Factors that determine residents’ acceptance of smart city technologies

ABSTRACT

While some cities attempt to determine their residents’ demand for smart-city technologies, others simply move forward with smart-related strategies and projects. This study is among the first to empirically determine which factors most affect residents’ and public servants’ intention to use smart-city services. A Smart Cities Stakeholders Adoption Model (SSA), based on Unified Theory of Acceptance and Use of Technology (UTAUT2), is developed and tested on a mid-size U.S. city as a case study. A questionnaire was administered in order to determine the influence of seven factors – effort expectancy, self-efficacy, perceived privacy, perceived security, trust in technology, price value and trust in government – on behaviour intention, specifically the decision to adopt smart-city technologies. Results show that each of these factors significantly influenced citizen intention to use smart-city services. They also reveal perceived security and perceived privacy to be strong determinants of trust in technology, and price value a determinant of trust in government. In turn, both types of trust are shown to increase user intention to both adopt and use smart-city services. These findings offer city officials an approach to gauging residential intention to use smart-city services, as well as identify those factors critical to developing a successful smart-city strategy.     

A fast handover protocol for 6LoWPAN wireless mobile sensor networks

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.     

Link efficiency and quality of experience aware routing protocol to improve video streaming in urban VANETs

In a vehicular ad‐hoc network (VANET), vehicles can play an essential role in monitoring areas of a smart city by transmitting data or multimedia content of environmental circumstances like disasters or road conditions. Multimedia content communication with quality of experience (QoE) guarantees is a challenging undertaking in an environment such as that of a VANET. Indeed, a VANET is characterized by numerous varying conditions, significantly impacting its topology, quality of communication channels, and paths with respect to bandwidth, loss, and delay. This paper introduces a link efficiency and quality of experience aware routing protocol (LEQRV) to improve video streaming provisioning in urban vehicular ad‐hoc networks. LEQRV uses an enhanced greedy forwarding‐based approach to create and maintain stable high quality routes for video streaming delivery. It improves the performance of the quality of experience by increasing the achieved QoE scores and reducing the forwarding end‐to‐end delay and frame loss.     

Forest of ultra thin silicon nanowires: realization of temperature and catalyst size

One of the most important progresses in the field of nano science and technology was partially due to the high surface to volume ratio of quasi one-dimensional silicon nanowires (SiNWs) with various applications in biological and chemical sensors, optoelectronic devices, catalysis, Li ion batteries and solar cells. In this study we have prepared a uniform forest of ultrathin SiNWs using plasma enhanced chemical vapor deposition method. Uniformly distributed SiNWs were obtained based on an Au layer containing gold nano-seeds with the average diameters ranging from 10 to 40 nm at various temperatures. The physicochemical properties of SiNWs were characterized using field emission scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction (XRD), photoluminescence (PL) and high-resolution transmission electron microscopy. Microscopic assessments revealed that crystalline-amorphous core–shell SiNWs with different diameters and lengths ranging from 35 to 130 nm and ~?0.7 to 1.9 µm are formed during the vapor–liquid–solid mechanism, respectively. The XRD spectra show that the main lattice directions are Si(111), Si(220) and Si(311) which confirm crystalline structure of synthesized NWs. The PL spectrum reveal two distinct emission peaks at wavelengths of about 480 nm (blue range) and 690 nm (red range) as sharp and a broad peak, respectively.     

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.     

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.     

Microgrid management using hybrid inverter fuzzy-based control

Neural Computing and Applications - Microgrid systems are becoming a very promising solution to meet the power demand growth especially in remote areas where diesel generators (DG) are commonly...     

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.     

DLSA: Delay and Link Stability Aware Routing Protocol for Flying Ad-hoc Networks (FANETs)

Flying Ad-hoc Network (FANET) is a new class of Mobile Ad-hoc Network in which the nodes move in three-dimensional (3-D) ways in the air simultaneously. These nodes are known as Unmanned Aerial Vehicles (UAVs) that are operated live remotely or by the pre-defined mechanism which involves no human personnel. Due to the high mobility of nodes and dynamic topology, link stability is a research challenge in FANET. From this viewpoint, recent research has focused on link stability with the highest threshold value by maximizing Packet Delivery Ratio and minimizing End-to-End Delay. In this paper, a hybrid scheme named Delay and Link Stability Aware (DLSA) routing scheme has been proposed with the contrast of Distributed Priority Tree-based Routing and Link Stability Estimation-based Routing FANET’s existing routing schemes. Unlike existing schemes, the proposed scheme possesses the features of collaborative data forwarding and link stability. The simulation results have shown the improved performance of the proposed DLSA routing protocol in contrast to the selected existing ones DPTR and LEPR in terms of E2ED, PDR, Network Lifetime, and Transmission Loss. The Average E2ED in milliseconds of DLSA was measured 0.457 while DPTR was 1.492 and LEPR was 1.006. Similarly, the Average PDR in %age of DLSA measured 3.106 while DPTR was 2.303 and LEPR was 0.682. The average Network Lifetime of DLSA measured 62.141 while DPTR was 23.026 and LEPR was 27.298. At finally, the Average Transmission Loss in dBm of DLSA measured 0.975 while DPTR was 1.053 and LEPR was 1.227.

     

An analytical hierarchy process-based approach to solve the multi-objective multiple traveling salesman problem

We consider the problem of assigning a team of autonomous robots to target locations in the context of a disaster management scenario while optimizing several objectives. This problem can be cast as a multiple traveling salesman problem, where several robots must visit designated locations. This paper provides an analytical hierarchy process (AHP)-based approach to this problem, while minimizing three objectives: the total traveled distance, the maximum tour, and the deviation rate. The AHP-based approach involves three phases. In the first phase, we use the AHP process to define a specific weight for each objective. The second phase consists in allocating the available targets, wherein we define and use three approaches: market-based, robot and task mean allocation-based, and balanced-based. Finally, the third phase involves the improvement in the solutions generated in the second phase. To validate the efficiency of the AHP-based approach, we used MATLAB to conduct an extensive comparative simulation study with other algorithms reported in the literature. The performance comparison of the three approaches shows a gap between the market-based approach and the other two approaches of up to 30%. Further, the results show that the AHP-based approach provides a better balance between the objectives, as compared to other state-of-the-art approaches. In particular, we observed an improvement in the total traveled distance when using the AHP-based approach in comparison with the distance traveled when using a clustering-based approach.