Industrial and agricultural Internet of Things (IoT) are emerging in very large-scale and wide-area applications (e.g., oil-field management, smart farming) that may spread over hundreds of square miles (e.g., 45 mi × 12 mi East Texas Oil-field). Although a single Low-Power Wide-Area Network (LPWAN) covers several miles, it faces coverage challenge in such extremely large-area IoT applications, especially in rural or remote areas with no/limited infrastructure, requiring an in-band integration of multiple LPWANs. We consider a seamless integration of multiple SNOW LPWANs. SNOW (Sensor Network Over White spaces) is an LPWAN architecture over the TV white spaces, avoiding overcrowding problems in the limited ISM band and the cost of licensed band and infrastructure. It offers high scalability through concurrent and bi-directional communication between a base station and numerous nodes. Existing integration of multiple SNOW LPWANs does not consider minimizing network latency and is less suitable for delay-sensitive or real-time applications. In this work, we propose thefirst latency-minimizing scalable in-band integration of multiple SNOWs. Considering the impact of bandwidth on latency and base station power dissipation, low-latency integration of multiple SNOWs as a constrained spectrum allocation problem is formulated. A novel greedy latency- and traffic- aware spectrum allocation to allocate each link's bandwidth is proposed, achieving an integrated network. To enable low-latency integration, we propose two medium access control protocols for multiple SNOWs, RI-TDMA and TDMA, and estimate their latency. We have implemented the proposed integration both on SNOW hardware and in NS-3 simulator. The physical experiments show up to 44% reduction in the maximum network latency under our approach compared to existing approach. The simulation results show at least 62.3% reduction of maximum network latency by the proposed approach with RI-TDMA and 86.7% with TDMA.

Today, the audit and diagnosis of the causal relationships between the events in a trigger-action-based event chain (e.g., why is a light turned on in a smart home?) in the Internet of Things (IoT) platforms are untrustworthy and unreliable. The current IoT platforms lack techniques for transparent and tamper-proof ordering of events due to their device-centric logging mechanism. In this article, we develop a framework that facilitates tamper-proof transparency and event order in an IoT platform by proposing a Blockchain protocol and adopting the vector clock system, both tailored for the resource-constrained heterogeneous IoT devices, respectively. To cope with the unsuited storage (e.g., ledger) and computing power (e.g., proof-of-work puzzle) requirements of the Blockchain in the commercial off-the-shelf IoT devices, we propose a partial consistent cut protocol and engineer a modular arithmetic-based lightweight proof of work puzzle, respectively. To the best of our knowledge, this is the first Blockchain designed for resource-constrained heterogeneous IoT platforms. Our event ordering protocol based on the vector clock system is also novel for the IoT platforms. We implement our framework using an IoT gateway and 30 IoT devices. We experiment with ten concurrent trigger-action-based event chains while each chain involves 20 devices, and each device participates in five different chains. The results show that our framework may order these events in 2.5 s while consuming approximately 140 mJ of energy per device. The results, hence, demonstrate the proposed platform as a practical choice for many IoT applications, such as smart home, traffic monitoring, and crime investigation.

Today, industrial and agricultural Internet of Things (IoT) are emerging in very large-scale and wide-area applications (e.g., oil-field management, smart farming) that may spread over hundreds of square miles (e.g., 45mi×12mi East Texas Oil-field). Although a single Low-Power Wide-Area Network (LPWAN) covers several miles, it faces coverage challenge in such extremely large-area IoT applications, specially in rural or remote areas with no/limited infrastructure, requiring an in-band integration of multiple LPWANs. To avoid the crowd in the limited ISM band and the cost of licensed band and infrastructure, SNOW (Sensor Network Over White spaces) is an LPWAN architecture over the TV white spaces. It offers high scalability through concurrent and bi-directional communication between a base station and numerous nodes. We consider a seamless integration of multiple SNOWs. Existing approach does not consider minimizing network latency and is less suitable for delay-sensitive or real-time applications. We propose the first scalable in-band integration of multiple SNOWs that minimizes network latency. By taking into account the impact of bandwidth on latency and base station power dissipation, we formulate lowlatency integration of multiple SNOWs as a constrained spectrum allocation problem. It is solved through a greedy algorithm by analyzing network latency and by adopting a latency- and traffic- aware bandwidth allocation along the links to achieve an integrated network. We have implemented the proposed integration both on SNOW hardware and in NS-3 simulator. Both physical experiments and simulations show a significant reduction (44% and 97%, resp.) in network latency under our approach compared to existing approach.

Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed Sensor Network over White Spaces (SNOW) is a promising LPWAN platform that operates over the TV white spaces. As it is a very recent technology and is still in its infancy, the current SNOW implementation uses the Universal Software Radio Peripheral devices as LPWAN nodes, which has high costs (≈$750 USD per device) and large form-factors, hindering its applicability in practical deployment. In this article, we implement SNOW using low-cost, low form-factor, low-power, and widely available commercial off-the-shelf (COTS) devices to enable its practical and large-scale deployment. Our choice of the COTS device (TI CC13x0: CC1310 or CC1350) consequently brings down the cost and form-factor of a SNOW node by 25× and 10×, respectively. Such implementation of SNOW on the CC13x0 devices, however, faces a number of challenges to enable link reliability and communication range. Our implementation addresses these challenges by handling peak-to-average power ratio problem, channel state information estimation, carrier frequency offset estimation, and near-far power problem. Our deployment in the city of Detroit, Michigan, demonstrates that CC13x0-based SNOW can achieve uplink and downlink throughputs of 11.2 and 4.8 kbps per node, respectively, over a distance of 1 km. Also, the overall throughput in the uplink increases linearly with the increase in the number of SNOW nodes.

Prolonging the network lifetime is a major consideration in many Internet of Things applications. In this paper, we study maximizing the network lifetime of an energy-harvesting LoRa network. Such a network is characterized by heterogeneous recharging capabilities across the nodes that is not taken into account in existing work. We propose a link-layer protocol to achieve a long-lived LoRa network which dynamically enables the nodes with depleting batteries to exploit the superfluous energy of the neighboring nodes with affluent batteries by letting a depleting node offload its packets to an affluent node. By exploiting the LoRa's capability of adjusting multiple transmission parameters, we enable low-cost offloading by depleting nodes instead of high-cost direct forwarding. Such offloading requires synchronization of wake-up times as well as transmission parameters between the two nodes which also need to be selected dynamically. The proposed protocol addresses these challenges and prolongs the lifetime of a LoRa network through three novel techniques. (1) We propose a lightweight medium access control protocol for peer-to-peer communication to enable packet offloading which circumvents the synchronization overhead between the two nodes. (2) We propose an intuitive heuristic method for effective parameter selections for different modes (conventional vs. offloading). (3) We analyze the energy overhead of offloading and, based on it, the protocol dynamically selects affluent and depleting nodes while ensuring that an affluent node is not overwhelmed by the depleting ones. Simulations in NS-3 as well as real experiments show that our protocol can increase the network lifetime up to 4 times while maintaining the same throughput compared to traditional LoRa network.

Low-Power Wide-Area Networks (LPWANs) are evolving as an enabling technology for Internet-of-Things (IoT) due to their capability of communicating over long distances at very low transmission power. Existing LPWAN technologies, however, face limitations in meeting scalability and covering very wide areas which make their adoption challenging for future IoT applications, especially in infrastructure-limited rural areas. To address this limitation, in this paper, we consider achieving scalability and extended coverage by integrating multiple LPWANs. SNOW (Sensor Network Over White Spaces), a recently proposed LPWAN architecture over the TV white spaces, has demonstrated its advantages over existing LPWANs in performance and energy-efficiency. In this paper, we propose to scale up LPWANs through a seamless integration of multiple SNOWs which enables concurrent inter-SNOW and intra-SNOW communications. We then formulate the tradeoff between scalability and inter-SNOW interference as a constrained optimization problem whose objective is to maximize scalability by managing white space spectrum sharing across multiple SNOWs. We also prove the NP-hardness of this problem. To this extent, We propose an intuitive polynomial-time heuristic algorithm for solving the scalability optimization problem which is highly efficient in practice. For the sake of theoretical bound, we also propose a simple polynomial-time 1/2-approximation algorithm for the scalability optimization problem. Hardware experiments through deployment in an area of (25x 15 )km2as well as large scale simulations demonstrate the effectiveness of our algorithms and feasibility of achieving scalability through seamless integration of SNOWs with high reliability, low latency, and energy efficiency.

WirelessHART networks provide the feasibility of achieving real-time performance over wireless through multichannel and graph routing for process monitoring and control applications. However, real-time scheduling theory for Wireless Sensor-Actuator Network (WSAN) is still not well-developed. Besides, the performance of a WSAN induces a complicated problem involving many interrelated objectives and variables, requiring a scheduling-control codesign. This work aims at addressing these challenges. Specifically, we will develop a realtime schedulability analysis for WSAN, and leverage this result to address multiple key challenging problems in wireless Cyber-Physical Systems in the future. Schedulability analysis remains the cornerstone in any real-time system. In WSAN, it is used to determine whether a set of real-time control loops/flows can meet deadlines. It is also used in various scheduling-control codesign, routing, and priority assignment. In this work, we will develop an analysis based on utilization bound. Because of its extremely low runtime overhead, utilization based analysis has been extensively studied in CPU scheduling. However, no work has been done yet on utilization based analysis for multi-hop wireless network. The key challenge arises from transmission conflict and dynamics in wireless. We will address this by characterizing transmission conflict as task blocking in nonpreemptive CPU scheduling, and then by adopting a hierarchical network structure where we will apply the analysis in each subnetwork.

Interference between concurrent transmissions causes severe performance degradation in a wireless network. This paper addresses interference cancellation to enable simultaneous packet receptions at a node with a single radio in Wireless Sensor Networks (WSN). Interference cancellation is particularly important for WSN as most of its applications rely on convergecast where all the traffic in the network is delivered to a base station leading to a lot of packet collisions. Existing solutions for collision recovery make simplified assumptions such as the availability of one of the collided packets, repeated collisions of the same packets, and the ability to identify the collided packets before recovering them which do not hold for WSNs and most wireless networks. In this paper, we propose a novel collision recovery method called Reverse and Replace Decoding (RnR) for WSNs. RnR entails a physical-link layer design to exploit the raw samples of the colliding signals. It does not rely on the assumptions made in existing work, and can recover all packets from a single collision. To demonstrate its feasibility, we have implemented RnR using GNU Radio on USRP devices based on IEEE 802.15.4 network. Our experiments on a 6-node testbed demonstrate that RnR can successfully decode packets in 95% cases of collisions, and improves the correctly packet decoding rate up to 97.5% compared to standard decoders in the case of collisions. Also, our simulation based on GNU Radio simulator using 25 nodes shows that RnR achieves 4x higher throughput compared to the state-of-the-art collision recovery mechanisms.

Low-Power Wide-Area Network (LPWAN) heralds a promising class of technology to overcome the range limits and scalability challenges in traditional wireless sensor networks. Recently proposed Sensor Network over White Spaces (SNOW) technology is particularly attractive due to the availability and advantages of TV spectrum in long-range communication. This paper proposes a new design of SNOW that is asynchronous, reliable, and robust. It represents the first highly scalable LPWAN over TV white spaces to support reliable, asynchronous, bi-directional, and concurrent communication between numerous sensors and a base station. This is achieved through a set of novel techniques. This new design of SNOW has an OFDM based physical layer that adopts robust modulation scheme and allows the base station using a single antenna-radio (1) to send different data to different nodes concurrently and (2) to receive concurrent transmissions made by the sensor nodes asynchronously. It has a lightweight MAC protocol that (1) efficiently implements per-transmission acknowledgments of the asynchronous transmissions by exploiting the adopted OFDM design; (2) combines CSMA/CA and location-aware spectrum allocation for mitigating hidden terminal effects, thus enhancing the flexibility of the nodes in transmitting asynchronously. Hardware experiments through deployments in three radio environments - in a large metropolitan city, in a rural area, and in an indoor environment - as well as large-scale simulations demonstrated that the new SNOW design drastically outperforms other LPWAN technologies in terms of scalability, energy, and latency.

Wireless sensor networks (WSNs) face significant scalability challenges due to the proliferation of wide-area wireless monitoring and control systems that require thousands of sensors to be connected over long distances. Due to their short communication range, existing WSN technologies such as those based on IEEE 802.15.4 form many-hop mesh networks complicating the protocol design and network deployment. To address this limitation, we propose a scalable sensor network architecture - called Sensor Network Over White Spaces (SNOW) - by exploiting the TV white spaces. Many WSN applications need low data rate, low power operation, and scalability in terms of geographic areas and the number of nodes. The long communication range of white space radios significantly increases the chances of packet collision at the base station. We achieve scalability and energy efficiency by splitting channels into narrowband orthogonal subcarriers and enabling packet receptions on the subcarriers in parallel with a single radio. The physical layer of SNOW is designed through a distributed implementation of OFDM that enables distinct orthogonal signals from distributed nodes. Its MAC protocol handles subcarrier allocation among the nodes and transmission scheduling. We implement SNOW in GNU radio using USRP devices. Experiments demonstrate that it can correctly decode in less than 0.1ms multiple packets received in parallel at different subcarriers, thus drastically enhancing the scalability of WSN.