The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT), where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers: vehicle identification layer, object layer, inter-intra devices layer, communication layer, servers and cloud services layer, big data and multimedia computation layer, and application layer. The final part of this paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario. INDEX TERMS Internet of Vehicles, IoV, layer architecture, smart city, applications, big data.
Nociceptive Transient Receptor Potential channels such as TRPV1 are targets for treating pain. Both antagonism and agonism of TRP channels can promote analgesia, through inactivation and chronic desensitization. Since plant-derived mixtures of cannabinoids and the Cannabis component myrcene have been suggested as pain therapeutics, we screened terpenes found in Cannabis for activity at TRPV1. We used inducible expression of TRPV1 to examine TRPV1-dependency of terpene-induced calcium flux responses. Terpenes contribute differentially to calcium fluxes via TRPV1 induced by Cannabis-mimetic cannabinoid/terpenoid mixtures. Myrcene dominates the TRPV1-mediated calcium responses seen with terpenoid mixtures. Myrcene-induced calcium influx is inhibited by the TRPV1 inhibitor capsazepine and Myrcene elicits TRPV1 currents in the wholecell patch-clamp configuration. TRPV1 currents are highly sensitive to internal calcium. When Myrcene currents are evoked, they are distinct from capsaicin responses on the basis of I max and their lack of shift to a pore-dilated state. Myrcene pre-application and residency at TRPV1 appears to negatively impact subsequent responses to TRPV1 ligands such as Cannabidiol, indicating allosteric modulation and possible competition by Myrcene. Molecular docking studies suggest a non-covalent interaction site for Myrcene in TRPV1 and identifies key residues that form partially overlapping Myrcene and Cannabidiol binding sites. We identify several non-Cannabis plantderived sources of Myrcene and other compounds targeting nociceptive TRPs using a data mining approach focused on analgesics suggested by non-Western Traditional Medical Systems. These data establish TRPV1 as a target of Myrcene and suggest the therapeutic potential of analgesic formulations containing Myrcene.
Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs.
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