Wireless Mesh Networks (WMNs) promise to offer robust, reliable, and economically scalable communication, surpassing traditional wired and wireless network infrastructures across diverse applications. Currently, most of the active research in this field often relies on simulations for validating new theories, due to cost constraints associated with physical setups. However, simulation software typically features simplified hardware abstractions that limit extensive configurability and fail to mirror real-world performance accurately. This paper addresses the need for a robust WMN testbed capable of overcoming these challenges. Our framework complements simulation results by facilitating practical, real-world experiments. Specifically, we present a versatile testbed framework supporting configurations such as Multi-Radio Multi-Channel, Multi-Radio Single-Channel, and Single-Radio Single-Channel mesh interfaces. Leveraging Commercial off-the-shelf (COTS) computing devices, our setup emphasizes reproducibility and scalability. Key features include automation of network setup to ensure reproducibility, telemetry collection and storage for network performance analysis, and a web-based dashboard for visualizing network topology and key performance metrics. This demonstration aims to reduce the time and effort required to establish a WMN testbed, thereby benefiting the scientific community at large.

Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or communication patterns. This work introduces a deep-learning-based approach for recognizing and identifying UAVs using their RF emissions. Captured RF signals are transformed into spectrograms, which are subsequently analyzed using deep neural networks. Existing methods achieve low identification accuracy, for instance the ResNet-50V2 model achieves an accuracy of 85.39% even in controlled, laboratory, noise-free conditions. Moreover, in outdoor environments at distances of 50m and 100m, the accuracy drops to 68.90% and 56.88%, respectively. To improve classification accuracy in outdoors, a CNN model was developed, yielding an accuracy of 78.12%. Leveraging the ResNet 50 V2 architecture, remarkable accuracy of 95.08% was attained in binary classification tasks involving a dataset comprising 195 mixed UAV images and 290 non-mix UAV images.

Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. This rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF systems today. The continued crowding of the RF spectrum makes RFI efficient and lightweight mitigation critical. Detecting and localizing the interfering signals is the foremost step for mitigating RFI concerns. Addressing these challenges, we propose a novel and lightweight approach, namely RaFID, to detect and locate the RFI by incorporating deep neural networks (DNNs) and statistical analysis via batch-wise mean aggregation and standard deviation (SD) calculations. RaFID investigates the generation of an expected signal using DNNs within the time domain. We performed the statistical analysis to compare our generated expected signal with the received signal to detect the existence of interference and determine interference frequency. Experimental results show that signal estimation is accurate, with a mean squared error of 0.012 and an average run-time of 0.5 seconds.

Traditional power-based localization methods suffer from low accuracy in the practical application environment. The main challenges are the antenna directivity and fading effect. Conventional methods assume omnidirectional antenna directivity such that the solution is the intersections of multiple circle-shape contours. This strong assumption results in significant localization error in practical non-isotropic antenna applications. In this article, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) scheme is proposed. It reduces the antenna directivity effect by including the antenna pattern into the localization system model. It reduces the bias error that introduced by power measurement through estimating the line-of-sight (LoS) component in received signal strength (RSS). Moreover, the error mode for the proposed ARPAP system, along with the theoretical limit, Cramer-Rao Bound (CRB), and bias of the proposed positioning system are derived. The Pearson correlation coefficient between the proposed error model and simulation result shows a high similarity score. The proposed positioning scheme and analytic error model are instantiated for the cellular network. Both analytical model and simulation results demonstrate the superiority of the proposed method over traditional methods.

The existing data service providers have gaps in their connectivity, especially in disaster and remote underserved areas. A mesh network can address this shortcoming by providing Internet access to devices outside the coverage area with the help of other nearby devices. Mesh networks are homogeneous using a single interface across the network and require large-scale deployment of proprietary devices. We propose to build a heterogeneous mesh network using various interfaces such as Wi-Fi, Bluetooth, and cellular data services to connect devices. These interfaces are available in devices such as smartphones, laptops, and home automation devices and have the potential to be a convergence device. We present an Android application enabling devices with various interfaces to become convergence devices and build heterogeneous mesh networks to address the shortcoming of the existing wireless ISP services. We target this prototype implementation to provide a tactical communication link in disaster zones, enabling the public to build the network and send a life-critical distress signal calling for help. The proposed implementation achieved an average throughput of 1 Mbps with multiple hops and through Wi-Fi, Bluetooth, and cellular data service.

The explosive expansion of collected data - in terms of dimensionality, diversity, volume - increases more rapidly than we can analyze to draw useful conclusions, make informed decisions, and provide specific recommendations. Various fields such as medical, healthcare, aviation, telecommunication require new tools to process the data which they collect to process effectively and economically and benefit from the estimated quantities that were learned from the data itself. In particular, there are different methodologies proposed and used in telecommunications to estimate the channel coefficients of different types of channels. All these methodologies are grounded based on the assumption of the statistical property of the channel. However, a flexible solution that can dynamically deploy different methods based on the received signal yields higher performance and maintained over time. In this paper, we propose to apply targeted learning and explore the suitable parameters for a communication system. The initial results demonstrate it is possible to distinguish and identify the best methodology to fit the current channel conditions.

In this paper, bias and CRB analysis for both proposed line-of-sight (LoS) and conventional radio-signal-strength (RSS) based positioning methods are derived. In contrast to the conventional lognormal based analysis, the derived error models take into account the environmental multipath fading effect. The proposed LoS-based scheme improves positioning accuracy by eliminating the effect of non-range dependent portion of powers from the received signal. It suppresses non-line-of-sight (NLoS) disturbance to the positioning result through estimating the LoS component from fading signal. Hence, it is robust to environmental fading disturbance. The derived error models are validated by simulation. Both analytical model and simulation results show that the accuracy and robustness of the proposed LoS-based positioning are superior to that of the conventional RSS-based methods.

In this paper, a cost efficient fusion scheme, Ubiquitous Tracking with Motion and Location Sensor (UTMLS), is proposed for the accurate localization and tracking in mixed GPS-friendly, GPS-challenging, and GPS-denied scenario. The proposed drift-reduction method in UTMLS addresses the cumulating error issue in the indoor tracking with the consumer grade motion sensor. The proposed hypothesis test method in UTMLS improves the tracking sensor fusion precision by detecting distorted GPS reports and intelligently switching between GPS and inertial sensor based schemes. The proposed scheme is instantiated and implemented on an Android smartphone platform. Experiments have been conducted to evaluate and validate the accuracy. Experimental results show that (1) the proposed drift-reduction method effectively suppresses the non-GPS tracking error accumulation due to the integration of acceleration noise with time (2) UTMLS realizes robust indoor/outdoor seamless tracking, preventing GPS fault estimates introduced tracking error in the conventional Kalman filtering process.

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.

Cyber-Physical Systems (CPS) consist of computational components interconnected by computer networks that monitor and control switched physical entities interconnected by physical infrastructures. A fundamental challenge in the design and analysis of CPS is the lack of common semantics across the components. We address this challenge by employing a novel approach that composes the correctness of various components instead of their functionality using a conjunction of non-interfering logical invariants. We present a distributed algorithm that uses this approach to adaptively schedule power transfers between nodes in a smart power grid in such a way that the stability of both the computer network and the physical system are maintained. Simulation results demonstrate the necessity and usefulness of our approach in maintaining overall system stability in the presence of uncertainties in the computer network and with limited information about the global state of the system.

The passive backscatter technology is increasingly employed in wireless sensor network (WSN) applications. In contrast to existing technologies it offers several advantages including a battery-less operation, extremely small-form factor, and low costs. Our previous works demonstrated that theoretically such backscatter-based network outperforms existing solutions. In contrast, this paper presents a new time division multiple access (TDMA) based approach that considers a practical realization of the multi-hop framework. The capacity limits for the proposed scheme are evaluated for two frameworks - (a) single hop with direct backscatter signal and (b) a multi-hop transmission. An analytical comparison shows that the multi-hop transmission improves the per-node capacity over the single-hop scenario. Numerical simulations verify the theoretical conclusions.

Traditionally wireless sensor networks (WSNs) employ active RF communication where the RF signal has to be generated at nodes, thus consuming a significant amount of energy. In contrast, employment of passive communication, where nodes backscatter a modulated version of the incident RF signal, offers great advantages including reducing the energy cost of communication. Existing example includes a traditional RFID system with active reader communicating directly with passive tags. However, the proposed approach targets future, more advanced systems that support a generic backscatter communication. However, the inherent asymmetry in communication, reliance on external source of RF signal, and limited energy storage renders the traditional WSN schemes inadequate. Consequently, a novel multi-hop framework for passive backscattering communication is presented and analyzed in this paper. This includes theoretical and simulation study of communication capacity and discussion of performance optimization potentials for routing schemes.

In this paper, two integrated schemes for fair capacity allocation and power control for ad hoc wireless network are presented. The game theory approach is utilized to derive a collaborative update law. In the practical wireless network, a non-uniform distribution of nodes results in varying degree of interference among the network links or hops. Such varying mutual interference may lead to unfair allocation of bandwidth and result in network congestion. As a result some links may become bottlenecks for the entire network. Hence, it is important to fairly allocate resources in the entire network. The proposed schemes aim to address it by providing equal bandwidth to every link in ad hoc wireless network. Consequently, all node pairs should maintain the same Signal-to-Interference Ratio (SIR). While for small, sparse network the solution is easily attained with power control alone, in a congested network with clusters of nodes the commonly achievable SIR level is unknown and has to be dynamically calculated. The simulation shows that with the same initial power, a network converges to the same SIR value that guarantees fairness.

Radio frequency spectrum in wireless communication is a common resource shared by all collocated devices. Most spectrum management schemes rely on a centralized base station-receiver station relaying architecture. In contrast, infrastructure-less systems including wireless ad hoc networks require a scalable, distributed methodology. Moreover, the access has to be coordinated among the nodes in the network in the aspects which include power and rate adaptation. The challenge is to find an optimal tradeoff between such parameters not only for a particular link but also for an entire multi-hop network. Several existing schemes address power, throughput and Signal-to-Interference ratio (SIR) control on a link level. In contrast, the proposed work performs optimization for the entire network. The game theory methodology is employed to solve the distribution of the radio resources while the adaptive power and rate control estimates the required but unknown radio interface parameters online. Moreover, the adaptive power and rate control scheme optimizes the local channel access. The resulting spectrum channel management scheme becomes a hybrid of adaptive controls and game theory approaches that guarantees a fair and efficient channel sharing and maximizes the overall performance. The results are shown analytically and through simulations.

Typical wireless multi-hop networks employ active communication where RF signal is actively generated by a sending node. In contrast, backscatter communication modulates backscattered RF signal that requires significantly less energy to operate than the active one. This paper proposes a novel, multi-hop architecture for such backscatter-based communication that is not currently present in the literature. Such a system consists of a powerful reader and small, battery-less nodes. The reader generates the original RF signal, while the passive devices backscatter this signal to communicate among themselves thus enabling multihop transmission from far-away nodes. The paper presents the generic analysis of the system capabilities and demonstrates the feasibility of such multi-hop network. Also, it studies the connectivity models in terms of k-connectivity of such network with uniformly distributed tags.

This paper proposes multi-key encryption scheme and engine architecture (MKE) that increases security and optimizes energy efficiency of sensor networks, while minimizing modifications to existing implementations. The scheme improves security of AES against correlation power analysis (CPA) attack by employing MKE engine, breaking the correlation between power consumption and the used key. Other schemes utilize complex hardware designs, for example by using the inhomogeneous s-boxes that reduce energy efficiency of the engine. In contrast, the proposed hardware engine uses a randomly sequence of few keys to encode subsequent blocks of a messages. Additionally, the scheme improves security of AES against brute-force attacks for a given key size by utilizing multiple keys to encrypt subsequent blocks of a message. In contrast, a typical security upgrade would require a larger key size and encryption engine, which would increase cost and energy consumption of the devices. Both analytical and simulation results are presented in this paper.

Multiple non-interfering channels are available in 802.11 and 802.15.4 based wireless networks. Capacity of such channels can be combined to achieve a better performance thus providing a higher quality of service (QoS) than for a single channel network. However, existing routing protocols often are not suited to fully take advantage of these channels. The proposed multi-interface multi-channel routing (MMCR) protocol considers various QoS parameters such as throughput, end-to-end delay, and energy utilization as a single unified cost metric and identifies the route that optimizes the cost metric and balances the traffic among the channels on a per flow basis. Multipoint relay nodes (MPRs) are first selected using available energy and bandwidth and utilized in routing. A novel load balancing scheme is introduced and analytical performance guarantees are demonstrated. Simulation results using the Ns2 show superior performance of the MMCR over the multi-channel optimal link state routing protocol (m-OLSR) in terms of throughput end-to-end delay, and energy efficiency.

The bandwidth utilization of a single channel-based wireless networks decreases due to congestion and interference from other sources and therefore transmission on multiple channels are needed. In this paper, we propose a distributed dynamic channel allocation scheme for wireless networks using adaptive learning automata whose nodes are equipped with single radio interfaces so that a more suitable channel can be selected. The proposed scheme, adaptive pursuit reward-inaction, runs periodically on the nodes, and adaptively finds the suitable channel allocation in order to attain a desired performance. A novel performance index, which takes into account the throughput and the energy consumption, is considered. The proposed scheme is adaptive in the sense that probabilities in the each step are updated as a function of the error in the performance index. The extensive simulation results in static and mobile environments provide that using the proposed scheme for channel allocation in the multiple channel wireless networks significantly improves the throughput, drop rate, energy consumption per packet and fairness index.

In this work, an inexpensive electric utilities monitoring solution using wireless sensor networks is demonstrated that can easily be installed, deployed, maintained and eliminate unnecessary energy costs and effort. The monitoring solution is designed to support network enabled manufacturing (NEM) program using Missouri University of Science and Technology (MST), formerly the University of Missouri-Rolla (UMR), motes.

No abstract available.

In this paper, we propose a subnetwork key management strategy in which the heterogeneous security requirements of a wireless sensor network are considered to provide differing levels of security with minimum communication overhead. Additionally, it allows the dynamic creation of high security subnetworks within the wireless sensor network and provides subnetworks with a mechanism for dynamically creating a secure key using a novel and dynamic group key management protocol. The proposed energy-efficient protocol utilizes a combination of pre-deployed group keys and initial trustworthiness of nodes to create a level of trust between neighbors in the network. This trust is later used to allow secure communication between neighbors when creating a dynamic, high security subnetwork within the sensor network. Results of simulations of the protocol in Ns2 are presented and the complexity of the protocol is analyzed. The proposed protocol reduces delay by 50% and energy consumption by 70% over the existing dynamic group key management (DGKM) scheme.

Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks, result in a large number of packet drops, unfair scenarios and low throughputs with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, this paper presents a novel, decentralized, predictive congestion control (DPCC) for wireless sensor networks (WSN). The DPCC consists of an adaptive flow and adaptive back-off interval selection schemes that work in concert with energy efficient, distributed power control (DPC). The DPCC detects the onset of congestion using queue utilization and the embedded channel estimator algorithm in DPC that predicts the channel quality. Then, an adaptive flow control scheme selects suitable rate which is enforced by the newly proposed adaptive backoff interval selection scheme. An optional adaptive scheduling scheme updates weights associated with each packet to guarantee the weighted fairness during congestion. Closed-loop stability of the proposed hop-by-hop congestion control is demonstrated by using the Lyapunov-based approach. Simulation results show that the DPCC reduces congestion and improves performance over congestion detection and avoidance (CODA) [3] and IEEE 802.11 protocols.

Resource constraints require that ad hoc wireless networks are energy efficient during transmission and rate adaptation. In this paper we propose a novel cross-layer energy-efficient rate adaptation scheme that employs dynamic programming (DP) principle to analytically select the modulation scheme online. The scheme uses channel state information from the physical layer and congestion information from the scheduling layer to select a modulation rate. This online selection maximizes throughput while saving energy and preventing congestion. The simulation results indicate that an increase in throughput by 96% and energy-efficiency by 131% is observed when compared to the Receiver Based AutoRate (RBAR) protocol

A novel adaptive and distributed fair scheduling (ADFS) scheme for wireless sensor networks is shown through hardware implementation. In contrast to simulation, hardware evaluation provides valuable feedback to protocol and hardware development process. The proposed protocol focuses on quality-of-service (QoS) issues to address flow prioritization. Thus, when nodes access a shared channel, the proposed ADFS allocates the channel bandwidth proportionally to the weight, or priority, of the packet flows. Moreover, ADFS allows for dynamic allocation of network resources with little added overhead. Weights are initially assigned using user specified QoS criteria. These weights are subsequently updated as a function of delay, enqueued packets, flow arrival rate, and the previous packet weight. The back-off interval is also altered using the weight update equation. The weight update and the back-off interval selection ensure that global fairness is attained even with variable service rates. The algorithm is implemented using UMR/SLU motes for an industrial monitoring application. Results the hardware implementation demonstrates improved performance in terms of fairness index, flow rate, and delay.

In this paper, a novel distributed power control (DPC) scheme for cellular network in the presence of radio channel uncertainties such as path loss, shadowing, and Rayleigh fading is presented. Since these uncertainties can attenuate the received signal strength and can cause variations in the received signal-to-interference ratio (SIR), a new DPC scheme, which can estimate the slowly varying channel uncertainty, is proposed so that a target SIR at the receiver can be maintained. Further, the standard assumption of a constant interference during a link's power update used in other works in the literature is relaxed. A CDMA-based cellular network environment has been developed to compare the proposed scheme with earlier approaches. The results show that our DPC scheme can converge faster than others by adapting to the channel variations. In the presence of channel uncertainties, our DPC scheme renders lower outage probability while consuming significantly low power per active mobile user compared with other schemes that are available in the literature.

Resource constraints in ad hoc wireless networks require that they are energy efficient during both transmission and rate adaptation. In this paper, we propose a novel energy-efficient rate adaptation protocol that selects modulation schemes online to maximize throughput based on channel state while saving energy. This protocol uses the distributed power control (DPC) algorithm (M. Zawodniok et al., 2004) to accurately determine the necessary transmission power and to reduce the energy consumption. Additionally, the transmission rate is altered using energy efficiency as a constraint to meet the required throughput, which is estimated with queue fill ratio. Moreover, back-off scheme is incorporated to prevent energy wastage and to avoid retransmissions in the event of congestion. The back-off scheme employs backpressure mechanism to emulate congestion control. Consequently, the nodes will conserve energy when the traffic is low, offer higher throughput when needed and save energy during congestion by limiting transmission.

A novel distributed power control (DPC) scheme and a MAC protocol for wireless ad hoc networks in the presence of radio channel uncertainties such as path loss, Shadowing and Rayleigh fading is presented. The DPC quickly estimates the time-varying nature of the channel and uses the information to select a suitable transmitter power value in order to maintain a target signal-to-interference ratio (SIR) at the receiver. The standard assumption of a constant interference during a link's power update used in other works is relaxed. The performance of the proposed DPC is demonstrated analytically. The power used for all RTS-CTS-DATA-ACK frames is selected using the proposed DPC; hence, energy savings and spatial reuse are achieved. The hidden-terminal problem is overcome by periodically increasing the power. The NS simulator is used to compare the proposed scheme with 802.11. The proposed MAC protocol renders a significant increase in throughput in the presence of channel variations compared with 802.11 while consuming low energy per bit.