ACM Transactions on Sensor Networks

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the ...

In Vehicular Edge Computing (VEC), Unmanned Aerial Vehicles (UAVs) have become a feasible solution for addressing high deployment costs faced by base stations in congested roads during peak hours. However, UAVs cannot cache all requested content due to ...

The popularity of IoT devices has penetrated our daily life while posing new challenges in user authentication. Today’s solutions, e.g., passwords, fingerprints, and FaceIDs, primarily rely on specialized sensors or user interfaces to collect user’s ...

End-edge collaborative inference enhances computational efficiency by segmenting a deep neural network (DNN) model into two parts, executed across the end device and the edge node. However, existing collaborative inference strategies often involve ...

With the widespread adoption of deep learning models in wireless sensing, substantial efforts have been made to develop sophisticated models that improve the accuracy and performance of sensing applications. However, the exploration of potential ...

Recent years have witnessed the rapid growth of IoT (Internet of Things). Bluetooth Low Energy (BLE) is one of the most popular wireless protocols to implement IoT applications because of its energy efficiency and low-cost properties. However, the ...

Personalized federated learning (PFL) framework that targets individual models for optimization provides better privacy and flexibility for clients. However, in challenging intelligent sensing applications, the heterogeneous client’s data distributions ...

Intelligent voice systems are widely utilized to control smart home applications, which raises significant privacy and security concerns. Recent studies have revealed their vulnerability to adversarial attacks, replay attacks, etc. However, these attacks ...

In this paper, we investigate the edge cache deployment for high definition (HD) urban map provisioning, which is an essential building block for future autonomous driving. Given a deployment budget, we first formulate a satisfied downloading request ...

Wireless sensor networks (WSNs) leveraging millimeter wave (mmWave) communication for bandwidth-demanding applications is considered in this paper. Despite the large bandwidth, the delivery of delay-sensitive information collected by sensors may still ...

Drones have witnessed extensive popularity among diverse smart applications, and visual SLAM technology is commonly used to estimate 6-DoF pose for the drone flight control system. However, traditional image-based SLAM cannot ensure the flight safety of ...

Abstract: Target material sensing in non-invasive and ubiquitous contexts plays an important role in various applications. Recently, a few wireless sensing systems have been proposed for material identification. In this paper, we introduce mm-CUR, A Novel ...

With the proliferation of sensor networks in urban areas, vast amounts of data from location-based social network platforms are now available, thus enabling the stakeholders to develop location-aware services for the end users. PoI (Point of Interests) ...

The fast development of artificial intelligence and Internet of Things (IoT) technologies has enabled various applications of smart cities, e.g., smart monitoring and surveillance. However, vulnerabilities of IoT devices bring new threats to the security ...

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT ...

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML ...

Edge intelligence (EI) integrates edge computing and artificial intelligence empowering service providers to deploy deep neural networks (DNNs) on edge servers in proximity to users to provision intelligent applications (e.g., autonomous driving) for ...

In the Internet of Things (IoT) era, the pervasive application of tremendous end devices puts forth an unprecedented demand for data processing. To address this challenge, the end-edge-cloud system has emerged as a solution, where task offloading plays a ...

Federated Learning is an emerging application paradigm of edge computing in smart cities. On the one hand, it enables efficient, private, and secure processing of sensitive data. On the other hand, it alleviates the burden of centralized data processing ...

As an emerging learning paradigm, Federated Learning (FL) enables data owners to collaborate training a model while keeps data locally. However, classic FL methods are susceptible to model poisoning attacks and Byzantine failures. Despite several defense ...

Federated learning (FL), which holds promise for use in edge intelligence applications for smart cities, enables smart devices collaborate in training a global model by exchanging local model updates instead of sharing local training data. However, the ...

Smart cities have drawn a lot of interest in recent years, which employ Internet of Things (IoT)-enabled sensors to gather data from various sources and help enhance the quality of residents’ life in multiple areas, e.g. public safety. Accurate crime ...

Vehicle edge computing (VEC) provides efficient services for vehicles by offloading tasks to edge servers. Notably, extant research mainly employs methods such as deep learning and reinforcement learning to make resource allocation decisions, without ...

Single-modal data has a limitation on fatigue detection, while the shortage of labeled data is pervasive in multimodal sensing data. Besides, it is a time-consuming task for board-certified experts to manually annotate the physiological signals, ...

Recent years have witnessed a remarkable proliferation of applications in smart cities. Crowd analysis is a crucial subject, and it incorporates two subtasks in smart city systems, i.e., crowd counting and crowd localization. Nevertheless, the presence of ...

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available ...

Due to an explosive growth in mobile devices and the rapid evolution of wireless communication technologies, local-edge-cloud computing is becoming an attractive solution for providing a higher-quality service by exploiting the multi-computation power of ...

In recent years, the number of devices and terminals connected to the smart city has increased significantly. Edge networks face a greater variety of connected objects and massive services. Considering that a large number of services have different QoS ...

Accurate localization is one of the basic requirements for smart cities and smart factories. In wireless cellular network localization, the straight-line propagation of electromagnetic waves between base stations and users is called line-of-sight (LOS) ...

Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these ...

The smart city is an increasingly popular concept when it comes to urban development. In a smart city, numerous sensor services are generated by IoT sensors in a distributed manner, requiring proper management and effective interaction to guarantee the ...

Despite considerable technological advances for smart cities, they still face problems such as instability of cloud server connection, insecurity during data transmission, and slight deficiencies in TCP/IP network architecture. To address such issues, we ...

Visual target detection based on deep learning with high computing power devices has been successful, but the performance in intelligent agriculture with edge devices has not been prominent. Specifically, the existing model architecture and optimization ...

With the rapid development and application of smart city, Cyber-Physical-Social Systems (CPSS) as its superset is becoming increasingly important, and attracts extensive attentions. For satisfying the smart requirements of CPSS design, a cloud-edge ...

Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-...

Transformer is a popular machine learning model used by many intelligent applications in smart cities. However, it has high computational complexity and it would be hard to deploy it in weak-edge devices. This paper presents a novel two-round offloading ...

As an emerging mobility-on-demand service, bike-sharing system (BSS) has spread all over the world by providing a flexible, cost-efficient, and environment-friendly transportation mode for citizens. Demand-supply unbalance is one of the main challenges in ...

With the development of 5G and Internet of Things technologies, the application process of smart transportation in smart cities continues to advance. Sensors are a key source of information for smart transportation, and their data commonly includes ...

This work presents a novel approach by utilizing Heterogeneous Activation Neural Networks (HA-NNs) to evolve the weights of Artificial Neural Networks (ANNs) for reinforcement learning in console and arcade computer games like Atari's Breakout and Sonic ...

With the rapid growth in the world population, developing agricultural technologies has been an urgent need. Sensor networks have been widely used to monitor and manage agricultural status. Moreover, Artificial Intelligence (AI) techniques are adopted for ...

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It has been widely deployed in real-world systems (e.g. Chrome, iOS, macOS). In LDP-based mechanisms, an aggregator collects private values perturbed by each ...

Cooperative edge caching enables edge servers to jointly utilize their cache to store popular contents, thus drastically reducing the latency of content acquisition. One fundamental problem of cooperative caching is how to coordinate the cache replacement ...

The integration of small cell architecture and edge intelligence is expected to make high-grade mobile connectivity accessible and thus provide smart and efficient services for various aspects of urban life. It is well known that small cell architecture ...

Although Digital Twins (DTs) became very popular in industry, nowadays they represent a pre-requisite of many systems across different domains, by taking advantage of the disrupting digital technologies such as Artificial Intelligence (AI), Edge Computing ...

Face recognition is an essential technology in intelligent transportation and security within smart cities. Nevertheless, face images taken in nighttime urban environments often suffer from low brightness, small sizes, and low resolution, which pose ...

Connectivity restoration is essential for ensuring continuous operation in wireless sensor networks (WSNs). However, existing works lack enough network robustness when suffering from the secondary external damages. In this paper, we propose a novel ...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a user to label new data points with the desired outputs. In robotics, active learning allows a robot to adapt its perception intelligence to a new ...

Due to the ability to simultaneously interrogate multiple tags with a far operating distance, radio frequency identification (RFID) has been widely used in modern farms to improve management efficiency. In smart farm applications, how to quickly locate ...

The agriculture is the backbone of economic system, and it plays an essential part in the survival of a nation’s prosperity. In addition to providing raw materials and food, it also offers numerous employment opportunities. Consequently, agriculture ...

Protecting the intellectual property (IP) of deep neural network (DNN) models becomes essential and urgent with the rapidly increasing cost of DNN training. Fingerprinting is one promising IP protection method that queries suspicious models with specific ...

Carefully perturbing adversarial inputs degrades the performance of traditional machine learning (ML) models. Adversarial machine learning (AML) that takes adversaries into account during training and learning emerges as a valid technique to defend ...

Cyber-security intelligence have made a great impact over healthcare industry where several researchers are developing new techniques to improve security for healthcare systems. Besides, Artificial Intelligence (AI) become the tremendous technology in ...

Smart agriculture which integrates the agriculture with Internet of Things (IoT) has attracted attention since it contributes to increase the productivity and quality of crops, reduce energy consumption and facilitate the farmers. Wireless sensor networks ...

Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existing works mainly exploit dedicated ...

In agriculture, paddy crop monitoring placed a crucial role because it supports food security control. Water shortage, high cost of fertilizers, and soil deterioration were identified as some of the difficulties encountered when monitoring rice crops ...

Sensor network integrates sensor technology, embedded computing technology, modern network and wireless communication technology, distributed information processing technology, etc. It can monitor, perceive and collect the information of various ...

The detection of anomaly status plays a pivotal role in the maintenance of public transportation and facilities in smart cities. Owing to the pervasively deployed sensing devices, one can collect and apply multi-dimensional sensing data to detect and ...

In this paper, we investigate the topic of designing a sensor network by simultaneously optimizing its data energy efficiency and latency. We propose to design a network structure based on the number of nearest neighbors of wireless nodes and provide an ...

Agriculture-based Internet of Things (Ag-IoT) can generate near-real-time quantitative data to analyze crop growth. Unmanned Aerial Vehicle (UAV) based mobile edge computing systems is widely used in Ag-IoT due to the low cost, fast movement and ease of ...

Apple leaf diseases have significant impacts on apple quality and productivity. So, the implementation of accurate disease detection in the early stages is a powerful guarantee for the rapid and high-quality development of the apple industry. However, ...

The recent breakthrough in wireless power transfer technology enables power to be delivered between transceivers, which is quite helpful for sensor-cloud systems. Traditional charging schemes are only suitable for static sensors, while the issue of ...

Communications and Computer Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan

School of Information Technology, Skyline University, Sharjah, 1797, UAE

Smart Sensors are used for monitoring, sensing, and ...

Extensive and exhaustive water utilization for agriculture, industries and ground water consumption for domestic purposes has heavily deterioted the water bodies. Cloud and sensor technology is widely deployed in a several real-time applications, ...

The applications and services offered by the Internet of Things (IoT) have grown significantly during the past few years. Device makers and corporate suppliers have taken notice of this, which has led to a sudden inflow of new-age firms. Confidential data ...

Time series analysis can help build sustainable smart cities with high energy efficiency. Smart cities contain a large number of sensors. The sensor data are mostly complex time series, such as traffic, electricity, and gas emission data. Accurate time ...

Spectrum sensing has a very important role in the operation of cognitive radio network. A proper sensing and utilization improve the performance of cognitive radio network (CRN). Different methodologies were developed in past to improve the sensing and ...

In the agricultural sector, identifying plant diseases is crucial as they hamper the plant's robustness and health, which play a vital role in agricultural productivity. Early detection allows farmers to take proper measurements and save crops from ...

Internet of Vehicles(IoV) enables vehicles to generate and share messages to improve transportation safety and efficiency, especially in a smart city scenario where modern communication technology is utilized. The current IoV, however, faces three main ...

Trajectory planning is the core technology in the field of mobile robot research. However, the traditional trajectory planning algorithm has the problems of low accuracy and high complexity. Therefore, we propose a trajectory planning algorithm for target ...

Contact tracing is an important technique to reduce the impact of infectious diseases in smart cities. Smart phones equipped proximity sensors can be used to enable contact tracing, however accuracy of detection and energy efficiency is a key challenge. ...

For the heterogeneous big data parallel computing model, two levels of parallelism between nodes are not considered, resulting in low efficiency of heterogeneous big data parallel computing and bandwidth to send and receive information, high communication ...