Due to the combination of constrained power, low duty cycle, and high mobility, neighbor discovery is one of the most challenging problems in wireless sensor networks. Existing discovery designs can be divided into two types: pairwise-based and group-based. The former schemes suffer from high discovery delay, while the latter ones accelerate the discovery process but increase transmission package size or incur too much energy overhead, far from practical. Guided by the Talk More Listen Less (TMLL) principle (published in 2016), in which beacons are not necessarily placed in the wakeup slots, we propose two different versions of a group-based protocol we called Talk Half Listen Half (THLH). For the first time, a group-based protocol uses the Channel Occupancy Rate (COR), one of the fundamental novel components of the TMLL model, for performance improvements, in the same way, Duty Cycle (DC) was used in previous group-based protocols. Both versions of the protocol use low transmission overhead in comparison with previous group-based discoveries. After analyzing pros and cons of each approach, we arrived at the conclusion that both behave the best for networks where the average number of new neighbors per slot (β) is low, a metric that sets the bases for performance comparisons of any current/future work with variable COR usage. We also derived a formula that links this new metric with the worst case avg. COR usage of our proposed protocols. Finally, simulation results show that our protocol can improve the average discovery latency and worst case latency close to 50% given low β values.

In the multiratecasting problem in wireless sensor networks, the source sensor is usually required to report to multiple destinations at dif- ferent rates for each of them. We present a MST-based rate-aware cost-efficient multiratecast routing protocol (MSTRC). The proposed MSTRC examines only one set partition of destinations at each for- warding step. A message split occurs when the locally-built minimum spanning tree (MST) over the current node and the set of destina- tions has multiple edges originated at the current node. Destinations spanned by each of these edges are grouped together, and for each of these subsets the best neighbor is selected as the next hop. We also suggested a novel face recovery mechanism to deal with void ar- eas, when no neighbor provides positive progress toward destinations. It constructs a MST of current node and destinations without the progress via neighbors, and for each set partition of destinations cor- responding to an edge e in MST, the face routing keeps going until a node that is closer to one of these destinations is found, allowing for greedy continuation, while the process repeats for the remaining desti- nations similarly. Our experimental results demonstrate that MSTRC is highly rate-efficient in all scenarios, and unlike existing solutions, it is adaptive to destination rate deviations.

Future and next-generation wireless networks are driven by the rapidly growing wireless traffic stemming from diverse services and applications, such as the Internet-of-Things (IoT), virtual reality, autonomous vehicles, and smart intersections. Many of these applications require massive connectivity between IoT devices as well as wireless access links with ultra-high bandwidth (Gbps or above) and ultra-low latency (10ms or less). Therefore, realizing the vision of future wireless networks requires significant research efforts across all layers of the network stack. In this thesis, we use a cross-layer approach and focus on several critical components of future wireless networks including IoT systems and full-duplex (FD) wireless, and on experimentation with advanced wireless technologies in the NSF PAWR COSMOS testbed. First, we study tracking and monitoring applications in the IoT and focus on ultra-low-power energy harvesting networks. Based on realistic hardware characteristics, we design and optimize Panda, a centralized probabilistic protocol for maximizing the neighbor discovery rate between energy harvesting nodes under a power budget. Via testbed evaluation using commercial off-the-shelf energy harvesting nodes, we show that Panda outperforms existing protocols by up to 3x in terms of the neighbor discovery rate. We further explore this problem and consider a general throughput maximization problem among a set of heterogeneous energy-constrained ultra-low-power nodes. We analytically identify the theoretical fundamental limits of the rate at which data can be exchanged between these nodes, and design the distributed probabilistic protocol, EconCast, which approaches the maximum throughput in the limiting sense. Performance evaluations of EconCast using both simulations and real-world experiments show that it achieves up to an order of magnitude higher throughput than Panda and other known protocols. We then study FD wireless - simultaneous transmission and reception at the same frequency - a key technology that can significantly improve the data rate and reduce communication latency by employing self-interference cancellation (SIC). In particular, we focus on enabling FD on small-form-factor devices leveraging the technique of frequency-domain equalization (FDE). We design, model, and optimize the FDE-based RF canceller, which can achieve >50dB RF SIC across 20MHz bandwidth, and experimentally show that our prototyped FD radios can achieve a link-level throughput gain of 1.85-1.91x. We also focus on combining FD with phased arrays, employing optimized transmit and receive beamforming, where the spatial degrees of freedom in multi-antenna systems are repurposed to achieve wideband RF SIC. Moving up in the network stack, we study heterogeneous networks with half-duplex and FD users, and develop the novel Hybrid-Greedy Maximum Scheduling (H-GMS) algorithm, which achieves throughput optimality in a distributed manner. Analytical and simulation results show that H-GMS achieves 5-10x better delay performance and improved fairness compared with state-of-the-art approaches. Finally, we described experimentation and measurements in the city-scale COSMOS testbed being deployed in West Harlem, New York City. COSMOS' key building blocks include software-defined radios, millimeter-wave radios, a programmable optical network, and edge cloud, and their convergence will enable researchers to remotely explore emerging technologies in a real world environment. We provide a brief overview of the testbed and focus on experimentation with advanced technologies, including the integrating of open-access FD radios in the testbed and a pilot study on converged optical-wireless x-haul networking for cloud radio access networks (C-RANs). We also present an extensive 28GHz channel measurements in the testbed area, which is a representative dense urban canyon environment, and study the corresponding signal-to-noise ratio (SNR) coverage and achievable data rates. The results of this part helped drive and validate the design of the COSMOS testbed, and can inform further deployment and experimentation in the testbed. In this thesis, we make several theoretical and experimental contributions to ultra-low-power energy harvesting networks and the IoT, and FD wireless. We also contribute to the experimentation and measurements in the COSMOS advanced wireless testbed. We believe that these contributions are essential to connect fundamental theory to practical systems, and ultimately to real-world applications, in future wireless networks.

Applications for Wireless Sensor Networks can be updated dynamically by means of wireless upgrade mechanisms. Current research efforts in wireless upgrade mechanisms for WSN have focused on transmitting application packets for upgrades via wireless medium. However, these schemes require significant overhead involved in sending and receiving application packets that affect the sensor operation, in addition to bringing the nodes down to reprogram and restart them. By designing applications in a way that allows dynamic functionality changes during operation, the overhead and sensor delays can be eliminated. Dynamically Controllable Application (DCA) is a novel scheme for designing WSN applications whose behavior can be rapidly and dynamically changed during operation. The results indicate that a veritable functionality change is achieved in a span of a few milliseconds.

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As wireless sensor platforms become less expensive and more powerful, the promise of their wide-spread use for everything from health monitoring to military sensing continues to increase. Like other networks, sensor networks are vulnerable to malicious attack; however, the hardware simplicity of these devices makes defense mechanisms designed for traditional networks infeasible. This work explores the denial-of-sleep attack, in which a sensor node's power supply is targeted. Attacks of this type can reduce sensor lifetime from years to days and can have a devastating impact on a sensor network. This work identifies vulnerabilities in state-of-the-art sensor network medium access control (MAC) protocols that leave them susceptible to denial-of-sleep attack. It then classifies these attacks in terms of an attacker's knowledge of the MAC layer protocol and ability to bypass authentication and encryption protocols. Attacks from each category in the classification are modeled to show the impacts on four current sensor network MAC protocols: S-MAC, T-MAC, B-MAC and G-MAC. To validate the effectiveness and analyze the efficiency of the attacks, implementations of selected attacks on S-MAC and T-MAC are described and analyzed in detail. This research goes on to introduce a suite of mechanisms designed to detect and mitigate the effects of denial-of-sleep attacks on sensor networks. The Clustered Anti Sleep-Deprivation for Sensor Networks, or Caisson, suite includes a lightweight, platform-independent anti-replay mechanism, an adaptive rate-limiter and a jamming detection and mitigation mechanism. These tools are designed to be applied selectively or in concert to defend against denial-of-sleep attacks depending on the specific vulnerabilities in the MAC protocol used in a particular sensor network deployment. This work makes two major contributions to state-of-the-art wireless sensor network research. First, it fully explores the denial-of-sleep attack, to include the implementation of a subset of these attacks on actual sensor devices and an analysis of the efficiency of these attacks. Second, it provides a set of tools by which these attacks are detected and defeated in a lightweight, platform-independent, and protocol-independent way. If sensor networks are to live up to current expectations, they must be robust in the face of newly emerging network attacks, to include denial-of-sleep. / Ph. D.