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Data dissemination in mobile opportunistic networks needs to tackle sparse node density, high mobility and intermittent communications. Because end-to-end routing paths are not available due to fragile network topology, the store-carry-and-forward data delivery model is adopted to route messages in a delay-tolerant manner. This paper proposes a message forwarding scheme based on movement patterns. This scheme exploits two-hop movement information in geographic neighborhood and devises a relay node selection policy in mobile opportunistic networks. Selected relay nodes have higher probability of handing over carried messages to either destinations or encountered nodes moving closer to destinations. Simulated results show that the proposed scheme obtains considerable performance of successful delivery ratio and messaging overhand under the TVCM and SPMM mobility models.

Networked cyber-physical systems are driven by control loops that commonly depend on tight communication timing guarantees, requiring both complex and inflexible scheduling mechanisms to provide the necessary amount of determinism. We previously presented the fundamental idea of Cooperative Congestion Control (CoCC), which enables elastic control applications to share a common network cooperatively. By balancing the resource demands of control applications, it achieves fairness w.r.t. to the quality of control and efficiently avoids congestion and thus harmful delays and losses without the need for complex scheduling mechanisms. We propose lightweight and effective solutions that enable CoCC to deal with multiple bottlenecks and double its convergence speed. Our evaluation results show that high utilization, low delay, fairness, and fast convergence can be achieved by CoCC even in complex multi-bottleneck scenarios with a high number of flows.

This paper gives an insight on the performance of mixed dual-hop radio-frequency (RF)-underwater optical wireless communication (UOWC) systems. The system consists of multiple-input multiple-output (MIMO) RF hop employing Nakagami- $m$ fading channel on the source ( $S$ ) node communicating with a destination node ( $D$ ) considered as the legitimate receiver via an amplify-and-forward (AF) relay ( $R$ ) node equipped with multiple RF antennas for reception. It considers transmit antenna selection (TAS) scheme for communication in the MIMO RF hop while the information is transmitted from the $S$ node to the $D$ node, i.e. submarine etc., via the UOWC hop. Specifically, the $R$ node receives incoming information messages from $S$ node via MIMO RF links, applies maximal-ratio combining (MRC) technique, amplifies the output combined signal, and subsequently forwards it to the destination utilising a variable gain relaying (VGR) via an UOWC link. We derive exact closed-form expressions for the system's end-to-end (E2E) statistical channel characteristics. Our derived analytical expressions present an efficient technique to depict the impact of our system and channel parameters on the performance, namely the varying number of increasing antennas $N_t=N_r=2,3,4$ or more from the $S$ node towards $R$ node and the involvement of underwater detection techniques of $r = 1$ for heterodyne detection and $r = 2$ for intensity modulation/direct detection (IM/DD) in the underwater turbulence severity of the UOWC link. Outage probability (OP) and average bit error rate (BER) closed-form expressions for the varying bubble levels (BL) (L/min) for different scenarios, varying temperature gradients (TG) (°C cm−1), different fresh and saline waters, and various binary modulation techniques have been accurately validated for the E2E system presented in this work along with the tightness of their respective high-end asymptotes.

Due to recent technological advancements in the area of unmanned aerial systems, equipping an unmanned aerial vehicle (UAV) with a base station (BS) has been proposed to augment terrestrial base stations and to enhance the performance of 5G and beyond-5G networks. At the same time, advances in multi-hop ad hoc networks have made these networks feasible for supporting communications when fixed infrastructure is unavailable. In this paper, we examine the benefit of combining these two types of networks, utilizing multi-hop ad hoc networks to augment the coverage achievable by a UAV-BS. In particular, we explore the 3D placement problem for multiple UAV-BSs that maximize the number of covered ground nodes both with and without support of multi-hop ad hoc ground networks. First we present a mathematical formulation of the single UAV-BS placement problem, then we propose a heuristic algorithm that to the best of our knowledge is the first one in the literature to maximize the number of directly and indirectly covered ground nodes considering nodes with the same as well as with different quality of service (QoS) requirements. Simulation results show the merits of our proposed algorithm regarding utilizing the multi-hop capabilities of the ground nodes in terms of reducing the number of UAV-BSs required to cover the nodes compared to a benchmark algorithm that does not take into account the potential of covering nodes indirectly.

Seeking to solve problems in the power electric system (PES) related to exacerbated and uncontrolled energy consumption by final consumers such as residences, condominiums, public buildings and industries, electric power companies (EPC) are increasingly seeking new information and communication technologies (ICTs) to transform traditional electric power distribution networks into smart grids (SG). With this implementation, PES will be able to remotely control electric power consumption as well as monitor data generated by smart meters (SM). However, Internet-of-Things (IoT) technologies will enable all this to happen quickly and at low cost, since they are low-cost devices that can be deployed quickly and at scale in these scenarios. With this in mind, this work aimed to study, propose, and implement a hybrid communication infrastructure with LoRaWAN and LoraMesh for the demand-side management as a service (HyDSMaaS) using IoT devices such as long range (LoRa) to provide an advanced metering infrastructure (AMI) capable of performing all these applications as a service offered by EPC to end consumers. Additionally, services such as demand-side management (DSMaaS) can be used in this infrastructure. From the preliminary results it was found that the LoRaWAN network achieved a range of up to 2.35 km distance and the LoRaMESH one of 600 m, thus, the latter is more suitable for scenarios where there is little interference and the SMs are at long distances, while the other is used for scenarios with greater agglomeration of nearby SMs. Considering the hybridized scenario between LoraWAN and LoRaMESH, it can be seen that the implementation possibilities increase, since its range was approximately 3 km considering only one hop, and it can reach 1023 devices present in a mesh network. Thus, it was possible to propose the actual implementation of LoRaWAN and LoRaMESH protocols as well as the hybridization of the two protocols for HyDSMaaS. Additionally, the results obtained are exclusively from Radioenge’s LoRa technology, which can be further improved in the case of using more powerful equipment.

Cross-Technology Interference (CTI) is a severe issue for wireless networks operating in the license-free ISM bands. In particular, CTI can significantly affect the performance of low-power wireless links used in the Internet-of-Things (IoT). The IEEE 802.15.4 standard adopts a channel hopping scheme in its Time-Slotted Channel Hopping (TSCH) mode as a means to mitigating the adverse effect of CTI. However, the indiscriminate procedure by which TSCH nodes hop over different channels can suffer from severe interference in specific channels. Adaptive channel blacklisting is a technique to alleviate this issue by leaving out low-quality channels from the hopping list. To enable an effective blacklisting, especially in highly varying networks, an accurate real-time prediction of the quality of all available channels is of paramount importance. Previous studies rely on the past records of the channels as an indication of their quality in near future. Evidently, such approaches cannot extend to highly dynamic environments. This paper present a self-supervised approach for training deep neural networks capable of predicting the future behaviour of the frequency channels. The trained models can then substitute the quality assessment approaches in blacklisting schemas. Considering in-vehicle wireless networks as a target application, we evaluate this idea using a real-world experimental dataset, consisting of three measurement scenarios inside a moving vehicle. The experimental results show that using the proposed technique for TSCH blacklisting significantly improves the reliability of networks experiencing such highly dynamic interference and performs at least as good as the existing channel assessment methods in low-interference conditions.

Vehicle Platooning combines lower fuel consumption with safe and efficient transportation in a cooperative driving application. To ensure this, vehicles must communicate quickly and reliably. The sliding window protocol Caterpillar Random Linear Network Coding (CRLNC) is used as it does not rely on acknowledgements and uses network coding to reduce losses. However, the protocol was intentionally designed for single hop scenarios. Therefore, three contributions to the CRLNC protocol are presented in this paper to optimize it for multi hop/single path transmissions. The contributions are idle-slot-management, adaptive-window, and queue-management. By effectively combining these contributions, a large gain in throughput of up to 40% is achieved. Furthermore, it was investigated what are the optimal protocol parameters for CRLNC in single path scenarios are. It is shown that the best transmission in terms of throughput and delay is achieved, when the code rate is less than one minus the error probability and a large window size is used. Even if the error probability is unknown, a large window size will bring more benefits to the transmission.

Despite the world being in 21st century most of the developing and under-developed nation use traditional method for farming we requires tremendous energy and hectic schedule from a small scale farmer with a very measly return in terms of profit moreover the water wastage and continuous monitoring required to keep check in plants condition is just unjustified but with 58% of population having agriculture as primary income source most of the Indian farmers having extremely low income it seems impossible for them to hop over costly machineries. But now with the cost-effective processors out there in the market can provide a solution to all these issues faced by Indian farmers. With exponential progress of Internet of Things (IoT) devices in the market smart irrigation systems are becoming a new trend. This paper proposes design and theory of one such smart irrigation system using NodeMCU to wirelessly operate a network of irrigation modules by irrigating the field when required by measuring the water content of soil and keep checking condition of plant using a camera this paper also provide insight of how to keep safe integrity of data which travels from NodeMCU to user smartphone using ciphering methods and by keeping proposed system reliable and cost effective.

The angular instability of unmanned-aerial-vehicle (UAV) relays as well as the turbulence-induced beam wander may lead to significant pointing error in free-space optical backhaul. The hop distance between each relay in a serial relay scheme is an important parameter that can be optimized in order to enhance the performance of the system. In this study, we have optimized the error and outage probabilities as a function of the hop distance vector for amplify-and-forward relays (AF) under a number of constraints such as the maximum and average relay power and the maximum relay gain. We have discovered that for the unconstrained optimization problem, the optimum hop distances is a strictly increasing sequence for both the error and outage probabilities. For maximum power constraint optimization, the optimum hop distances form a strictly increasing sequence, whereas for maximum gain constraint optimization, the optimum hop distances lead to a nondecreasing sequence.

Based on the reverse asymmetric time synchronization framework, we have proposed several schemes with a major focus on the energy efficiency and computational complexity of a large number of battery-powered, low-cost sensor nodes in wireless sensor networks (WSNs). To address the cumulative end-to-end synchronization error, we have also introduced an idea of compensating for the processing delays at packet-relaying gateways as an energy-efficient way of multi-hop extension of WSN time synchronization schemes. In this paper, we present a comprehensive analysis of the multi-hop extension of WSN time synchronization schemes based on packet-relaying gateways with the per-hop delay compensation and the results of extensive experiments for the energy-efficient time synchronization schemes based on the reverse asymmetric time synchronization framework together with the flooding time synchronization protocol as a representative of existing schemes. Experimental results based on a real testbed demonstrate that the multi-hop extension based on packet-relaying gateways with the per-hop delay compensation greatly improves the performance of time synchronization of all the schemes considered compared to the multi-hop extension based on the conventional time-translating gateways.

This paper presents a double sky-hook algorithm for controlling semi-active suspension systems in order to improve road-holding property for application in an in-wheel motor. The main disadvantage of the in-wheel motor is the increase in unsprung masses, which increases after shaking of the wheel, so it has poor road-holding that the conventional theoretical sky-hook algorithm cannot achieve. The double sky-hook algorithm uses a combination of damper coefficients, one from the chassis motion and the other from the wheel motion. Computer simulations using a quarter and full car dynamic models with the road conditions specified by ISO2631 showed the effectiveness of the algorithm. It was observed that the algorithm was the most effective in the vicinity of the wheel hop frequency. This paper also proposed the parameter set of the double sky-hook algorithm to differentiate the driving mode of vehicles under advanced development.

With increasing industries, the air pollution monitoring system has become the main challenge of intelligent cities. Several methods have been developed in APMS, and WSNs seem like the solution to sensors, because of less cost, and dense deployment. Thus, the deployment of the sensors is significant for better performance while assuring less economic cost. WSN is related to the set of distributed sensors for monitoring the PHY situations and arranging the gathered data at the central position. In this article, a comparative analysis is defined in routing protocols for WAPMS to study and defined the advantages and disadvantages. The conclusion showed the prior protocols into two different classes such as cluster and non-cluster based routing protocols. The cluster-based methods have designed the PHY network into virtual classes, whereas the non-cluster based protocols both use broadcasting tools for data communication cluster-based routing protocol, compared to LEACH; TLLEACH is based on the two-level cluster heads (CHs) with less communication distance, and less hop is necessary to transfer for distance to the base station. It reduces the average energy consumption. Their network parameters are calculated to E2D and energy in the network.

The ever-increasing bandwidth requirements of modern datacenters have led researchers to employ optical circuit switching (OCS) to reduce the cost of data center networks, but most of these proposals these schemes assume that the traffic in the network is uniform, which will lead to the traffic cannot be fully invoked in the real environment. We propose an intelligent multi-hop scheduling algorithm capable of supporting any one hop scheduling algorithm to reduce the impact of traffic imbalance on the overall performance of the network. Through the intelligent multi-hop method, the algorithm can transport the oversubscribed traffic with the minimum overall link occupancy. Simulation results show that the proposed algorithm can reduce the average end-to-end delay by 46.4% and the average stream completion time by 51%, and the throughput is improved by 20.1%.

Relay-assisted D2D communication can expand the communication range and improve the spectrum efficiency. However, most relay selection algorithms only consider single-hop information between transmitter and relay. To obtain better communication quality, we propose an Improved Relay Selection Strategy based on the Two-hop Social Relationship which not only considers the social relationship of transmitter-to-relay, but also the social relationship of relay-to-receiver. Besides, we combine the social awareness and channel state information to define the social relationship. The strategy determines the candidate relays according to the maximum D2D communication distance firstly, and then selects the optimal relay among candidate ones based on Two-hop Social Relationship (THSR). The simulation results show that the proposed algorithm can significantly improve the system throughput.

The coordinates of nodes are very important in the application of wireless sensor networks (WSN). The range-free localization algorithm is the best method to obtain the coordinates of sensor nodes at present. Range-free localization algorithm can be divided into two stages: distance estimation and coordinate calculation. For reduce the error in the distance estimation stage, a node localization algorithm for WSN based on virtual partition and distance correction (VP-DC) is proposed in this paper. In the distance estimation stage, firstly, the distance of each hop on the shortest communication path between the unknown node and the beacon node is calculated with the employment of virtual partition algorithm, then, the length of the shortest communication path is obtained by summing the distance of each hop, finally, the unknown distance between nodes is obtained according to the optimal path search algorithm and the distance correction formula. This paper innovative proposes the virtual partition algorithm and the optimal path search algorithm, which effectively avoids the distance estimation error caused by hop number and hop distance, and improves the localization accuracy of unknown nodes.

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the implicit feedback, but negative sampling in GNN-based CF has been largely unexplored. In this work, we propose to study negative sampling by leveraging both the user-item graph structure and GNNs' aggregation process. We present the MixGCF method---a general negative sampling plugin that can be directly used to train GNN-based recommender systems. In MixGCF, rather than sampling raw negatives from data, we design the hop mixing technique to synthesize hard negatives. Specifically, the idea of hop mixing is to generate the synthetic negative by aggregating embeddings from different layers of raw negatives' neighborhoods. The layer and neighborhood selection process are optimized by a theoretically-backed hard selection strategy. Extensive experiments demonstrate that by using MixGCF, state-of-the-art GNN-based recommendation models can be consistently and significantly improved, e.g., 26% for NGCF and 22% for LightGCN in terms of NDCG@20.

The system performance is studied in the context of dual-hop relay systems using mixed free-space optical/ radio frequency (FSO/RF) links. Two destinations need assistance from relay associated with FSO link connecting a source node. The heterodyne detection or direct detection is necessary for detecting signal in the first hop. In the second hop, the relay relying on the non-orthogonal multiple access technique to support multiple users through an RF link. The high-speed backhauling related to FSO is suitable to design hybrid system for applications in high dense users environment. The FSO link is modeled by the M distribution in the presence of pointing errors, while the RF link is modeled by Rayleigh fading. To provide system performance analysis, we present closed-form formulas of the outage probability. Furthermore, we find that performance gap among two users mainly depend on power allocation factors and transmit signal-to-noise ratio (SNR) at the source. Finally, we conduct Monte Carlo simulation results to corroborate the derived formulas.

Beacon messages and emergency messages in vehicular ad hoc networks (VANETs) require a lower delay and higher reliability. The optimal MAC protocol can effectively reduce data collision in VANETs communication, thus minimizing delay and improving reliability. In this paper, we propose a Q-learning MAC protocol based on detecting the number of two-hop neighbors. The number of two-hop neighbors in highway scenarios is calculated with very little overhead using the beacon messages and neighbor locations to reduce the impact of hidden nodes. Vehicle nodes are regarded as agents, using Q-learning and beacon messages to train the near-optimal contention window value of the MAC layer under different vehicle densities to reduce the collision probability of beacon messages. Furthermore, based on the contention window value after training, a multi-hop broadcast protocol combined with contention window adjustment for emergency messages in highway scenarios is proposed to reduce forwarding delay and improve forwarding reliability. We use the trained contention window value and the state information of neighboring vehicles to assign an appropriate forwarding waiting time to the forwarding node. Simulation experiments are conducted to evaluate the proposed MAC protocol and multi-hop broadcast protocol and compare them with other related protocols. The results show that our proposed protocols outperform the other related protocols on several different evaluation metrics.

To address the current situation limitation of traditional DV-Hop, we suggested a DV-Hop localization based on a rectification factor using the Social Learning Class Topper Optimization (SL - CTO) algorithm in that paper. In order to adjust the number of hops between beacon nodes, we have implemented a rectification factor in the suggested method. By measuring the dimensions of all the beacons at dumb nodes, the suggested algorithm decreases communication among unknown or dumb and beacon nodes. The model of network imbalance, It is often considered to be demonstrate a applicability of the Proposed approach in the anisotropic network. Simulations have been performed on LabVIEW@2015, and Comparisons were made with conventional DV-Hop, particle swarm optimization-based DV-Hop and runner-root optimization-based DV-Hop for our proposed algorithm. In comparison to current localization methods, simulation outcomes showed that the proposed localization technique reduces computing time, localization error variance and localization error.

The conventional LoRa system is not able to sustain long-range communication over fading channels. To resolve the challenging issue, this paper investigates a two-hop opportunistic amplify-and-forward relaying LoRa system. Based on the best relay-selection protocol, the analytical and asymptotic bit error rate (BER), achievable diversity order, coverage probability, and throughput of the proposed system are derived over the Nakagamim fading channel. Simulative and numerical results show that although the proposed system reduces the throughput compared to the conventional LoRa system, it can significantly improve BER and coverage probability. Hence, the proposed system can be considered as a promising platform for low-power, long-range and highly reliable wireless-communication applications., 7 pages, 6 figures, conference

The DV-Hop algorithm is widely used because of its simplicity and low cost, but it has the disadvantage of a large positioning error. In recent years, although some improvement measures have been proposed, such as hop correction, distance-weighted correction, and improved coordinate solution, there is room for improvement in location accuracy, and the accuracy is affected in anisotropic networks. A location algorithm based on beacon filtering combining DV-Hop and multidimensional support vector regression (MSVR) is proposed in this paper. In the process of estimating the coordinates of unknown nodes, received signal strength indication (RSSI), MSVR, and weighted least squares method are combined. In addition, the verification error of beacon nodes is proposed, which can select the beacon nodes with smaller errors to reduce the location error. Simulation results show that in different distributions, the location accuracy of the proposed algorithm is at least 34% higher than that of the classical DV-Hop algorithm and at least 28% higher than that of the localization based on multidimensional support vector regression (LMSVR) algorithm. The proposed algorithm has the potential of application in small-scale anisotropic networks.

Wireless sensor networks (WSNs) have the ability to sense and process information. Only when the position of the sensor nodes is available, the information transmitted to the user is meaningful. In this paper, to improve the localization accuracy of DV-Hop, a two-objective DV-Hop localization algorithm based on parallel grey wolf optimization is proposed called PGWO-DV- Hop. Unlike the traditional DV-Hop based on intelligent optimization algorithm, after DV-Hop, two objective functions are established by using the estimated coordinates of neighboring nodes, the estimated distance and the theoretical distance between unknown node and neighboring nodes. To optimize the functions, the parallel grey wolf optimization (PGWO) is proposed. Simulation results show that compared with original DV-Hop and the other two typical improved algorithms, our proposed strategy significantly improves the localization accuracy.

Multi-hop machine reading comprehension (MRC) task aims to enable models to answer the compound question according to the bridging information. Existing methods that use graph neural networks to represent multiple granularities such as entities and sentences in documents update all nodes synchronously, ignoring the fact that multi-hop reasoning has a certain logical order across granular levels. In this paper, we introduce an Asynchronous Multi-grained Graph Network (AMGN) for multi-hop MRC. First, we construct a multigrained graph containing entity and sentence nodes. Particularly, we use independent parameters to represent relationship groups defined according to the level of granularity. Second, an asynchronous update mechanism based on multi-grained relationships is proposed to mimic human multi-hop reading logic. Besides, we present a question reformulation mechanism to update the latent representation of the compound question with updated graph nodes. We evaluate the proposed model on the HotpotQA dataset and achieve top competitive performance in distractor setting compared with other published models. Further analysis shows that the asynchronous update mechanism can effectively form interpretable reasoning chains at different granularity levels.

Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes. However, such attention mechanism does not account for nodes that are not directly connected but provide important network context. Here we propose Multi-hop Attention Graph Neural Network (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. MAGNA diffuses the attention scores across the network, which increases the receptive field for every layer of the GNN. Unlike previous approaches, MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. We demonstrate in theory and experiments that MAGNA captures large-scale structural information in every layer, and has a low-pass effect that eliminates noisy high-frequency information from graph data. Experimental results on node classification as well as the knowledge graph completion benchmarks show that MAGNA achieves state-of-the-art results: MAGNA achieves up to 5.7% relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. MAGNA also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion MAGNA advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics.

Internet of Things (IoT) is an emerging technology that has captured the attention of academia and industry. IoT technology creates a smart platform and reduces the need for human interaction, which may result in more precise reactions. The battery is the power supply when IoT devices are distributed in a vast area with a lack of direct power connection, for example in agricultural fields or wildlife animal monitoring. In such scenarios, battery replacement can be a hard or sometimes impossible task. In this research, we propose a data transmission model to improve data routing in a clustered IoT platform possible to develop in vast areas such as farms. The proposed approach will balance power consumption in inter-cluster data routing. Hence the lifetime of relay cluster heads have been improved and nodes can operate for a longer period of time. Our results show that considering power level and workload as factors to select the next cluster head relay node in inter-cluster data routing will improve network stability and reduce packet loss in the network.

In this paper, we analyze a multi-hop device-to-device (D2D) communication network operating in a region of cellular outage, e.g., in case of a natural disaster. In particular, we assume that the D2D devices operate in a half-duplex manner and can receive signals from or transmit to a single D2D relay. For this system, we characterize a multi-hop D2D transmission protocol wherein we divide the transmission area into multiple regions based on the D2D transmission range. In contrast to the other works present in literature, we have taken into account the probability of a relay being used by another D2D source at the instant when the typical D2D source attempts to connect to it. Then, we derive the signal to interference and noise ratio (SINR) coverage probability for a typical device. Based on this, we define and characterize a performance metric called the availability-coverage product (ACP) to jointly take into account the coverage performance of the devices and the probability of them being used as relays by the other devices in outage. Our analysis highlights several system design insights in terms of the D2D communication range and the optimal number of active devices in the network in terms of the ACP.

Internet of things plays a major role in all the applications that are utilized for automating the environment. In this, the searching of devices in the larger network is a tedious process. Further, it became easier by defining the social relationship among the devices in the network. Social Internet of things is the combination of real-world network and multiple devices in the Internet of things. In social Internet of things, the user can access information globally at any place based on its ID. But, the accessing of this ID in the large database is difficult. Several works were carried out to locate the ID of the devices in the social Internet of things [SIoT]. Community and effective search-based algorithm is one of the methods for effective localization of devices in the social Internet of things as the conventional method for device localization. The Routing protocol for Low power and Lossy networks (RPL) able to increase lifetime of network with high packet loss. In existing, the localization and routing is performed separately. This problem is overcome with the proposed trust-based community nodes and optimized RPL in social Internet of things network. Here, the community is formed with the combination of high trust and low trust nodes of similar taste. These trusted node-based community formation helps to locate the trusty nodes in the transmission process. The dragon fly optimization is used to improve the RPL performance by selecting trusty nodes as next hop in the routing process. Due to the combination of trust community and optimized RPL, the lifetime and success rate of the data transmission is increased by 90% as compared to the existing preference-based device localization and traditional RPL protocol. The whole process is realized with the help of MATLAB R2018a in windows 10 environment. Therefore, the proposed trust community and optimized RPL is best for the trust-based data transmission in social Internet of things network by having high alive nodes of 90% and time delay of 30 s for hop localization.

The deployment of unmanned aerial vehicles (UAV) as a mobile relay is an emerging technique in cooperative wireless communication system. In this paper, we analyze the performance of UAV assisted full, half duplex (FD)(HD) cooperative communication in a dual hop relaying scenario, which consists of a base station, an energy harvesting enabled UAV as relay and a desired user. For our desired system, we derived the closed form expressions for outage probability, throughput by adopting decode and forward protocol (DF) employed over rician fading channel. We also presented the impact of self-interference on system performance in FD systems. The results shows that, performance of the system improved by increasing the rician factor (K). Finally, numerical results were plotted based on obtained expressions.

Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also, multi-step document retrieval often incurs higher number of relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address these challenges, we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process. To improve the retrieval accuracy, we propose a graph-based reranking model that perform multi-document interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA., Comment: Accepted by SIGIR 2021

Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates. Existing studies take the greedy strategy in the sense that they only use samples for ranking at the current hop, and ignore the global information across the whole documents. In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR. Specifically, TPR leverages the scores of a dense model and conditional probabilities to score the full paths. Moreover, to further enhance the performance of the dense ranker in the iterative training, we propose a "thinking" negatives selection method that the top-K candidates treated as negatives in the current hop are adjusted dynamically through supervised signals. After achieving multiple supporting paths through TPR, the EPR component which integrates several fine-grained training tasks for QA is used to select the best path for answer extraction. We have tested our proposed solution on the multi-hop dataset "HotpotQA" with a full wiki set ting, and the results show that TPRR significantly outperforms the existing state-of-the-art models. Moreover, our method has won the first place in the HotpotQA official leaderboard since Feb 1, 2021 under the Fullwiki setting. Code is available at https://gitee.com/mindspore/mindspore/ tree/master/model_zoo/research/nlp/tprr.

We attack the problem of dynamic UAV-based ad-hoc mesh network deployment to find and connect people in an area of interest. Genetic algorithms tune the parameters of potential fields that control UAV movement in order to optimize people coverage and network longevity. We extend earlier work that assumed one-hop communication between UAVs to the more realistic two-hop case and find significant increase in coverage and network longevity. Experimental results show that enabling two-hop communications between UAVs improves the performance on average between 4% to 32.19%.

Underwater communication is very critical in today's world with the advent of IoT devices which require proper communication standards and protocols to be in place for improving performance. Routing protocols is one such important feature of underwater communications which helps devices pick the best route based on different parameters. In this paper we designed and implemented an On-Demand Routing protocol which uses pH of the water and the SNR to choose the best hop while forming a route in underwater communications. The effects of pH on SNR were studied and the various factors affecting pH were observed. A pH model was developed using the data collected from the Hawaiian Ocean Waters.The routing protocol was designed and implemented in UnetStack3 as well.

With the strengthening of epidemic prevention and control in public areas in many areas, in order to ensure the health of people entering public areas, many public areas are equipped with multiple wireless temperature sensors, which not only effectively saves manpower, but also effectively realizes the real-time detection and data transmission of people's body temperature in public areas. a large number of wireless temperature sensor nodes deployed in the public area constitute a wireless sensor network, which is a multi hop self-organizing network system through wireless communication. considering the minimum energy consumption and optimal efficiency of the network, this paper proposes a routing algorithm based on ant colony optimization, which can fully consider the routing changes caused by the constant change of energy in wireless sensor networks. The algorithm is simulated in Visual Studio.Net development environment. Through the experimental simulation, the experimental results are analyzed to find the route with fast convergence and better path, which proves that the algorithm can be well applied in wireless sensor networks.

Background. Return to participation in sport is an important outcome when evaluating the success of anterior cruciate ligament (ACL) reconstruction surgery. A review by Arden et al. (2014) demonstrates that just because an athlete has surgery, it does not automatically mean that they will return to sport. So, the purpose of this study was to assess if patients could return to their pre-injury activity level 8 months after ACLR. Methods. Patients completed IKDC-2000, ACL-RSI questionnaires, underwent FMS, YBT, HOP test sets and drop jump tests, which were evaluated with the LESS. ACL laxity was measured by using a GNRB® device. Participants were 81 patients. They were divided into two groups based on single leg HOP test results. The first group included 30 persons who all had their four single leg HOP test scores above 90%. The second group consisted of 51 persons for whom at least one of the single leg HOP tests scored lower than 90%. Results. IKDC and HOP test results in the first group scored higher than in the second group. Psychological confidence was higher in the first group, who had better HOP test results. For the first group, FMS and LESS results were better than those for the second group. Moderate positive correlation was found between LESS scores and laxity results, FMS, three HOP sub-tests. IKDC had the biggest number of correlations with other tests. Results showed negative and weak correlation with laxity results using 134 N power, as well as positive and moderate correlation with HOP sub-tests, FMS, ACL-RSI. Single HOP for distance, triple HOP for distance, crossover HOP for distance and LESS increased the odds of achieving RTS criteria. No other factors were found to be associated with the accomplishment of reaching RTS criteria after ACLR. Conclusions. The main factor which can help decide if a person can return to their pre-injury level of physical activity are the single leg HOP test and LESS. The use of single leg HOP tests and LESS along with psychological and self-reported evaluations is the best test combination for assessment 8 months after ACLR. Keywords: ACL reconstruction, function tests, return to play, jump tests, psychological readiness.

Energy efficiency of a sensor node is one of the vital and key terms in the operation of Wireless Sensor Networks (WSNs). For developing an efficient network, data aggregation is done taking into consideration different routing algorithms and topologies. This paper develops hierarchical routing algorithms, Vicinity Search Closest Hop (VSCH) and Cluster-Tree which increases the energy efficiency of the network. These algorithms basically consider the node deployment, sink location, grouping of nodes, scheduling, and routing. Routing of data is done through various levels by multi hop technique and data is aggregated at every hierarchical level. Increased network lifetime is achieved with higher energy efficiency and low power consumption.

Multi-hop Question Answering over Knowledge Graphs (KGQA) in previous studies has achieved remarkable results by exploiting the prediction property of Knowledge Graphs (KG) embedding. However, when facing Chinese sentences, its answer selection performs poorly. We improve the method for KGQA by combining the traditional method for KGQA with a lattice based CNN (LCN) model. We refine the granularity of questions and answers to make its coverage more extensive and generalizable, and expand the answer set to improve the performance in single results.

The massive growth of mobile users will spread to significant numbers of small cells for the Fifth Generation (5G) mobile network, which will overlap the fourth generation (4G) network. A tremendous increase in handover (HO) scenarios and HO rates will occur. Ensuring stable and reliable connection through the mobility of user equipment (UE) will become a major problem in future mobile networks. This problem will be magnified with the use of suboptimal handover control parameter (HCP) settings, which can be configured manually or automatically. Therefore, the aim of this study is to investigate the impact of different HCP settings on the performance of 5G network. Several system scenarios are proposed and investigated based on different HCP settings and mobile speed scenarios. The different mobile speeds are expected to demonstrate the influence of many proposed system scenarios on 5G network execution. We conducted simulations utilizing MATLAB software and its related tools. Evaluation comparisons were performed in terms of handover probability (HOP), ping-pong handover probability (PPHP) and outage probability (OP). The 5G network framework has been employed to evaluate the proposed system scenarios used. The simulation results reveal that there is a trade-off in the results obtained from various systems. The use of lower HCP settings provides noticeable enhancements compared to higher HCP settings in terms of OP. Simultaneously, the use of lower HCP settings provides noticeable drawbacks compared to higher HCP settings in terms of high PPHP for all scenarios of mobile speed. The simulation results show that medium HCP settings may be the acceptable solution if one of these systems is applied. This study emphasises the application of automatic self-optimisation (ASO) functions as the best solution that considers user experience.

This paper presents a generalized approach on performance of relay aided communication systems for 5G and beyond scenarios. A dual-hop decode and forwarding scheme is considered in the analysis. The relationship between the outage performance and cumulative distribution function (CDF) of signal to noise ratio (SNR) is exploited to derive a universal expression of outage probability valid for all fading scenarios irrespective of their nature or complexity. Further, an effort is made to parameterise the channel PDF in such a manner that reflects a commonly encountered practical fading scenario faced by current and future wireless communication systems. The analytical results obtained for various cases are validated by Monte-Carlo simulations.

Translation of the essay Les “hop” musicaux (The musical “hop”), written by Emile Jaques-Dalcroze and originally published in Le Rythme magazine (1930). The translation is preceded by a presentation on the origins and developments of the musical “hop”, one of the most important and ludic didactic principles of Dalcroze Eurhythmics, integrated with exercises in solfeggio, improvisation and living plasticity (“plastique animee”). At the end of the essay, to exemplify the use of the musical "hop", Dalcroze introduce a compendium of 55 exercises, a valuable material on a topic that has not been studied much in Brazil.

In this paper, the performance analysis of a reconfigurable intelligent surface (RIS) in a dual-hop decode-and-forward (DF) relay empowered asymmetric radio frequency (RF)/free space optical (FSO) systems is presented. The RIS-assisted RF network is subjected to Nakagami-m distribution while the RIS-assisted FSO networks experience Gamma-Gamma distribution in which both atmospheric turbulence and pointing errors are considered. Thus, the closed-form expressions for the system outage probability and average bit error rate (ABER) are derived with limited number of reflecting elements at RIS-assisted RF network and multiple number of reflecting elements at RIS-assisted FSO network. Further, to obtain more insight about the system characteristic, the asymptotic closed-form expressions are derived at high signal-to-noise ratio (SNR) for the system performance metrics. The results illustrate the impact of the system and channel parameters on the proposed system in terms of atmospheric turbulence, pointing errors under beam width condition, m-fading parameter, and number of reflecting elements. The correctness of the derived analytical expressions is validated via the Monte-Carlo simulations.

We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i.e., the question-answer pairs. We propose a cooperative game approach to deal with this problem, in which how the evidence passages are selected and how the selected passages are connected are handled by two models that cooperate to select the most confident chains from a large set of candidates (from distant supervision). For evaluation, we created benchmarks based on two multi-hop QA datasets, HotpotQA and MedHop; and hand-labeled reasoning chains for the latter. The experimental results demonstrate the effectiveness of our proposed approach.

In this paper, we investigate the secrecy performance of a dual-hop hybrid radio frequency–visible light communication (RF–VLC) system in which we consider a single source antenna (S) transmitting data signals via RF channel through decode and forward (DF) relay (R) toward an authorized receiving antenna (D) at the destination via VLC channel. In this system, we assume that the eavesdropper (E) attempts to eavesdrop the RF channel (S,R) during transmission of the signal. The relay decodes the incoming RF signal into corresponding optical signal and retransmits toward destination via VLC channel (R,D). The RF and VLC channels of the hybrid system are modelled using generalized-K fading distribution and generalized Lambertian radiant intensity distribution respectively. Also the receiver is assumed to be mobile and its mobility is defined by random waypoint (RWP) model. The channels statistics are used to derive the exact analytical expressions for average secrecy capacity and intercept probability in terms of Meijer’s G-function. The derived closed form analytical expressions are the efficient tool to illustrate the impact of variation in different channel parameters on the secrecy performance, namely, shadowing parameters of RF channel, field of view (FOV), semiangle, and the order of Lambertian emission of the VLC channel. Further, numerical analysis is carried out to support the mathematical analysis of the RF–VLC hybrid system.

Generating on-topic responses and understanding the background information of context are both significant for dialogue generation. However, few works simultaneously concentrate on these two issues. For this purpose, we propose an open-domain topic-aware dialogue generation model via joint learning. We first design two-hop based static graph attention mechanism to enhance the semantic representations of context, and then two auxiliary sub-tasks are introduced. Topic Predictor module is designed to focus on the most pertinent topics and Language Modeling module further facilitates learning richer information from context. Experimental study has shown the proposed model’s promising potential. In particular, our model predicts the most topics that best match the query per response. Besides, further analysis proves that our model can generate more diversified and informative responses.

As localization represents the main backbone of several wireless sensor networks applications, several localization algorithms have been proposed in the literature. There is a growing interest in the multi-hop localization algorithms as they permit the localization of sensor nodes even if they are several hops away from anchor nodes. One of the most famous localization algorithms is the Distance Vector Hop (DV-Hop). Aiming to minimize the large localization error in the original DV-Hop algorithm, we propose an improved DV-Hop algorithm in this paper. The distance between unknown nodes and anchors is estimated using the received signal strength indication (RSSI) and the polynomial approximation. Moreover, the proposed algorithm uses a recursive computation of the localization process to improve the accuracy of position estimation. Experimental results show that the proposed localization technique minimizes the localization error and improves the localization accuracy.

In view of the limitations of the opportunistic routing algorithm that divides communities based on interest only considering single community at present, this paper proposes a multi-community opportunistic routing algorithm based on machine learning (MORAML). According to the characteristics of small range and high density of places visited by private cars, this paper analyzes the resident area of users based on clustering method, and takes the center of the area as the hot spot location. Aiming at the problem that K-means algorithm is difficult to select the K value, this paper combines the advantages of DBSCAN and K-means algorithm to design a hybrid clustering algorithm to calculate the hot location of users, and then the clustering method is used to achieve community division of users. Because users have multiple hot spots, they can belong to multiple communities. In the process of message transmission, MORAML makes full use of the multi-community characteristics and centrality of nodes to decide the next hop route, and sets up redundant copy removal mechanism to reduce message transmission and avoid resource waste. Experiments show that MORAML has higher delivery rate, smaller network load and better performance compared with the existing classical routing algorithms.

This article provides an introduction to a dialogue on the impact of artificial intelligence (AI) in shifting the horizons of ignorance and knowledge, inevitability and possibility, reality and hope, its implications for theology and religious studies, and the possible contributions of theology, religious studies, and the humanities to these earth-shaking dialogues.