The genus of Parasenecio (Senecioneae) comprises about 70 species of high medicinal value. In this study, the plastomes of Parasenecio palmatisectus and P. latipes were newly sequenced using high-throughput sequencing technology and compared with those of eight other species in Senecioneae. The complete chloroplast (cp) genomes were 151,185 bp in P. latipes with 37.5% GC and 151,263 bp in P. palmatisectus with 37.5% GC. We predicted 133 genes, including 37 tRNA genes, 86 protein-coding genes, 8 rRNA genes, and 2 pseudogenes (ycf1 and rps19). A comparative genomic analysis showed that the complete cp genome sequences of Parasenecio species and their related species were relatively conserved. A total of 49 to 61 simple sequence repeats (SSRs) and 34 to 46 interspersed repeat sequences were identified in the 10 Senecioneae species of plastomes. Within the tribe Senecioneae, single-copy regions were more variable than inverted repeats regions, and the intergenic regions were more variable than the coding regions. Two genic regions (ycf1 and ccsA) and four intergenic regions (trnC-GCA-petN, ycf1-trnN-GUU, psaI-ycf4, and rpl32-trnL-UAG) were identified as highly valuable plastid markers. A phylogenetic analysis under maximum likelihood revealed that the two Parasenecio species are sister to the genera of Ligularia and Sinosenecio in the tribe Senecioneae. This study also contributes to the super-barcode, phylogenetic, and evolutionary studies of Parasenecio plants.

Benefitting from the maturation of Wireless Power Transfer (WPT) technology, Wireless Rechargeable Sensor Networks (WRSNs) 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 paper, we study a fundamental issue of Wireless chArger placement wIth obsTacles (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.

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.

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 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.

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.