Opinion survey research is a crucial method used by social scientists for understanding societal beliefs and behaviors.Traditional methodologies often entail high costs and limited scalability, while current automated methods such as opinion synthesis exhibit severe biases and lack traceability.In this paper, we introduce SUR-VEYPILOT, a novel finite-state orchestrated agentic framework that automates the collection and analysis of human opinions from social media platforms.SURVEYPILOT addresses the limitations of pioneering approaches by (i) providing transparency and traceability in each state of opinion collection and (ii) incorporating several techniques for mitigating biases, notably with a novel genetic algorithm for improving result diversity.Our extensive experiments reveal that SURVEYPILOT achieves a close alignment with authentic survey results across multiple domains, observing average relative improvements of 68.98% and 51.37% when comparing to opinion synthesis and agent-based approaches.Implementation of SURVEYPILOT is available on https: //github.com/thanhpv2102/SurveyPilot

SUMMARY: Herein, we present COCOMAPS 2.0, for the analysis, visualization, and comparison of the interface in protein-protein and protein-nucleic acid complexes. COCOMAPS 2.0 complements the residue-level and buried surface area analyses of the original COCOMAPS tool with a comprehensive and accurate atomic-level characterization of the interface, enabling detailed interpretation of molecular recognition. Furthermore, it provides a greatly enhanced flexibility, interactivity, and efficiency in graphical visualizations. AVAILABILITY AND IMPLEMENTATION: COCOMAPS 2.0 is accessible as a public web tool at https://aocdweb.com/BioTools/cocomaps2 and as a standalone code at https://doi.org/10.5281/zenodo.17390665.

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Current FPGA programming tools require extensive hardware-specific manual code tuning to achieve performance, which is intractable for most software application teams. We present HPVM2FPGA, a novel end-to-end compiler and auto-tuning system that can automatically tune hardware-agnostic programs for FPGAs. HPVM2FPGA uses a hardware-agnostic abstraction of parallelism as an intermediate representation (IR) to represent hardware-agnostic programs. HPVM2FPGA's powerful optimization framework uses sophisticated compiler optimizations and design space exploration (DSE) to automatically tune a hardware-agnostic program for a given FPGA. HPVM2FPGA is able to support software programmers by shifting the burden of performing hardware-specific optimizations to the compiler and DSE. We show that HPVM2FPGA can achieve up to 33×speedup compared to unoptimized baselines and can match the performance of hand-tuned HLS code for three of four benchmarks. We have designed HPVM2FPGA to be a modular and extensible framework, and we expect it to match hand-tuned code for most programs as the system matures with more optimizations. Overall, we believe that it constitutes a solid step closer to fully hardware-agnostic FPGA programming, making it a suitable cornerstone for future FPGA compiler research.

Summary Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.

The industrial control and automation systems have played an increasingly important role in critical manufacturing processes. In such systems, many Internet of Things devices continuously collect large number of streaming data for real-time processing. Verifiable data streaming (VDS) addresses such authenticity issue for streaming data, but most VDS schemes are not efficient and lightweight, do not support range querying, and cannot be used in practice. To improve the efficiency and achieve a verifiable range query in data streaming, we present here a new primitive, namely, a chameleon authentication tree with prefixes (PCAT), which is extended from the PBTree and chameleon authentication tree. Our scheme is not only lightweight but also supports dynamic expansion and verifiable range query in data streaming, making it more suitable for resource-constrained devices. We separate the PCAT's algorithms into the following phases: initialization, data appending, query, and verification. Our analyses prove that the PCAT satisfies all the security requirements of VDS. Moreover, an efficiency analysis and performance evaluation demonstrate that our scheme not only supports lightweight data streaming authentication but also has high efficiency, which means that the PCAT is easier to apply in the industrial control and automation systems.

As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

The localization accuracy is critical for the development of future autonomous systems and location-based services. The accuracy level for localization is difficult to achieve in the case of urban and GPS denied environments due to high scattering. Fingerprint-based localization techniques promise to address these challenges. However, this technique demands to build a radio map before localization, which is a time-consuming and labor-intensive task. This article designs a crowd-sourced based localization system to address the radio map building problem in fingerprinting localization system. In this method, the first initial radio map is constructed from the path-loss RSS model, followed by the update of the fingerprints with crowd-sourcing. Finally, the vehicle location is estimated from the RSS sample by matching it with an updated radio map with a deep learning algorithm. The main advantage of the proposed approach is the calibration-free crowd-sourced fingerprint generation and its applicability in various location-based services in urban infrastructure.

Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure service offloading method, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

Internet of Things (IoT) is playing a promising role in e-healthcare applications in the recent decades; nevertheless, security is one of the crucial challenges in the current field of study. Many healthcare devices (for instance, a sensor-augmented insulin pump and heart-rate sensor) collect a user’s real-time data (such as glucose level and heart rate) and send them to the cloud for proper analysis and diagnosis of the user. However, the real-time user’s data are vulnerable to various authentication attacks while sending through an insecure channel. Besides that, the attacks may further open scope for many other subsequent attacks. Existing security mechanisms concentrate on two-party mutual authentication. However, an IoT-enabled healthcare application involves multiple parties such as a patient, e-healthcare test-equipment, doctors, and cloud servers that requires multi-party authentication for secure communication. Moreover, the design and implementation of a lightweight security mechanism that fits into the resource constraint IoT-enabled healthcare devices are challenging. Therefore, this article proposes a lightweight, multi-party authentication and key-establishment protocol in IoT-based e-healthcare service access network to counter the attacks in resource constraint devices. The proposed multi-party protocol has used a lattice-based cryptographic construct such as Identity-Based Encryption (IBE) to acquire security, privacy, and efficiency. The study provided all-round analysis of the scheme, such as security, power consumption, and practical usage, in the following ways. The proposed scheme is tested by a formal security tool, Scyther, to testify the security properties of the protocol. In addition, security analysis for various attacks and comparison with other existing works are provided to show the robust security characteristics. Further, an experimental evaluation of the proposed scheme using IBE cryptographic construct is provided to validate the practical usage. The power consumption of the scheme is also computed and compared with existing works to evaluate its efficiency.

The integration of Internet of Things (IoT) and software defined networks is the most suitable network paradigm for development of smart world. IoT based solutions have been developed for fulfilling the gap between Cyber and physical world. There are many issues for realizing the IoT due to its large scale and heterogeneous network structure. Energy efficient node deployment also called green deployment for IoT is one of the major challenging issue. Hence most of the existing deployment methods for WSNs are not workable for IoT. This paper addresses the challenges of deployment schemes to get an energy efficient IoT networks. It presents a homogeneous grid based deployment strategy and a heterogeneous circle packing based deployment strategy. The performance of both approaches are calculated by simulating different deployment scenarios. The network lifetime and energy consumption are calculated. It is found that the circle packing based deployment approach outperforms the other grid based approach in terms of energy efficiency and well suited for the heterogeneous network.

introduction Introduction to the Special Issue on Privacy and Security in Evolving Internet of Multimedia Things Share on Editors: Suraj Sharma View Profile , Xuyun Zhang View Profile , Hesham El-Sayed View Profile , Zhiyuan Tan View Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 16Issue 3sOctober 2020 Article No.: 93pp 1–3https://doi.org/10.1145/3423955Online:17 December 2020Publication History 0citation77DownloadsMetricsTotal Citations0Total Downloads77Last 12 Months38Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access