Recently, Wireless Rechargeable Sensor Networks (WRSNs) based platforms have become promising for broad applications. However, if an adversary disrupts the wireless charging process in WRSNs, sensors may die due to lack of timely energy supply, compromising the reliability and availability of systems relying on sensing tasks. In this paper, we develop a zero-cost power jamming attack in WRSNs, termed rEflection-based jaMmIng aTtack (EMIT), which introduces an off-the-shelf and inconspicuous reflector such as a Coca-Cola can that intentionally reflects the wave from the charger to destructively interfere with the charging wave at the target sensor. Our approach lifts the limitations of traditional charging attacks, including high cost, complex implementation and ease of detection. We conduct extensive field experiments to evaluate EMIT attack in different types of WRSNs. The results show that on average, the success rate of EMIT attack is 90% in WRSNs with fixed charging locations, and 75% in WRSNs with dynamic charging locations. Finally, we build a real-world WRSN on university campus to study the effectiveness of EMIT attack in complex scenarios. In total, EMIT attack causes 134 sensor deaths over 66 days.

Recently, deploying static directional chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Extensive simulations and field experiments are conducted to evaluate the performance of our solution. The results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 132.09% on average.

Existing VLC backhaul solutions typically rely on rigid wired (e.g., Ethernet or power-line) or alignment-sensitive LOS links for inter-attocell connectivity, which renders the overall network vulnerable to backbone link failures. In this poster, we introduce a novel network architecture that exploits the inherent non-line-of-sight (NLOS) optical channels between adjacent attocells to enable inter-attocell communication. In particular, we design a chirp signal based on chirp spread spectrum (CSS) modulation to enhance the robustness of communication under low signal-to-noise ratio (SNR) conditions, along with a two-stage window alignment approach to achieve precise synchronization while reducing real-time decoding latency. The potential and feasibility of the newly suggested approach are demonstrated through implementations on both ESP32 and FPGA platforms.

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%

To guarantee the reliability for WRSNs, placing sufficient static chargers effectively ensures charging coverage for the entire network. However, this approach leads to a considerable number of sensors located within charging overlaps. The destructive wave interference caused by concurrent charging in these overlaps may weaken sensors received power, thereby negatively impacting charging performance. This work addresses a CHArging utIlity maximizatioN (CHAIN) problem, which aims to maximize the overall charging utility while considering wave interference among multiple chargers. Specifically, given a set of stationary sensors, we investigate how to determine optimal positions for a fixed number of chargers. To tackle this problem, we first develop a charging model with wave interference, then propose a two-step charger placement scheme to identify the optimal charger positions. In the first step, we maximize the overall additive power of the waves involved in interference by selecting an appropriate initial position for each charger. Then, in the second step, we maximize the overall charging utility by finding the optimal final position for each charger around its initial position. Finally, to evaluate the performance of our scheme, we conduct extensive simulations and field experiments and the results suggest that CHAIN performs better than the existing algorithms.

Concurrent wireless charging offers significant performance improvements for Wireless Rechargeable Sensor Networks (WRSNs). However, wave interference, arising from interactions between electromagnetic waves from multiple chargers, disrupts this process. This results in uneven power distribution, potentially leading to significantly attenuated or even negligible energy reception at certain locations. This paper addresses this challenge by introducing the Concurrent cHarging with Efficient phaSe Scheduling (CHESS) problem. CHESS maximizes the energy received by critical sensors through a novel on-demand phase scheduling approach. To achieve this, we propose a practical charging model with charger phases and wave interference effects. Subsequently, a charger grouping algorithm reduces computational complexity, followed by a phase vector searching algorithm to identify optimal phases for maximizing sensor energy reception. Finally, a phase scheduling algorithm enables dynamic adaptation to the evolving energy demands. Simulations show significant efficiency improvements, outperforming baseline algorithms by an average of ${8 9. 8 \%}$

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e.,back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by$49.5\%$and increases the energy usage effectiveness by$10.2\%$on average as compared with existing algorithms.

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average.

Benefitting from the maturation of Wireless Power Transfer technology, Wireless Rechargeable Sensor Networks have become a promising solution for prolonging network lifetime. In practical charging scenarios, obstacles are ubiquitous. However, most prior arts have failed to consider the combined impacts of the material, size, and location of obstacles on the charging performance, making these schemes unsuitable for real applications. In this article, we study a fundamental issue of W ireless ch A rger placement w I th obs T acles (WAIT), that is, how to place wireless chargers by comprehensively considering these parameters of obstacles, such that the overall charging utility is maximized. To tackle the WAIT problem, we first build a practical charging model with obstacles by introducing shadow fading, and conduct experiments to verify its correctness. Then, we design a piecewise constant function to approximate the nonlinear charging power. Afterwards, we develop a Dominating Coverage Set extraction algorithm to reduce the continuous solution space to a limited number. Finally, we prove the WAIT problem is a maximizing monotone submodular function problem, and propose a 1-1/e-ε approximation algorithm to address it. Extensive simulations and field experiments show that our scheme outperforms comparison algorithms by at least 20.6% in charging utility improvement.

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms.

Without the need for batteries, backscatter-based Wireless Powered Communication Network (WPCN) has been envisioned as a promising alternative to conventional wireless networks. Unfortunately, the unique phase cancellation problem in backscatter-based WPCN is essentially a phenomenon that severely affects connectivity and reliability of the network. Many arts have tried to tackle this issue either by using multiple antennas to employ the signal diversity, which increases the size and is not cost-efficient, or by making a repetition of the same information with different load impedances, which significantly decreases the throughput of network. In our paper, we propose an effective deployment scheme, aiming to fundamentally eliminate the phase cancellation problem. Specifically, we first build a practical communication model seeking the blind areas caused by phase cancellation. Then, a greedy algorithm and a minimum-weight graph based algorithm are proposed to elaborate topology of the network to ensure the connectivity. Finally, extensive experiments are carried out to evaluate the performance.

With the rapid development of industry, vibration equipment has become one of the most widely used components for industrial systems. Utilizing vibration sensing and recognition is an effective way to diagnose and understand the working condition of these systems. However, the performance of traditional video/laser-based vibration sensing and recognition solutions varies significantly under different lighting conditions, while the invasive approaches need to directly mounting dedicated sensors to the target, which might pose a threat to its operating safety. To tackle this issue, we propose RF-Vsensing, an RFID-based contactless vibration sensing and recognition method without attaching anything to the target device. Unlike existing methods, RF-Vsensing can realize highly accurate non-contact vibration sensing and recognition using commercial off-the-shelf RFID devices. The evaluation results show that the average accuracy of vibration can reach 96.07% and the average recognition accuracy of clockwise and anticlockwise can reach 99.44%.

Computational Radio Frequency Identification (CRFID) tags operate solely on harvested energy and have emerged as viable platforms for a variety of ubiquitous sensing and computation applications. Due to their battery-less nature, these tags can be permanently deployed in hard-to-reach places where the possibility of tag access is eliminated. In such scenarios, maintaining and upgrading the tag’s firmware becomes infeasible because programming tools, including wired interface and PC-based software, are required to erase, modify, or reprogram the microcontroller unit’s memory. Such limitations necessitate the demand for an over-the-air (OTA) scheme, which can wirelessly reprogram or upgrade the firmware in CRFID tags. In this article, we present R 3 —a reliable OTA reprogramming scheme that is compliant with EPC protocol and requires no hardware upgrade to RFID reader or CRFID tag. We demonstrate our scheme on three platforms, which include both software-defined as well as chip-based CRFID tags, that is, WISP5.1 and Optimized WISP (Opt-WISP), and Spider tag, respectively. The selection also includes both the FLASH- and FRAM-based microcontrollers. We extensively evaluate our scheme in terms of several metrics, including overall system delay, time and energy overhead, and success rate in line with interrogation range. We foresee our endeavor to offer the viability of OTA reprogramming and firmware upgrade for CRFID tokens under practical situations.