In the continuous iteration of software, synchronizing the updates of test code and production code is crucial for ensuring project quality. However, in actual development, test code maintenance often lags behind the evolution of production code, which can lead to issues such as invalid functionality verification. Existing test code repair methods based on pre-trained models or Large Language Models (LLMs) show less effective or low generalization due to their lack of precise contextual awareness or strong limitations in use scenarios.

Blockchain data encoding has been proposed as a solution to reduce the storage load on full nodes in blockchain systems. However, the high latency and cost associated with the decoding process make coding techniques difficult to apply to frequently accessed blockchain data. This limitation is particularly significant in blockchain applications for the Internet of Things, where nodes generally have limited storage capabilities. To address this issue, we propose a novel blockchain encoding storage architecture. We classify the frequently used state data in blockchain into hot data and cold data, with hot data stored normally and cold data encoded. In addition, we designed a new transaction processing mechanism that prioritizes transactions involving specific data within a single block production process, thereby reducing the frequency of data decoding. At the same time, we use erasure codes and Merkle trees to ensure the security of the stored data. Experimental results show that in our approach, the account data storage on full nodes is significantly lower than that of normal full nodes, while the block transaction volume and block generation time are not substantially different from the normal state. Finally, we verify the robustness of our method against node dropouts through experiments.

Commits messages play a crucial role in helping developers efficiently comprehend code modifications. Due to the time pressure of project iteration or poor message-writing practices, many commits suffer from missing messages. To address this issue, researchers have explored the automated generation of commit messages. Because of the truncation mechanism of the learning-based model, most of the current studies focus on code changes appearing at the beginning of a commit into the model for commit message generation. This may not be the best strategy for commit message generation because each code change in a commit contributes unequally to its overall purpose. To better generate commit messages, we propose a novel method that identifies the core code change in a commit for commit message generation. Specifically, we employ a method to predict the relative importance of the classes contained in a commit, and the code change of the class with the highest importance score (i.e., core change) is used to generate the commit message. Incorporating core change information can boost the performance of other existing methods (such as NMT, NNGen, and CoreGen). Building on this insight, we develop CCGen—a Core Change-Based Generation model that integrates a Transformer architecture with CodeBERT-enhanced encoding to leverage code semantics. The experiment demonstrates that the proposed method for commit message generation outperforms the state-of-the-art by 18.47% on average across seven metrics including 19.97 on ROUGE-L.

Coded blockchain leverages error correction codes to create coded fragments that are then stored in a distributed manner by nodes. It significantly reduces the storage requirement of conventional blockchain. A key limitation, however, is that as nodes store arbitrary coded fragments, which leads to high transmission overhead or decoding failure. To this end, this paper introduces a novel reinforcement learning approach to assign coded fragments to nodes. Specifically, nodes learn to store coded fragments based on feedback from other nodes, which considers their block decoding process. To study its efficacy, this paper compares the proposed approach with existing centralized and distributed coded fragments assignment solutions. The simulation results show that our approach outperforms existing solutions by 6 % in terms of loss function and by 20 % in transmission overhead.

Internet of Things (IoT) systems are increasingly widespread across various fields. Concurrently, vulnerabilities in IoT devices are continuously emerging, potentially leading to severe consequences, such as information leakage, system failure, or even resource abuse. For IoT system developers, understanding the characteristics of these critical vulnerabilities is crucial for safeguarding the security of IoT systems. However, existing studies on IoT vulnerabilities have not specifically focused on such severe or critical vulnerabilities, i.e., high-risk vulnerabilities. Moreover, previous works analyzed IoT vulnerabilities based on unofficial information sources, such as online reports, GitHub issues, or open-sourced projects. To fill this gap, this article presents the first large-scale empirical study on high-risk IoT vulnerabilities based on the well-known vulnerability data source, i.e., the national vulnerability database (NVD), which is maintained by the U.S. government. We constructed a database consisting of 1739 IoT-related vulnerabilities archived over the last two decades (from 1999 to 2023), including 1076 high-risk vulnerabilities for analysis. We classified the high-risk vulnerabilities into four categories, consisting of 25 different weakness types. We further collected 11 detection tools and summarized their capabilities in detecting IoT vulnerabilities. Our study sheds lights on new findings and insights for developers to secure the IoT system.

This article considers maximizing coverage quality in Internet of Things (IoT) networks using unmanned aerial vehicles (UAVs) to augment the link from solar-powered devices to a sink/gateway. Specifically, it aims to jointly optimize the assignment of UAVs to hovering points or a charging station, time in which devices monitor targets, and the amount of data transmitted by devices. These quantities are optimized over a given planning horizon using a mixed integer linear program (MILP). Further, this article presents a heuristic method named decoupled energy aware algorithm (DEAA) to optimize the said quantities. In addition, it outlines a model predictive control (MPC) approach that only requires current and historical energy arrivals information of devices. The simulation results showed that DEAA and MPC achieved 80.58% and 61.19% of the optimal results computed by MILP.

Artificial intelligence (AI) integration in communication is crucial for 6G. It optimizes terrestrial communication and computing resource usage in the Internet of Things (IoT) using AI techniques, such as supervised learning for data analysis and reinforcement learning for resource allocation. However, in remote areas, i.e., oceans and deserts, IoT devices lose connection due to limited terrestrial coverage. Low Earth Orbit Satellite (LEOS) offers low-latency, high-bandwidth access in these unconnected regions. However, power and computing limitations on both IoT devices and LEOSs present challenges for continuous service. To this end, we present an LEOS-assisted open radio access network (RAN) Architecture (LO-RAN) where an RAN intelligence controller (RIC) is integrated to provide AI abilities. We formulate a joint Offloading decision, Path selection, and Resource allocation problem (OPR) to minimize the weighted energy consumption and latency of LO-RAN. We proposed a Joint Optimization for the Offloading decision, Path selection, and Resource allocation (JOOPR) algorithm. It selects contact and processing LEOSs for path selection, uses proximal policy optimization (PPO) for offloading decisions, and applies Karush-Kuhn–Tucker (KKT) to solve resource allocation. The outputs from path selection and resource allocation contribute to the reward that feeds into the PPO. We conduct numerical simulations to compare the proposed JOOPR with the state-of-the-art approaches. The results show that JOOPR reduces energy consumption and latency by at most 28.75% and 33.01%, respectively.

Complete targets coverage is required by many Internet of Things (IoT) applications. In this respect, an important goal is to maximize the number of time slots with complete targets coverage. Achieving such coverage is challenging when devices experience spatio-temporal energy arrivals. To this end, this article outlines a deep learning assisted approach that has an offline stage whereby it determines and stores an exhaustive collection of optimal activation schedules based the energy levels and arrivals of devices. In addition, it presents a network partitioning and training strategy, and outlines an algorithm to mend coverage holes in its online stage. We have compared the proposed approach with the optimal solution, and also a state-of-the-art heuristic algorithm. The results show that our solution achieves 94% of the optimal coverage lifetime. Moreover, the proposed approach has a 35% smaller optimality gap as compared with the said heuristic algorithm.

In this article, we consider an unmanned aerial vehicle (UAV) with an in-band full-duplex radio that is used to interconnect industrial Internet of things (IIoT) devices and exploit their computation and energy resources to help process data. We formulate a mixed integer linear program to optimize the first sampling rate of each device, second amount of data the UAV transmits and receives to/from a device, third position of the UAV over time, and finally the number of virtual machines used by devices and UAV to compute data. We also propose a distributed protocol to determine quantity using aforementioned points. Our results show that an IBFD-UAV has a higher max–min computed sampling rate as compared to when the UAV uses a half duplex radio. Moreover, the said protocol achieves a max–min rate that is 80% optimal.

Smart contracts are programs that permanently store and automatically execute on the blockchain system such as Ethereum. Due to the non-tamperable nature of the underlying blockchain, smart contracts are difficult to update once deployed, which requires redeploying the contracts and migrating the data. It means that the observation of smart contract evolution in the real world makes more sense. Hence, in this paper, we conducted the first large-scale empirical study to characterize the evolution of smart contracts in Ethereum. For evolution identification, we presented a contract similarity-based search algorithm, digEvolution, and evaluated its effectiveness with five different search strategies. Then we applied this algorithm to 80,152 on-chain contracts we collected from Ethereum, to dig out the evolution among these contracts. We then explored three research questions. We first studied whether the evolution of smart contracts is common (RQ1), then we studied how do the Gas consumption (RQ2) and the vulnerability (RQ3) of smart contracts vary during the evolution. Our research results show that the evolution of smart contracts is not very common. There are some contract components that have vulnerability but still be called by users. The Gas consumption of most smart contracts doesn’t vary during the evolution, contract is Gas-efficient before and after the evolution. The vulnerability of most smart contracts doesn’t vary during the evolution, both are secure before and after the evolution.

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions; b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensemble graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance.

High-quality and appropriate commit messages help developers to quickly understand and track code evolution, which is crucial for the collaborative development and maintenance of software. To relieve developers of the burden of writing commit messages, researchers have proposed various techniques to generate commit messages automatically, among which learning-based techniques have proven to be promising.

In recent years, money laundering crimes on blockchain, especially on Ethereum, have become increasingly rampant, resulting in substantial losses. The unique features of money laundering on Ethereum, such as decentralization and pseudonymity, pose new challenges for Ethereum anti-money laundering. Specifically, the existence of dense and extensive laundering gangs and intricate multilayered laundering pathways makes it exceptionally challenging for regulators to identify suspicious accounts and trace money flows. To address this issue, we propose an innovative DenseFlow framework that effectively identifies and traces money laundering activities by finding dense subgraphs and applying the maximum flow idea. We conduct multiple experiments on four datasets from Ethereum to validate the effectiveness of our approach. The precision of our DenseFlow is 16.34% higher than the start-of-the-art comparison methods on average, highlighting its distinctive contribution to tackling money laundering issues on blockchain.

This article considers the problem of monitoring one or more mobile targets over a given planning horizon. Unlike previous works, it leverages ambient backscatter communication to reduce the energy expenditure of sensor nodes in order to prolong coverage lifetime. We outline a mixed integer linear program (MILP) that aims to maximize the number of time slots in which all mobile targets are monitored by a sensor node; a.k.a. complete mobile targets coverage. For a given targets trajectory, it outputs the activation time of sensor nodes to ensure all targets are monitored by at least one sensor node. Further, we propose an algorithm called maximum energy opportunity selection (MEOS), which uses the energy level of sensor nodes to set their operation mode. The simulation results show that the complete mobile targets coverage lifetime of MILP and MEOS algorithms improves when nodes employ backscatter communications. Specifically, it leads to a 79% improvement in complete mobile targets coverage lifetime.

The Internet of Things (IoT) system provides sensing and computing services via terrestrial networks. However, the restricted coverage of terrestrial networks, such as base stations, limits the ubiquitous IoT services. Low-Earth orbit (LEO) satellites are able to provide network coverage for terrestrial IoT devices in unconnected scenarios, e.g., maritime. IoT devices in such scenarios usually have restricted onboard computation and power resources. In this article, we present an LEO-assisted IoT network (L-IoT) architecture where a device splits its task and offloads a portion of its task to the LEO to process within the coverage time. We formulate a task split problem with communication and computation resource allocation (SCC) to minimize the L-IoT energy consumption. We proposed an alternating optimization for split ratio and resource allocation (AOSR) algorithm. In particular, we use the outputs of Karush-Kuhn–Tucker (KKT) for resource allocation as part of the reward that feeds twin-delayed deep deterministic policy gradient. Lastly, the results of numerical simulations show that the proposed AOSR approach reduces 12.7% energy consumption compared to soft actor-critic (SAC) and 15% to deep deterministic policy gradient (DDPG).

A key constraint that limits the implementation of blockchain in Internet of Things (IoT) is its large storage requirement resulting from the fact that each blockchain node has to store the entire blockchain. This increases the burden on blockchain nodes, and increases the communication overhead for new nodes joining the network since they have to copy the entire blockchain. In order to reduce storage requirements without compromising on system security and integrity, coded blockchains, based on error correcting codes with fixed rates and lengths, have been recently proposed. This approach, however, does not fit well with dynamic IoT networks in which nodes actively leave and join. In such dynamic blockchains, the existing coded blockchain approaches lead to high-communication overheads for new joining nodes and may have high-decoding failure probability. This article proposes a rateless coded blockchain with coding parameters adjusted to network conditions. Our goals are to minimize both the storage requirement at each blockchain node and the communication overhead for each new joining node, subject to a target decoding failure probability. We evaluate the proposed scheme in the context of real-world Bitcoin blockchain and show that both storage and communication overhead are reduced by 99.6% with a maximum 10−12 decoding failure probability.

Devices in Internet of Things (IoT) networks are likely to operate over a shared medium, and thus they have to use a channel access protocol to minimize or avoid collision. Further, they may have to harvest radio frequency (RF) energy in order to transmit or/and receive data. To this end, this survey presents the first comprehensive review of prior works that employ contention-based and contention-free protocols in IoT networks with one or more dedicated RF energy sources. Specifically, these protocols work in conjunction with RF-energy sources to deliver energy delivery or/and data. In this respect, this survey covers protocols based on Aloha, carrier sense multiple access (CSMA), polling, and dynamic time division multiple access (TDMA). Further, it covers protocols that assume a receiver with successive interference cancellation capability. It highlights key issues and challenges addressed by prior works, and provides a qualitative comparison of these works. Finally, it identifies gaps in the literature and presents a list of future research directions.

In this article, we study an industrial Internet of Things (IIoT) network with a sink/gateway that is capable of decoding multiple transmissions via successive interference cancellation (SIC), and energy harvesting devices tasked with providing complete targets coverage of targets. In particular, we study a novel question: how to schedule the sensing and transmission of these devices to ensure complete target coverage over time? We outline a mixed integer linear program (MILP) and two heuristic solutions that jointly optimize the active time and transmit power of devices. Our simulation results show that the complete targets coverage lifetime of our heuristic solutions is within 80% of the optimal complete targets coverage lifetime, as computed by MILP.

This article studies a coverage problem in Internet of things (IoT) networks using unmanned aerial vehicles (UAVs) supported by solar-powered charging platforms. The problem at hand is to determine an assignment of UAVs to either a charging station or a monitoring point over a planning horizon. A key constraint is$K$-coverage, where given a set of$\mathcal {M}$points,$K$of these points must be monitored by a UAV in each time slot. In this respect, the paper aims to design UAVs assignment solutions that yield the longest$K$-coverage lifetime. We formulate a novel mixed integer linear program (MILP) to jointly optimize UAVs assignments over a given planning horizon. The problem is challenging as the energy level of charging platforms and UAVs are coupled across time slots. Moreover, the formulated MILP requires non-causal energy arrivals information at charging platforms. To this end, we outline a model predictive control (MPC) and a Monte Carlo tree search (MCTS) based solution that use non-causal energy arrivals information. The simulation results show that MPC and MCTS achieve approximately 81.04% and 67.07% of the optimal results computed by MILP.

Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQA due to the distribution gap between single-turn and multi-turn datasets. On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. The proposed method consists of three parts, namely, a QA pair Generator, a QA pair Reassembler, and a question Rewriter. Given a sample consisting of context and single-turn QA pairs, the Generator obtains candidate QA pairs and a knowledge graph based on the context. The Reassembler utilizes the knowledge graph to get sequential QA pairs, and the Rewriter rewrites questions from a conversational perspective to obtain a multi-turn dataset S2M. Our experiments show that our method can synthesize effective training resources for CQA. Notably, S2M ranks 1st place on the QuAC leaderboard (https://quac.ai/) at the time of submission (Aug 24th, 2022).

Knowledge-grounded dialogue (KGD) has become increasingly essential for online services, enabling individuals to obtain desired information. While KGD contains knowledge information, most knowledge points are fragmented and repeated in dialogues, making it difficult for users to quickly grasp complete and key information from a collection of sessions. In this paper, we propose a novel task of dialogue-grounded knowledge points generation (DialKPG) to condense a collection of sessions on a topic into succinct and complete knowledge points. To enable empirical study, we create TopicDial and OpenDial corpus based on two existing knowledge-grounded dialogue corpus FaithDial and OpenDialKG by a Three-Stage Annotation Framework, and establish a novel approach for DialKPG task, namely MSAM (Multi-Level Salience-Aware Mixture). MSAM explicitly incorporates salient information at the token-level, utterance-level, and session-level to better guide knowledge points generation. Extensive experiments have verified the effectiveness of our method over competitive baselines. Furthermore, our analysis shows that the proposed model is particularly effective at handling long inputs and multiple sessions due to its strong capability of duplicated elimination and knowledge integration.

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49% of nodes are malicious.

The online performance of customer service bots is often less than satisfactory because of the gap between limited training data and real-world user questions. As a straightforward way to improve online performance, model iteration and re-deployment are time consuming and labor-intensive, and therefore difficult to sustain. To fix badcases and improve online performance of chatbots in a timely and continuous manner, we propose a data-centric solution consisting of three main modules: badcase detection, bad case correction, and answer extraction. By making full use of online model signals, implicit user feedback and artificial customer service log, the proposed solution can fix online badcases automatically. Our solution has been deployed and bringing consistently positive impacts for hundreds of customer service bots used by Alipay app.

This article studies targets coverage in an energy harvesting (EH) Internet of Things (IoT) network. Specifically, it addresses the problem of activating subsets of EH sensor nodes to monitor targets, such as valuable assets or the ingresses and egresses of a building. A key requirement, so called complete targets coverage, is that all targets must be monitored by at least one sensor node at all times. To meet this requirement, we need to derive set covers, where each set cover is comprised of one or more sensor nodes that are activated simultaneously in each time slot. To this end, we show for the first time how the belief propagation message-passing framework can be used to derive these set covers. Advantageously, our approach does not require future energy arrivals information at devices. The results show that our message-passing approach is within 90% of the optimal coverage lifetime.

Blockchain is the foundation of emerging applications, such as smart contracts, nonfungible token (NFT), and metaverse. A key issue is that blockchain requires massive storage space, which limits its deployment in resource-limited end devices, e.g., Internet of Things. Recently, coded blockchain is proposed to reduce the storage requirement of blockchain while guaranteeing its security and data integrity. Coded blockchain encodes blocks into coded symbols, which are then distributively stored by clients. A key challenge when applying coded blockchain in resource-restricted networks is to ensure all clients store the same, and also the minimum, number of coded blocks. To this end, this article addresses a novel problem that minimizes the maximum (min–max) storage requirement of clients. It formulates the said problem as an integer linear program (ILP). It then proposes centralized algorithms to improve the computational efficiency of storage assignments. Moreover, it presents distributed algorithms that satisfy the distributive property of blockchain. Numerical results show that the proposed distributed algorithm with a short length code reduces the min–max storage of clients by 80% compared with traditional blockchain. In addition, the computational complexity of distributed algorithms is significantly lower than centralized algorithms.

The detection of mild depression is conducive to the early intervention and treatment of depression. This study explored the fusion of electroencephalography (EEG) and pupil area signals to build an effective and convenient mild depression recognition model. We proposed Mutual Information Based Fusion Model (MIBFM), which innovatively used pupil area signals to select EEG electrodes based on mutual information. Then we extracted features from EEG and pupil area signals in different bands, and fused bimodal features using the denoising autoencoder. Experimental results showed that MIBFM could obtain the highest accuracy of 87.03%. And MIBFM exhibited better performance than other existing methods. Our findings validate the effectiveness of the use of pupil area as signals, which makes eye movement signals can be easily obtained using high resolution camera, and the EEG electrode selection scheme based on mutual information is also proved to be an applicable solution for data dimension reduction and multimodal complementary information screening. This study casts a new light for mild depression recognition using multimodal data of EEG and pupil area signals, and provides a theoretical basis for the development of portable and universal application systems.

In recent years, depression recognition using physiological signals has achieved certain progress, but mild depression recognition is still in its infancy. Early detection can prevent the development of depression, and combining multiple modalities for analysis has been proved effective in the domain of mental disorders detection. Electroencephalogram (EEG) and eye movements (EM) are widely used to identify depression. However, the problem of using physiological signals to detect mental illness is that the generalization ability of the model is not strong, which is caused by individual differences. In view of the above problems, this paper proposes a hybrid fusion model based on deep belief network (DBN) and secondary classifier, called HFMBDSC, which first uses unsupervised DBN to fuse EEG features (linear, nonlinear features and network features) at the feature level. DBN transforms EEG features into another form to mitigate the effects of individual differences in EEG, and obtains DBN features that can more comprehensively represent EEG information. In the next step of decision level fusion, DBN features and EM features jointly make the final decision using the three classifiers that perform best when using single modality. The results show that DBN can improve the classification accuracy and effectively reduce the influence of individual differences in EEG features through visual analysis of feature space. The classification performance of HFMBDSC is significantly improved compared to the traditional single modality results. The highest accuracy rate of 89.54% is obtained under 10-fold cross-validation. These results suggest that mild depression recognition based on HFMBDSC is promising.

Secure file storage and distribution is a key challenge in the information era. It is important to ensure the files are not tampered or eavesdropped during storage and transmission. A promising solution is to store the files in blockchain. However, the massive blockchain data makes it hard to operate a file blockchain in resource restricted end devices. To this end, this paper proposes a File Storage in Coded Blockchain (FSC) solution that leverage the advantage of error correction code. In particular, the block data can be encoded and distributed stored at different nodes to reduce the storage requirement. In addition, this paper propose a Coding Practical Byzantine Fault Tolerant (C-PBFT) consensus protocol to distribute the coded information with reduced communication overhead. Simulation results show that the proposed FSC significantly reduces the storage requirement as compared with traditional blockchain technologies.

This article studies a novel wireless-powered Internet of Things (IoT) network that consists of: 1) a hybrid access point (HAP) that charges devices and also helps facilitate backscattering transmissions; 2) devices that use active radio frequency (RF) and backscattering transmissions; and 3) a mobile data collector. Our aim is to maximize the amount of data received by the HAP and data collector. The main problem is to determine the charging duration of the HAP and link activation schedule of devices. We formulate a novel mixed-integer linear program (MILP) and also propose a heuristic algorithm named reduced-set linear program approximation (RS-LPA). The results show that: 1) throughput increases with the number of backscatter transmission sets; 2) smaller amount of data is uploaded to the mobile collector when sampling cost is low; and 3) the throughput of RS-LPA is on average 10.55% lower than MILP.

Recently, many works seek to exploit the negligible transmission cost of backscattering radio frequency (RF) signals, and also demonstrated its feasibility in allowing passive or batteryless tags to communicate over multiple hops. In this context, this article studies data collection in amultihopInternet of Things (IoT) wireless network consisting of tags equipped with sensor(s). These tags forward data via tag-to-tag communications to a gateway. Our aim is for the gateway to collect the maximum amount of data from tags over a given time frame. To do so, we optimize the time used by tags to sample their environment and data transmission, which involves solving an NP-hard link scheduling problem. We present a mixed integer nonlinear program (MINLP) to determine the transmitting tags in each time slot as well as the sensing duration of tags. We also propose a heuristic, called Max-L, that aims to maximize the number of links in each transmission set in order to reduce the transmission time of samples. Our results show that Max-L collects 85% of the optimal amount of samples.

This article studies the complete targets coverage problem in a novel and practical context: sensor nodes operating in an Internet of Things (IoT) network that have a dual-battery system with nonideal properties. Specifically, sensor nodes have batteries that cannot be charged and discharged simultaneously. Also, sensor nodes must fully discharge/charge a battery before it is charged/discharged again. We outline a mixed-integer linear program (MILP) and use it to optimize the activation schedule of sensor nodes. Its objective is to maximize complete targets coverage lifetime. We also propose a heuristic solution named battery switching aware algorithm (BSAA) to solve large problem instances. Simulation results show the performance of BSAA achieves approximately 98% of the coverage lifetime of MILP. In addition, equipping sensor nodes with a dual battery system prolongs the coverage lifetime by up to 70.2%.

This article studies a novel problem that aims to maximize the number of uploaded samples by devices in wireless-powered Internet of Things (IoT) networks. To do so, it takes advantage of ambient backscatter communications (AmBC) to help sensor devices conserve energy, and thus leaving them with more energy to collect samples. We outline a mixed-integer linear program (MILP) that aims to determine the operation mode of each device in each time slot in order to maximize the total amount of uploaded samples. We also present a heuristic approach to set the operation mode of devices based on their residual energy and data. Our results show that as compared to the case without AmBC, the total data uploaded by devices increases by 48% and 45% for the MILP and heuristic, respectively-both of which exploit AmBC.

Measuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the word frequency change. In this paper, we use both frequency changes and word semantic shifts to measure document influence by developing a neural network framework. Our model has three steps. Firstly, we train the word embeddings for different time periods. Subsequently, we propose an unsupervised method to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our model outperforms DIM.

Future Internet-of-Things (IoT) networks will consist of radio-frequency (RF) energy harvesting devices that are charged by solar-powered power beacons (PBs). To this end, this article aims to maximize the minimum data rate of devices acting as sources operating in a multihop IoT network. The main problem is to decide the amount of energy delivered by solar-powered PBs, routing of data from each source, and link scheduling, which determines the capacity of links. To this end, we make two contributions. First, we present a linear program (LP) to optimize the max-min rate of sources. Our LP considers nonlinear RF conversion at devices, energy storage loss at devices due to the imperfect battery, and time-varying channel quality, which affect the amount of energy harvested by devices. The second contribution is a novel distributed protocol called distributed max-min rate allocation (D-MRA), whereby devices only need local information, such as their battery and data buffer state to make decisions. Our results show that the max-min rate of D-MRA is 58.25% that of LP, which requires global information, in all tested cases.

Backscatter communication can potentially find wide Internet of Things (IoT) applications because of its negligible energy consumption. Traditional backscatter communication relies on either dedicated radio-frequency (RF) sources, such as RF identification readers and power beacons, or ambient RF sources, e.g., TV and cellular signals. In this article, we study a backscatter-assisted wirelessly powered IoT system where devices can backscatter when nearby devices are actively transmitting via RF. Our objective is to determine the transmission schedule for all devices that maximizes the minimum system throughput. We formulate such a scheduling problem as a linear program for both linear and random networks. A key step in the formulation is to identify the groups of devices that can simultaneously backscatter without causing interference. Simulation results show that the max-min system throughput in linear and random networks can be increased by 46 and 180 times, respectively, by using the proposed backscatter-assisted schedule as compared with the traditional time-division multiple access (TDMA).

We consider Rechargeable Wireless Sensor Networks (R-WSNs) where nodes harvest energy from both solar and the Radio Frequency (RF) transmissions of their neighbors. Our aim is to maximize the minimum source or sensing rate of nodes. This rate is determined by the available energy at sensor nodes as well as link capacity, which is determined by the set of transmitting nodes. In this paper, we first study and show the benefits of energy sharing. Intuitively, a sensor node should share its energy if doing so increases source rates. We present a novel Linear Program (LP) to determine the routing, link schedule, energy transmission, and reception time that maximize the minimum source rate of a given R-WSN. Our numerical results indicate that, on average, the minimum transmission rate of sensor nodes increased by 16.03 percent when nodes share energy. This motivates the development of a practical protocol called E-RSVP that iteratively increases the time slots of each source node. It also considers using time slots for transmission or reception of energy. Our simulation results show E-RSVP yields minimum source rates that are 14.80 percent higher as compared to the case without energy sharing.

In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a sampling time to collect sensory data, carry out computation, and transmit sensed data to the HAP. A key challenge, however, is random channel gains, which cause sensor nodes to receive varying amounts of Radio Frequency (RF) energy. To this end, we formulate a stochastic program to determine the charging time of the HAP and sampling time of sensor nodes. Our objective is to minimize the expected penalty incurred when sensor nodes experience an energy shortfall. We consider two cases: single and multi time slots. In the former, we determine a suitable HAP charging time and nodes sampling time on a slot-by-slot basis whilst the latter considers the best charging and sampling time for use in the next T slots. We conduct experiments over channel gains drawn from the Gaussian, Rayleigh or Rician distribution. Numerical results confirm our stochastic program can be used to compute good charging and sampling times that incur the minimum penalty over the said distributions.

Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti‐noise performance, over‐segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over‐segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti‐noise performance, highly precise segmentation result, and are useful in effectively segmenting low‐contrast images.

Wireless sensor networks can be used to monitor targets continuously. This assumes sensor nodes have energy neutral operation, whereby the energy consumed to monitor targets is less than their harvested energy. In this paper, we consider a new problem: minimum energy harvesting node placement for energy neutral coverage and connectivity (MEHNP-ENCC). We aim to determine the locations to place the minimal number of nodes used for sensing and relaying such that deployed nodes 1) cover all targets, 2) have a path to the sink, and 3) have energy neutral operation. We first model MEHNP-ENCC as a mixed integer linear program (MILP). After that we propose an MILP-based approach called greedy MILP (GMILP), whereby a greedy heuristic is used to generate a collection of locations. We also propose two heuristics: 1) DirectSearch considers locations that cover one or more lines connecting targets to the sink, whilst 2) GreedySearch also considers locations farther afield from the said lines that have a high recharging rate. Simulation results show that DirectSearch requires 20% more sensor nodes than the optimal solution whilst this value is 10% for GreedySearch and GMILP.

A fundamental problem in energy harvesting Wireless Sensor Networks (WSNs) is to maximize coverage, whereby the goal is to capture events of interest that occur in one or more target areas. To this end, this paper addresses the problem of maximizing network lifetime whilst ensuring all targets are monitored continuously by at least one sensor node. Specifically, we will address the Distributed Maximum Lifetime Coverage with Energy Harvesting (DMLC-EH) problem. The objective is to determine a distributed algorithm that allows sensor nodes to form a minimal set cover using local information whilst minimizing missed recharging opportunities. We propose an eligibility test that ensures the sensor nodes with higher energy volunteer to monitor targets. After that, we propose a Maximum Energy Protection (MEP) protocol that places an on-duty node with low energy to sleep while maintaining complete targets coverage. Our results show MEP increases network lifetime by 30% and has 10% less redundancy as compared to two similar algorithms developed for finite battery WSNs.