Growing interest in emerging IoT applications provides a strong drive to release a plethora of communication radios from different standards, which are largely classified into short-range (IEEE 802.15.4) and long-range radios (IEEE 802.15.4g). In this paper, we propose a joint, self-configuring MAC and routing protocol, SEDA-Net, which aims at adaptively choosing the best configuration for communication coordination and data delivery, depending on different deployed topologies and external conditions. SEDA-Net is a combination of SEDA-MAC, SEDA-Routing, and Cross-Opt. SEDA-MAC and SEDA-Routing adaptively determine (i) the best radio configuration for communication coordination under duty-cycling and (ii) each node's next-hop over which radio and Cross-Opt jointly optimizes inter-coupled MAC and routing iteratively. SEDA-Net differs from prior approaches which are designed with static configurations of radios and/or mainly with the goal of throughput maximization for dual Wi-Fi or Wi-Fi/LTE setups. We implement SEDA-Net on Contiki OS and perform extensive simulations and experiments using a testbed in an office building. This testbed consists of 45 nodes equipped with a commercial platform, Firefly, having 2.4 GHz short-range and 920 MHz long-range radios. We demonstrate that energy efficiency quantified by the network lifetime increases by up to 2.1 times, compared to that of existing approaches.

Internet of Things (IoT), which connects a large number of devices with wireless connectivity, has come into the spotlight. As the scope of IoT applications becomes wider, we observe a surge of missioncritical IoT services, e.g., industrial automation systems and medical IoT systems, requiring to satisfy stringent latency, reliability, and/or energy efficiency guarantees. For this purpose, a new MAC, called Time Slotted Channel Hopping (TSCH), has been standardized in IEEE 802.15.4e. However, it is challenging to design a distributed scheduling protocol that achieves the required QoS and energy efficiency at the same time due to complicated tradeoff (providing enough number of slots for QoS vs. minimizing scheduled slots for energy efficiency). In this paper, we propose a novel framework for providing QoS, called SSAP, which is designed to maximize network lifetime in a distributed fashion while satisfying given reliability and latency requirements. To this end, we decompose our goal into two crucial design components: (i) scheduling of slot and channel, and (ii) control of medium access period, each of which is performed by low-complexity and distributed mechanisms. To the best of our knowledge, this paper is the first work to comprehensively handle multiple QoSes for TSCH-based IoT networks. We implement SSAP in Contiki OS and perform extensive simulations and real experiments under various scenarios. Our evaluation results demonstrate that SSAP satisfies highly reliable communication and latency requirements while having the network lifetime that is 1.6 times longer compared to existing protocols for TSCH.

With the explosive increase in wireless devices, physical-layer signal analysis has become critically beneficial across distinctive domains including interference minimization in network planning, security and privacy (e.g., drone and spycam detection), and mobile health with remote sensing. While SDR is known to be highly effective in realizing such services, they are rarely deployed or used by the end-users due to the costly hardware ~1K USD (e.g., USRP). Low-cost SDRs (e.g., RTL-SDR) are available, but their bandwidth is limited to 2-3 MHz and operation range falls well below 2.4 GHz - the unlicensed band holding majority of the wireless devices. This paper presents SDR-Lite, the first zero-cost, software-only software defined radio (SDR) receiver that empowers commodity WiFi to retrieve the In-phase and Quadrature of an ambient signal. With the full compatibility to pervasively-deployed WiFi infrastructure (without any change to the hardware and firmware), SDR-Lite aims to spread the blessing of SDR receiver functionalities to billions of WiFi users and households to enhance our everyday lives. The key idea of SDR-Lite is to trick WiFi to begin packet reception (i.e., the decoding process) when the packet is absent, so that it accepts ambient signals in the air and outputs corresponding bits. The bits are then reconstructed to the original physical-layer waveform, on which diverse SDR applications are performed. Our comprehensive evaluation shows that the reconstructed signal closely reassembles the original ambient signal (>85% correlation). We extensively demonstrate SDR-Lite effectiveness across seven distinctive SDR receiver applications under three representative categories: (i) RF fingerprinting, (ii) spectrum monitoring, and (iii) (ZigBee) decoding. For instance, in security applications of drone and rogue WiFi AP detection, SDR-Lite achieves 99% and 97% accuracy, which is comparable to USRP.

This paper presents GateScatter, the first backscatter-based gateway connecting commodity IoT to WiFi. The backscatter design of GateScatter is an economic option towards pervasive Internet connectivity for ever-growing IoT. The carefully designed tag optimally reshapes ZigBee IoT packets with an arbitrary payload into an 802.11b WiFi packet over the air, such that the payload can be reliably retrieved at the WiFi receiver (hence a gateway). Gate-Scatter is highly compatible - it works with a wide range of IEEE 802.15.4-compliant systems, is agnostic to upper layer proprietary protocols, and does not require any modification to the commodity IoT platforms. GateScatter is extended to BLE IoT for generality. We prototype GateScatter hardware on FPGA where the wide applicability is demonstrated through evaluations on five popular IoT devices including Samsung SmartThings sensor, Philips smart bulb, and Amazon Echo Plus. Further extensive evaluations show that GateScatter consistently achieves throughput above 200 kbps and range of over 27 m under diverse practical scenarios including a corridor, dormitory room, and under user mobility.

Multi-user MIMO (MU-MIMO) is a widely-known, fundamental technique to significantly improve the spectrum efficiency. While there is a great demand for spectrum efficiency and massive scalability under explosively increasing IoT, hardware limitations make it particularly challenging for the mechanism to be transferred to the IoT (e.g., ZigBee) domain. This paper presents X-MIMO, a zero-cost, software-only cross-technology MU-MIMO for commodity ZigBee. As the first work to shed the light on the feasibility of MU-MIMO on commodity IoT, X-MIMO leverages on cross-technology communication (CTC) to turn the pervasively-deployed WiFi AP into MU-MIMO transmitter, delivering different packets to multiple ZigBees in parallel. X-MIMO uniquely exploits WiFi CSI to extract the accurate physical layer signal of the ZigBee packet and the WiFi-ZigBee channel coefficient. Rigorous derivation shows that X-MIMO's precoding is inherently immune to the uncertainties of the commodity devices, making X-MIMO highly reliable in practice. Lastly, spectrum-efficient emulation is proposed to maximize the spectrum reuse. We implement and comprehensively evaluate the performance of X-MIMO on commodity devices (Atheros AR9334 WiFi NIC and TelosB CC2420) as well as on USRP B210 for in-depth analysis. Results reveal that X-MIMO achieves 495 Kbps with <1% symbol error rate (SER) and 704.24 Kbps with 6.1% SER for two and three streams, respectively. Near-linear increase of the throughput effectively demonstrates the feasibility of X-MIMO.

We propose a new MAC protocol for IoT applications, called Bird-MAC, which is highly energy efficient in the applications where IoT sensors report monitoring status in a quasi-periodic manner, as in structural health monitoring and static environmental monitoring. Two key design ideas of Bird-MAC are: (a) no need of early-wake-up of transmitters and (b) taking the right balance between synchronization and coordination costs. The idea (a) is possible by allowing a node (whether it is a transmitter or receiver) to wake up just with its given wake-up schedule, and letting a late bird (which wakes up later) notify its wake-up status to its corresponding early bird (which wakes up earlier), where the early bird just infrequently waits for the late bird's wake-up signal. The idea (b) is realized by designing Bird-MAC to be placed in a scheme between purely synchronous and asynchronous schemes. We provide a rigorous mathematical analysis that is used to choose the right protocol parameters of Bird-MAC. We demonstrate the performance of Bird-MAC through extensive simulations, and real experiments. The experiment on our testbed using a 26 node testbed at an underground parking lot of our office building to monitor its structural health shows that energy consumption is reduced by about up to 45 percent over existing sensor MAC protocols. We also confirm the applicability of Bird-MAC in a challenging and realistic scenario through the experiment on Yeongjong Grand Bridge in South Korea.

Energy efficiency is critical in many IoT applications with sensors that deliver data over wireless communications. Duty-cycling has been a major method for reducing energy consumption. One popular duty-cycling is MAC duty-cycling where an MCU commands the periodic or adaptive turning on and off of an RF chip. Recently, there has been an increase in the number of RF chips for IoT that are equipped with PHY duty-cycling, a new capability of autonomously switching on and off an RF chip without the use of an MCU. These two schemes working at different layers have different pros and cons in terms of operating time scale, the amount of energy saved when the RF chip is switched off, all of which depend on the characteristics of the MCU and the RF chip. In this paper, we propose a novel protocol named HD-MAC (Hierarchical Duty-cycling MAC) that hierarchically integrates duty-cycling in the MAC and physical layers. By smartly applying the new function of chip-level duty-cycling, HD-MAC is able to further reduce the amount of on-time in MAC duty-cycling; hence, energy efficiency can be improved. To optimize HD-MAC’s energy efficiency while achieving a given delay requirement, we formulate an optimization problem and solve it to obtain the optimal parameters in the cross-layer context. We implement HD-MAC on Contiki OS and perform extensive experiments using a real sensor mote Firefly with a CC1200 RF chip. We demonstrate that the energy efficiency of HD-MAC is up to 72% higher than that of existing protocols while still satisfying the delay requirement and sustaining similar reliability.

We propose a new sensor MAC protocol, called Bird-MAC, which is highly energy efficient in the applications where sensors periodically report monitoring status with a very low rate, as in structural health monitoring and static environmental monitoring. Two key design ideas of Bird-MAC are: (a) no need of early-wake-up of transmitters and (b) taking the right balance between synchronization and coordination costs. The idea (a) is possible by allowing a node (whether it is a transmitter or receiver) to wake up just with its given wake-up schedule, and letting a late bird (which wakes up later) notify its wake-up status to its corresponding early bird (which wakes up earlier), where the early bird just infrequently waits (i.e., nods) for the late bird's wake-up signal. The idea (b) is realized by designing Bird-MAC to be placed in a scheme between purely synchronous and asynchronous schemes. We provide rigorous mathematical analysis that is used to choose the right protocol parameters of Bird-MAC. We demonstrate the performance of Bird-MAC through extensive simulations, and real experiments using a 26 node testbed at an underground parking lot of our office building to monitor its structural health, where we confirm that energy consumption is reduced by about up to 45% over existing sensor MAC protocols.

Abstract Resource allocation in a software project is crucial for successful software development. Among various types of resources, human resource is the most important as software development is a human‐intensive activity. Human resource allocation is very complex owing to the human characteristics of developers. The human characteristics affecting allocation can be grouped into individual‐level characteristics and team‐level characteristics. At the individual level, familiarity with tasks needs to be taken into account as it affects the performance of developers. In addition, developers have different levels of productivity, depending on their capability and experience; the productivity of developers also varies according to tasks. At the team level, characteristics such as team cohesion, communication overhead, and collaboration and management also affect human resource allocation. As these characteristics affect the efficiency of project execution, we treat them as constraints of human resource allocation in our approach. We identify individual‐level constraints and team‐level constraints based on the literature and interviews with experts in the industry. With these constraints, our approach optimizes the scheduling of human resource allocations, resulting in more realistic and efficient allocations. We also provide a guideline supporting various factors, with respect to roles and module characteristics, to estimate the productivity of developers based on COCOMO II. As productivity data are hard to obtain and manage, our guideline can provide a useful direction for human resource allocation in case of software projects. To validate our proposed approach, we document a case study using real project data. Copyright © 2011 John Wiley & Sons, Ltd.