before starting to communicate. Due to the frequency-selective underwater acoustic ambient noise and high medium power absorption - which increases exponentially with distance - a shorter distance between AUVs translates into a lower transmission loss and a higher available bandwidth. By leveraging the predictability of AUV trajectories, a novel solution is proposed that optimizes communications by delaying packet transmissions in order to wait for a favorable network topology (thus trading end-to-end delay for energy and/or throughput). In addition, the solution proposed - which is implemented and compared with geographic routing solutions and delay-tolerant networking solutions using an emulator that integrates underwater acoustic WHOI Micro-Modems - exploits the frequency-dependent radiation pattern of underwater acoustic transducers to reduce communication energy consumption by adjusting the transducer directivity on the fly.

The growing importance, large scale, and high server density of high-performance computing datacenters make them prone to strategic attacks, misconfigurations, and failures (cooling as well as computing infrastructure). Such unexpected events lead to thermal anomalies - hotspots, fugues, and coldspots - which significantly impact the total cost of operation of datacenters. A model-based thermal anomaly detection mechanism, which compares expected (obtained using heat generation and extraction models) and observed thermal maps (obtained using thermal cameras) of datacenters is proposed. In addition, a Thermal Anomaly-aware Resource Allocation (TARA) scheme is designed to create time-varying thermal fingerprints of the datacenter so to maximize the accuracy and minimize the latency of the aforementioned model-based detection. TARA significantly improves the performance of model-based anomaly detection compared to state-of-the-art resource allocation schemes.

The goal of this paper is to enable near-realtime acquisition and processing of high resolution, high-quality, heterogeneous data from mobile and static sensing platforms to advance ocean exploration by providing infrastructure for a distributed computing framework. Reaching this goal will improve the efficiency of monitoring dynamic oceanographic phenomena such as phytoplankton growth and rate of photosynthesis, salinity and temperature gradient, and concentration of pollutants. resource provisioning framework for organizing the heterogeneous sensing, computing, and communication capabilities of static and mobile devices in the vicinity in order to form an elastic resource pool a hybrid static/mobile computing grid is presented. This local computing grid can be harnessed to enable innovative data- and compute-intensive mobile applications such as onshore near-real-time data processing, analysis and visualization, mission planning and online ocean adaptive sampling.

Uncertainties in the wireless communication medium do not allow for guarantees in network performance for cognitive radio applications envisaged for mobile ad hoc emergency networking. The novel concept of mission policies, which specify the Quality of Service (QoS) requirements of the incumbent network as well as the cognitive radio networks, is introduced. The use of mission policies, which vary over time and space, enables graceful degradation in the QoS of incumbent network (only when necessary) based on mission-policy specifications. A Multi-Agent Reinforcement Learning (MARL)-based cross-layer communication framework, RescueNet, is proposed for self-adaptation of nodes in cognitive radio networks. Also, the novel idea of knowledge sharing among the agents (nodes) is introduced to significantly improve the performance of the proposed solution.

The heterogeneous sensing and computing capabilities of sensor nodes, mobile handhelds, as well as computing and storage servers in remote data centers can be harnessed to enable innovative mobile applications that rely on real-time in-situ processing of data generated in the field. There is, however, uncertainty associated with the quality and quantity of data from mobile sensors as well as with the availability and capabilities of mobile computing resources on the field. Data and computing-resource uncertainty, if unchecked, may propagate up the "raw-data→information→knowledge" chain and have an adverse effect on the relevance of the generated results. A unified uncertainty-aware framework for data and computing-resource management is proposed to enable in-situ processing of application workflows on mobile sensing and computing platforms and, hence, to generate actionable knowledge from raw data within realistic time bounds. A two-phase solution that captures the propagation of data-uncertainty up the data-processing chain using interval arithmetic in the first phase and that employs multi-objective optimization for task allocation in the second phase is presented and evaluated in detail.

Clouds provide the abstraction of nearly-unlimited computing resources through the elastic use of federated resource pools (virtualized datacenters). They are being increasingly considered for HPC applications, which have traditionally targeted grids and supercomputing clusters. However, maximizing energy efficiency and utilization of cloud datacenter resources, avoiding undesired thermal hotspots (due to overheating of over-utilized computing equipment), and ensuring quality of service guarantees for HPC applications are all conflicting objectives, which require joint consideration of multiple pairwise tradeoffs. The novel concept of heat imbalance, which captures the unevenness in heat generation and extraction, at different regions inside a HPC cloud datacenter is introduced. This thermal awareness enables proactive datacenter management through prediction of future temperature trends as opposed to the state-of-the-art reactive management based on current temperature measurements. VMAP, an innovative proactive thermal-aware virtual machine consolidation technique is proposed to maximize computing resource utilization, to minimize datacenter energy consumption for computing, and to improve the efficiency of heat extraction. The effectiveness of the proposed technique is verified through experimental evaluations with HPC workload traces under single-as well as federated-datacenter scenarios (in the machine rooms at Rutgers University and University of Florida).

Localization underwater has been known to be challenging due to the limited accessibility of the Global Positioning System (GPS) to obtain absolute positions. This becomes more severe in the under-ice environment since the ocean surface is covered with ice, making it more difficult to access GPS or to deploy localization infrastructure. In this paper, a novel solution that minimizes localization uncertainty and communication overhead of under-ice Autonomous Underwater Vehicles (AUVs) is proposed. Existing underwater localization solutions generally rely on reference nodes at ocean surface or on localization infrastructure to calculate positions, and they are not able to estimate the localization uncertainty, which may lead to the increase of localization error in under-ice environments. In contrast, using the notion of external uncertainty (i.e., the position uncertainty as seen by others), our solution can characterize an AUV's position with a probability model. This model is further used to estimate the uncertainty associated with our proposed Doppler-based localization technique - a novel one that can exploit ongoing communications for localization, as well as that associated with the standard distance-based localization. Based on this uncertainty estimate, we further propose algorithms to minimize localization uncertainty and communication overhead. Our solution is emulated and compared against existing solutions, showing improved performance.

Underwater acoustic communications consume a significant amount of energy due to the high transmission power (10-50 W) and long data packet transmission times (0.1-1 s). Mobile Autonomous Underwater Vehicles (AUVs) can conserve energy by waiting for the `best' network topology configuration, e.g., a favorable alignment, before starting to communicate. Due to the frequency-selective underwater acoustic ambient noise and high medium power absorption - which increases exponentially with distance - a shorter distance between AUVs translates into a lower transmission loss and a higher available bandwidth. By leveraging the predictability of AUV trajectories, a novel solution is proposed that optimizes communications by delaying packet transmissions in order to wait for a favorable network topology (thus trading end-to-end delay for energy and/or throughput). In addition, the solution proposed - which is implemented and compared with other solutions using an emulator that integrates underwater acoustic WHOI Micro-Modems - exploits the frequency-dependent radiation pattern of underwater acoustic transducers to reduce communication energy consumption by adjusting the transducer directivity on-the-fly.

The predictable trajectory of underwater gliders can be used in geographic routing protocols. Factors such as drifting and localization errors cause uncertainty when estimating a glider's trajectory. Existing geographic routing protocols in underwater networks generally assume the positions of the nodes are accurately determined by neglecting position uncertainty. In this paper, a paradigm-changing geographic routing protocol that relies on a statistical approach to model position uncertainty is proposed. Our routing protocol is combined with practical cross-layer optimization to minimize energy consumption. Our solution's performance is tested and compared with existing solutions using a real-time testbed emulation that uses underwater acoustic modems.

Underwater testbeds are key tools for performance evaluation of underwater communication and coordination algorithms to enable swarming of autonomous vehicles. We describe a demonstration of underwater vehicle team formation and steering algorithms using our underwater testbed. The tesdbed allows the user to configure ocean currents and underwater communication parameters so to study properties of these algorithms such as stability, robustness, and convergence.

Underwater Acoustic Sensor Networks (UW-ASNs) consist of stationary or mobile nodes such as Autonomous Underwater Vehicles (AUVs), which may be classified as propeller-driven vehicles and gliders, that are equipped with a variety of sensors for performing collaborative monitoring tasks. The missions entrusted to the AUVs in this work are critical to human life and property, are bound by severe time and energy constraints, and involve a high degree of inter-vehicular communication. In this work, a task allocation framework for networked AUVs that participate as a team to accomplish critical missions is developed. The team formed as a result of this task allocation framework is the subset of all deployed AUVs that is best suited to accomplish the mission while adhering to the mission constraints. Research specific to this area has been limited, hence a task allocation framework for networked AUVs to accomplish critical missions is proposed.

In Wireless Sensor and Actor Networks (WSANs), the collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while actors collect and process sensor data and perform appropriate actions. WSANs can be thought of as a distributed control system that needs to timely react to sensor information with an effective action. In this paper, coordination and communication problems in WSANs with mobile actors are studied. First, a new location management scheme is proposed to handle the mobility of actors with minimal energy expenditure for the sensors, based on a hybrid strategy that includes location updating and location prediction. Actors broadcast location updates limiting their scope based on Voronoi diagrams, while sensors predict the movement of actors based on Kalman filtering of previously received updates. The location management scheme enables efficient geographical routing, and based on this, an optimal energy-aware forwarding rule is derived for sensor-actor communication. Consequently, algorithms are proposed that allow controlling the delay of the data-delivery process based on power control, and deal with network congestion by forcing multiple actors to be recipients for traffic generated in the event area. Finally, a model is proposed to optimally assign tasks to actors and control their motion in a coordinated way to accomplish the tasks based on the characteristics of the events. Performance evaluation shows the effectiveness of the proposed solution.

Underwater multimedia acoustic sensor networks will enable new underwater applications such as multimedia coastal and tactical surveillance, undersea explorations, picture and video acquisition and classification, and disaster prevention. Because of the different application requirements, there is a need to provide differentiated-service support to delay-sensitive and delay-tolerant data traffic as well as to loss-sensitive and loss-tolerant traffic. While research on underwater communication protocol design so far has followed the traditional layered approach originally developed for wired networks, improved performance can be obtained with a cross-layer design. Hence, the objective of this work is twofold: (1) study the interactions of key underwater communication functionalities such as modulation, forward error correction, medium access control, and routing; and (2) develop a distributed cross-layer communication solution that allows multiple devices to efficiently and fairly share the bandwidth-limited high-delay underwater acoustic medium.

Underwater Acoustic Sensor Networks (UW-ASNs) consist of devices with sensing, processing, and communication capabilities that are deployed underwater to perform collaborative monitoring tasks to support a broad range of applications. The enabling communication technology for distances over one hundred meters is wireless acoustic networking because of the high attenuation and scattering affecting radio and optical waves, respectively. In this work, the problem of data gathering is investigated by considering the interactions between the routing functions and the characteristics of the underwater acoustic channel. Two distributed geographical routing algorithms for delay-insensitive and delay-sensitive applications are proposed and shown through simulation experiments to meet the application requirements.

Significant research has been done on recognizing the daily activities using acceleration data but few works have focused on classifying the movements comprising an activity due to the shorter time scales of the movements compared to that of an activity. Recognizing the individual movements within an activity can help improve the activity recognition on the whole by using the extra information from the movement granularity. Also, for many applications such as rehabilitation, sports medicine, geriatric care, and health/fitness monitoring the importance of movement recognition cannot be overlooked. Hence, in this paper a novel machine learning algorithm using body area networks is proposed that can on the fly, jointly classify the type of movements, and starting and finishing instant of each movement within an activity. A case study on the best set of features and minimum number of accelerometers needed to correctly classify movements within a smoking activity is also presented.

UnderWater Acoustic Sensor Networks (UW-ASNs) consist of sensors and Autonomous Underwater Vehicles (AUVs) performing collaborative monitoring tasks. In this article, UWMAC, a distributed Medium Access Control (MAC) protocol designed for UW-ASNs, is introduced. The proposed MAC protocol is a transmitter-based Code Division Multiple Access (CDMA) scheme that incorporates a novel closed-loop distributed algorithm to jointly set the optimal transmit power and code length. CDMA is the most promising physical layer and multiple access technique for UW-ASNs because it is robust to frequency-selective fading, it compensates for the effect of multipath at the receiver, and it allows receivers to distinguish among signals simultaneously transmitted by multiple devices. UW-MAC aims at achieving three objectives, i.e., guarantee i) high network throughput, ii) low channel access delay, and iii) low energy consumption. It is demonstrated that UW-MAC simultaneously achieves these three objectives in deep water communications (where the ocean depth is more than 100 m), which are usually not severely affected by multipath. In shallow water communications, which may be heavily affected by multipath, it dynamically finds the optimal trade-off among these objectives according to the application requirements. UW-MAC is the first protocol that leverages CDMA properties to achieve multiple access to the scarce underwater bandwidth, while other protocols tailored for this environment have considered CDMA merely from a physical layer perspective. Experiments show that UW-MAC outperforms many existing MAC protocols tuned for the underwater environment under different architecture scenarios and simulation settings.

Underwater multimedia acoustic sensor networks will enable new underwater applications such as multimedia coastal and tactical surveillance, undersea explorations, picture and video acquisition and classification, and disaster prevention. Because of the different requirements of these applications, it is needed to provide efficient differentiated-service support to delay-sensitive and delay-tolerant data traffic as well as to loss-sensitive and loss-tolerant traffic. The objective of this paper is twofold: 1) explore the interactions of different underwater communication functionalities such as modulation, forward error correction, medium access control and routing, and 2) develop a distributed cross-layer solution integrating specialized communication functionalities that cooperate to allow multiple devices to efficiently and fairly share the bandwidth-limited high-delay underwater acoustic medium.

In this paper, coordination and communication problems in wireless sensor and actor networks (WSANs) are jointly addressed in a unifying framework. A sensor-actor coordination model is proposed based on an event-driven partitioning paradigm. Sensors are partitioned into different sets, and each set is constituted by a data-delivery tree associated with a different actor. The optimal solution for the partitioning strategy is determined by mathematical programming, and a distributed solution is proposed. In addition, a new model for the actor-actor coordination problem is introduced. The actor coordination is formulated as a task assignment optimization problem for a class of coordination problems in which the area to be acted upon needs to be optimally split among different actors. An auction-based distributed solution of the problem is also presented. Performance evaluation shows how global network objectives, such as compliance with real-time constraints and minimum energy consumption, can be achieved in the proposed framework with simple interactions between sensors and actors that are suitable for large-scale networks of energy-constrained devices.

This paper deals with IP traffic engineering (TE) for multipath selection in MPLS networks. A centralized and a distributed routing algorithms are proposed, which aggregate IP flows entering the MPLS domain, and optimally partition them among virtual flows that are forwarded on multiple paths according to their quality of service (QoS) requirements. The virtual-flow multipath routing problem is formulated as a multicommodity network flow (MCNF) problem, and is solved by implementing on-line the Dantzig-Wolfe decomposition method, which is proven to converge to the optimal solution through an iterative procedure that divides the complex optimization problem into a tractable subproblem. The proposed multipath algorithms are shown to outperform single-path routing solutions by means of extensive simulation experiments.

Underwater sensor networks consist of sensors and vehicles deployed to perform collaborative monitoring tasks over a given region. Underwater sensor networks will find applications in oceano-graphic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, tactical surveillance, and mine reconnaissance. Underwater acoustic networking is the enabling technology for these applications. In this paper, an architecture for three-dimensional underwater sensor networks is considered, and a model characterizing the acoustic channel utilization efficiency is introduced, which allows investigating some fundamental characteristics of the underwater environment. In particular, the model allows setting the optimal packet size for underwater communications given monitored volume, density of the sensor network, and application requirements. Moreover, the problem of data gathering is investigated at the network layer by considering the cross-layer interactions between the routing functions and the characteristics of the underwater acoustic channel. Two distributed routing algorithms are introduced for delay-insensitive and delay-sensitive applications. The proposed solutions allow each node to select its next hop, with the objective of minimizing the energy consumption taking the varying condition of the underwater channel and the different application requirements into account. The proposed routing solutions are shown to achieve the performance targets by means of simulation.

In wireless sensor and actor networks (WSANs), the collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while actors collect and process sensor data and perform appropriate actions. In this paper, the coordination and communication problems in WSANs with mobile actors are studied. A hybrid location management scheme is introduced to handle the mobility of actors with minimal energy expenditure. Actors broadcast location updates limiting their scope based on Voronoi diagrams, whereas sensors predict the movement of actors based on Kalman filtering of previously received updates. An optimal energy-aware forwarding rule is then derived for sensor-actor communication, based on geographical routing. The proposed scheme allows controlling the delay of the data-delivery process based on power control, and deals with network congestion by forcing multiple actors to be recipients for traffic generated in the event area. The motion of actors is coordinated to optimally accomplish the tasks based on the characteristics of the events

Wireless Sensor and Actor Networks (WSANs) are composed of a large number of heterogeneous nodes called sensors and actors. The collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while the role of actors is to collect and process sensor data and perform appropriate actions.In this paper, a coordination framework for WSANs is addressed. A new sensor-actor coordination model is proposed, based on an event-driven clustering paradigm in which cluster formation is triggered by an event so that clusters are created on-the-fly to optimally react to the event itself and provide the required reliability with minimum energy expenditure. The optimal solution is determined by mathematical programming and a distributed solution is also proposed. In addition, a new model for actor-actor coordination is introduced for a class of coordination problems in which the area to be acted upon is optimally split among different actors. An auction-based distributed solution of the problem is also presented.Performance evaluation shows how global network objectives, such as compliance with real-time constraints and minimum energy consumption, can be reached in the proposed framework with simple interactions between sensors and actors that are suitable for large-scale networks of energy-constrained devices.

Since ad hoc and sensor networks can be composed of a very large number of devices, the scalability of network protocols is a major design concern. Furthermore, network protocols must be designed to prolong the battery lifetime of the devices. However, most existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. In a geographical routing algorithm, data packets are forwarded by a node to its neighbor based on their respective positions. The neighborhood of each node is constituted by the nodes that lie within a certain radio range. Thus, from the perspective of a node forwarding a packet, the next hop depends on the width of the neighborhood it perceives. The analytical framework proposed in this paper allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. A wider topology knowledge may improve the energy efficiency of the routing tasks but increases the cost of topology information due to signaling packets needed to acquire this information. The problem of determining the optimal topology knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is shown that the problem is intrinsically localized, i.e., a limited topology knowledge is sufficient to make energy efficient forwarding decisions. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover, a new forwarding scheme, partial topology knowledge forwarding (PTKF), is introduced, and shown to outperform other existing schemes in typical application scenarios. A probe-based distributed protocol for knowledge range adjustment (PRADA) is finally introduced that allows each node to efficiently select online its topology knowledge range. PRADA is shown to rapidly converge to a near-optimal solution.

In this work DIMRO, an efficient algorithm to build source specific multicast trees, is presented. DIMRO aims at achieving a high traffic balance in the network in order to avoid bandwidth bottlenecks and consequent network partitions, one of the main causes for low network performance. To do so, it computes multicast trees by dynamically selecting the least loaded available paths, obtaining an optimal distribution of network resources. Strictly integrated with the DiffServ Quality of Service (QoS) approach, the proposed multirate native multicast algorithm maps the QoS service requested by receivers into the proper DiffServ class, so as to respect the expected QoS requirements. The results are a better leverage of the network bandwidth resources, an improved QoS perceived by multicast group members, and time and resource saving due to its low computational complexity, as shown through extensive C++ based simulation campaign.

Since sensor networks can be composed of a very large number of nodes, the developed protocols for these networks must be scalable. Moreover, these protocols must be designed to prolong the battery lifetime of the nodes. Typical existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. For this reason, a novel analytical framework is introduced. In a geographical routing algorithm, the packets are forwarded by a node to its neighbor based on their respective positions. The proposed framework allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover partial topology knowledge forwarding, a new forwarding scheme, is introduced. A wider topology knowledge can improve the energy efficiency of the routing tasks but can increase the cost of topology information due to signaling packets that each node must transmit and receive to acquire this information, especially in networks with high mobility. The problem of determining the optimal knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is demonstrated that the problem is intrinsically localized, i.e., a limited knowledge of the topology is sufficient to take energy efficient forwarding decisions, and that the proposed forwarding scheme outperforms the others in typical application scenarios. For online solution of the problem, a probe-based distributed protocol which allows each node to efficiently select its topology knowledge, is introduced and shown to converge to a near-optimal solution very fast.

The integration of a terrestrial UMTS network with a satellite platform represents one of the most attractive proposals to develop a functional Virtual Home UMTS environment for a generic mobile user provided with an "ad-hoc" dual mode terminal, which will be able to exploit the two integrated systems (hereafter referred to as "segments") for a set of innovative multimedia services. At this purpose, the IST research project "FUTURE" (Functional UMTS Real Emulator) aims at studying and implementing an integrated Satellite and Terrestrial UMTS (S-UMTS, T-UMTS) demonstrator in order to develop new multimedia services provided "anywhere for everyone", targeting, at the same time, to an efficient exploitation of the satellite transmission capabilities. This paper deals with the implementation and the demonstration of new services, based on the adoption of the SIP (Session Initiation Protocol), in the overall FUTURE scenario.