Public Safety Applications Research Articles

Low profile antenna based on a fractal shaped metasurface for public safety applications

In this article, design and analysis of a fractal shaped metasurface (FSMS) antenna for public safety applications is presented. It comprises of two layers, upper layer and lower layer. The upper layer has the metasurface (MS) and lower layer has the fractal inspired monopole antenna. MS is made up of Sierpinski Knopp fractal shaped unit cell, which is arranged in 4 × 6 layout to achieve miniaturization. Ansys Electronic Desktop tool is utilized for analyzing the performance of the MS antenna. The projected FSMS antenna is fabricated using FR4 dielectric material and experimented with the help of an anechoic chamber and Vector Network Analyzer (VNA). Results show that the FSMS antenna exhibits a bandwidth of 200 MHz and a gain of 1.56 dBi at 4.89 GHz. The results obtained in simulation and measurement are in good agreement. Consequently, the proposed antenna with a low profile of 0.43λ0 × 0.43λ0 × 0.03λ0, where λ0 is the free space wavelength at 4.89 GHz is well fit for public safety applications.

A single cell droplet microfluidic system for quantitative determination of food-borne pathogens

Single-cell detection methods are already of great significance for many bioanalysis applications, and droplet microfluidics technology is understood as particularly a powerful tool. Salmonella infection is a major hygienic problem worldwide that causes major public health and economic damage, and preventing Salmonella outbreaks requires detection food-borne detection methods that are rapid, portable, and reliable, ideally without the need for complicated pre-treatment protocol steps. Herein, we present a single-cell-level analysis method based on droplet microfluidics that can sensitively and rapidly detect Salmonella directly from food samples. Specifically, this method achieves single-cell encapsulation of Salmonella in droplets of a growth medium with resazurin that enables fluorescence-based detection of pathogens within 5 h. The ratio of positive droplets in a Poisson Distribution is used for quantitation, and the detection limit of our system determined to be 50 CFU/mL, a value lower than conventional analytical methods for assessing Salmonella contamination. Our experimental results demonstrate the precise and highly sensitive performance of a single-cell-precision, droplet-based microfluidic chip analytical method for monitoring pathogenic bacteria in food. Beyond our example case of Salmonella detection from milk samples, our work lays the foundation for a new generation of microfluidics-based analytical technologies for both public health and food safety applications which can undoubtedly benefit from increases in the sensitivity and rapidity of food-borne pathogen detection.

Public acceptance of drones: Knowledge, attitudes, and practice

Drones are currently being used for commercial, public safety, hobby, and scientific research purposes. Short term and long term forecasts show extensive utilization of drones in various sectors. Yet, it is uncertain if general public accepts this highly potential drone congestion. This quantitative survey study explored public acceptance of drones by using the Knowledge, Attitude and Practice (KAP) model and using statistical analysis to decrease the uncertainty. The KAP model was utilized in a recent survey study to understand the public opinion of drones. This survey instrument was thoroughly redesigned to better understand general public's knowledge and attitudes of forty different drone applications, and associated risk perceptions. The practice section was redesigned as well to better understand how public uses drones personally and on company level. The results showed that drones were not well accepted at present except for public safety and scientific research applications. Commercial and hobby uses are not supported. Public sees the drones as a risky technology that directly interferes with their privacy. Moreover, public is not aware of most of the future drone applications, and many current applications. The survey was distributed to both general public and stakeholders to represent both part of the society (153 fully completed surveys). A qualitative risk analysis was conducted in this study as well. By considering the results, this study recommends that public and private institutions should collaborate to develop risk mitigation and response strategies to minimize the risks. Also, public needs to get informed on these strategies through mass media and educational institutions. This might help improving the reputation of drones from killer machines or privacy interrupters towards a technology that helps our society.

5G Mobile Broadband: Spectrum Challenges for Rural Regions

Wireless technologies offer potential advantages for extending broadband to rural regions because they do not require costly extension of optical fiber that requires trenching through difficult terrain that could include mountains, deserts, roadless expanses, and/or permafrost in the far North. Also usage is shifting from fixed to mobile, even in rural areas, as smart phones and other “smart” devices are becoming increasingly popular, and operators strive to provide additional bandwidth to cope with demand. Wireless 5G (fifth generation) is currently considered the most promising technology to address the explosive growth in demand not only for consumer services but for public safety, remote monitoring, logistics, and other applications. Mobile 5G technology promises greatly increased bandwidth for mobile devices and potentially for other broadband services. Yet 5G presents challenges that may not only make it difficult to offer in rural areas, but may hinder the availability of other more cost-effective wireless solutions. This paper examines the challenges of extending broadband to rural and remote regions, particularly as posed by 5G technology in terms of installation cost, availability of spectrum, and other factors that may influence availability and affordability of broadband in rural regions. It reviews proposals to transfer spectrum currently used for fixed terrestrial and satellite broadband to 5G, and implications for service availability, quality, and pricing. It also examines implications for existing community and rural WISPs (wireless internet service providers) and other entrepreneurs and organizations that may want to offer broadband in rural areas. These issues are addressed through analysis of policy drivers to expedite investment in 5G (e.g. to keep up with other countries implementing 5G and to take advantage of purported economic benefits of 5G) and the resulting rush to reallocate spectrum for 5G. It includes a case study of current Canadian government efforts to reallocate spectrum for 5G from fixed satellite services, still critical for provision of communication services in the far North, as an example of how 5G spectrum policies may affect rural communities and small service providers. The paper proposes criteria for reallocating rural spectrum that would not endanger current broadband delivery to rural and remote regions. It concludes with lessons from the case study and recommendations for policy makers facing the challenges of formulating policies for 5G within a context of commitments to provide universal access to broadband, including in rural unserved and underserved regions.

A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.

Environmental Audio Scene and Sound Event Recognition for Autonomous Surveillance

Monitoring of human and social activities is becoming increasingly pervasive in our living environment for public security and safety applications. The recognition of suspicious events is important in both indoor and outdoor environments, such as child-care centers, smart-homes, old-age homes, residential areas, office environments, elevators, and smart cities. Environmental audio scene and sound event recognition are the fundamental tasks involved in many audio surveillance applications. Although numerous approaches have been proposed, robust environmental audio surveillance remains a huge challenge due to various reasons, such as various types of overlapping audio sounds, background noises, and lack of universal and multi-modal datasets. The goal of this article is to review various features of representing audio scenes and sound events and provide appropriate machine learning algorithms for audio surveillance tasks. Benchmark datasets are categorized based on the real-world scenarios of audio surveillance applications. To have a quantitative understanding, some of the state-of-the-art approaches are evaluated based on two benchmark datasets for audio scenes and sound event recognition tasks. Finally, we outline the possible future directions for improving the recognition of environmental audio scenes and sound events.

Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

Segmenting aerial images is of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pretrained segmentation model to survey a new city that is not included in the training set significantly decreases accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We designed an algorithm that reduces the domain shift impact using generative adversarial networks (GANs). In the experiments, we tested the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves overall accuracy from 35% to 52% when passing from the Potsdam domain (considered as source domain) to the Vaihingen domain (considered as target domain). In addition, the method allows efficiently recovering the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.

Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications

In recent years, popularity of unmanned air vehicles enormously increased due to their autonomous moving capability and applications in various domains. This also results in some serious security threats, that needs proper investigation and timely detection of the amateur drones (ADr) to protect the security sensitive institutions. In this paper, we propose the novel machine learning (ML) framework for detection and classification of ADr sounds out of the various sounds like bird, airplanes, and thunderstorm in the noisy environment. To extract the necessary features from ADr sound, Mel frequency cepstral coefficients (MFCC), and linear predictive cepstral coefficients (LPCC) feature extraction techniques are implemented. After feature extraction, support vector machines (SVM) with various kernels are adopted to accurately classify these sounds. The experimental results verify that SVM cubic kernel with MFCC outperform LPCC method by achieving around 96.7% accuracy for ADr detection. Moreover, the results verified that the proposed ML scheme has more than 17% detection accuracy, compared with correlation-based drone sound detection scheme that ignores ML prediction.

A SINR-Based Synchronization Protocol for D2D Communications in Public Safety

Device-to-device (D2D) communication, an emerging form of wireless communication, has attracted considerable attention, but an efficient synchronization protocol has not yet been developed. This has crucial implications for public safety applications that lack sufficient network infrastructure. In the public safety applications, the cellular network may not be available or fully functional when the base stations are malfunctioned or destroyed due to disasters, such as an earthquake, a tsunami, or an attack. One of the major features of D2D is to provide a self-organized communication network for emergency use. In this paper, we develop a synchronization protocol to assist mobile devices in a target area in establishing a synchronized network for public safety applications. The signal-to-interference-plus-noise ratio-based synchronization protocol proposed accounts for the dynamic physical layer reception effect, and its algorithm can efficiently designate D2D devices to assist in forwarding timing signals, thereby enhancing the coverage of the synchronous network. More importantly, our synchronization protocol and its algorithm may assist D2D devices in dynamically changing their state to adapt to the variant wireless channel conditions and dynamic topology of the network. In the simulation results, we show that the proposed synchronization protocol enabled more than 90% of the D2D devices to successfully synchronize with the network, whereas only 75% of D2D devices successfully synchronized with the network through the legacy synchronization protocol.

Dynamic Group Authentication and Key Exchange Scheme Based on Threshold Secret Sharing for IoT Smart Metering Environments.

The Internet of Things (IoT) environment is constantly evolving. Many IoT services have emerged, improving living conditions. Smart homes were among the first developments, and smart buildings, smart factories, and smart cities are attracting increasing attention. Smart cities represent the ultimate convergence of the IoT, the Cloud, big data, and mobile technology. Smart homes, buildings, and factories create smart cities. In addition, the IoT finds applications in traffic control, public safety, and medical services, permitting group-based communication. As the scale of service grows, the number of things (devices) constituting the service also increases. However, security vulnerabilities arise in group-based communication environments. A device may require authentication when entering a gateway; to secure environments with large numbers of devices (such as those featuring IoT smart metering), the gateways bear heavy loads. Therefore, efficient authentication of group leaders and devices is essential. Here, we develop a dynamic group authentication and key exchange scheme for group-based IoT smart metering environments which enables efficient communication among secure IoT services. Our group authentication scheme increases the computational efficiency of the group leader and the participating devices, based on a threshold secret sharing technique.

A compact meta-atom loaded asymmetric coplanar strip-fed monopole antenna for multiband operation

In this paper, a compact asymmetric coplanar strip (ACS)-fed monopole antenna is presented. The proposed antenna consists of a tapered shaped radiating element and a meta-atom (complementary split ring resonator-CSRR), loaded in the ground plane which helps in obtaining multiband characteristics with proper impedance matching performance. Antenna with dimensions of 25 × 12.2 × 1.6 mm3 has been designed, fabricated and tested. The experimental result that exhibits of −10 dB impedance bandwidth by the proposed antenna at the center frequency of 2.88 GHz (1100 MHz), 5.78 GHz (1940 MHz) and 7.64 GHz (660 MHz). It covers wireless applications namely wireless local area network (WLAN) 2.4/5.2/5.8 GHz, long-term evolution (LTE) LTE 2.5 GHz, public safety applications 4.9 GHz, worldwide interoperability for microwave access (WiMAX) 5.5 GHz, wireless access for vehicular environments (WAVE) 5.9 GHz and X-band downlink frequency band 7.5 GHz. The antenna has good radiation characteristics in both E-plane and H-plane for all the operating frequencies. The proposed antenna exhibits a better performance compared to the previously reported designs to the existing antenna designs which are discussed in the literature. Moreover, the antenna possesses compact size, the total size occupied by the design is 0.20λ0 × 0.1λ0.

On the design of planar antenna using Fibonacci word fractal geometry in support of public safety

In this article, a planar antenna based on Fibonacci word fractal geometry with defected ground structure (DGS) has been investigated. A novel type of antenna radiator is proposed using Fibonacci word fractal which works for public safety and dedicated short range public safety applications. Size of proposed antenna is 50 × 44 mm and it resonates at 4.9 and 5.9 GHz having bandwidth ranging from 4.8 to 5.1 GHz and 5.8 to 6.8 GHz, respectively. Defected Ground Structure is used to improve gain for lower frequency band. Representative results of fractal patch antenna are reported to access the effectiveness of the developed approach in public safety applications. Simulation and experimental result confirms the efficiency of Whale Optimization Algorithm for antenna design. Experimentally measured results are matches well with simulated results and quite promising.

Source term estimation of hazardous material releases using hybrid genetic algorithm with composite cost functions

Source term estimation (STE) of atmospheric dispersion plays an important role in public safety, environmental protection and many other application fields. In this paper, several new composite cost functions for STE using hybrid genetic algorithm are proposed and compared using Nemenyi test based on 68 STE tasks from Prairie Grass field experiment. Results show one of the new composite cost functions, named as WSD, has outstanding performance in estimating both source location and emission rate. Then the patterns in STE results using different cost functions are analyzed based on the 68 tasks mentioned above, which provides further insights into what to expect from STE. At last, the relationship between composite cost functions and multi-objective optimization is analyzed to facilitate the understanding of composite cost functions. To summarize, composite cost functions such as WSD has the potential to achieve a better balance between sensitivity and robustness of cost functions applied in STE, providing the most accurate estimates. Statistical algorithm comparison techniques like Nemenyi test can help us better understand the characteristics and performance of specific settings in STE methods.

A Novel Security Model for Cooperative Virtual Networks in the IoT Era

The Internet of Things (IoT) has particular applications in public safety as well as other domains such as smart cities, health monitoring, smart homes and environments, smart industry, and various types of pervasive systems. The attacker can simply attack the IoT device in such applications, because it is randomly distributed, dynamic topology and not reliable due to energy and communication limitation. Moreover, the threat to confidentiality and security is increasing as the number of devices connected in IoT is increasing. As the numbers of devices connected to the Internet is expanding, the threat to confidentiality and security is increasing. The aim of this paper is design a typical network security model for cooperative virtual networks in the IoT era. This paper presents and discusses network security vulnerabilities, threats, attacks and risks in switches, firewalls and routers, in addition to a policy to mitigate those risks. The paper provides the fundamentals of secure networking system including firewall, router, AAA server and VLAN technology. It presents a novel security model to defense the network from internal and external attacks and threats in the IoT Era. A testbed is built to investigate the proposed model, and the performed assessment show an effective security performance with a good network performance.

A greedy traffic light and queue aware routing protocol for urban VANETs

Vehicular Ad-hoc Networks (VANETs) require reliable data dissemination for time-sensitive public safety applications. An efficient routing protocol plays a vital role to achieve satisfactory network performance. It is well known that routing is a challenging problem in VANETs due to the fast-changing network typology caused by high mobility at both ends of transmission. Moreover, under urban environment, there are two non-negligible factors in routing protocol design, the non-uniform vehicle distribution caused by traffic lights, and the network congestion due to high traffic demand in rush hours. In this paper, we propose a greedy traffic light and queue aware routing protocol (GTLQR) which jointly considers the street connectivity, channel quality, relative distance, and queuing delay to alleviate the packet loss caused by vehicle clustering at the intersection and balance the traffic load among vehicles. Through performance evaluation, we show that our proposed protocol outperforms both TLRC and GLSR-L in terms of packet delivery ratio and end-to-end delay.

Spatiotemporal information preservation in turbulent vapor plumes

Monitoring of airborne chemicals is essential for public safety and environmental applications, but the complex nature of stochastic airflow limits the accuracy of data acquired from chemical sensors, including the identity, concentration, and location of a chemical source. Here, we explore the limits on information retrieval in a turbulent environment using temporally modulated vapor plumes. By exploiting the physical and statistical properties of turbulent flow, we demonstrate higher classification rates (≥20 Hz) at lower chemical error rates (≤0.1%) than prior reports. Physical constraints on vapor sensing are explored, including finite spatial sampling, large path distances (≥3 m), dilute concentrations (≤1 ppb), dynamic wind velocities (V = 0–5 m/s), cross winds, and occluded airflows. Our results highlight the fact that as turbulence increases, smaller structures can be embedded in an air flow, which increases measurement variance, but also makes each plume more quickly and accurately identifiable. For a given wind velocity, there exists a modulation rate which optimizes the rate of preserved information. This raises interesting possibilities for the identification of optimally preserved patterns in a turbulent environment.

Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions

The UAV or mini-drones equipped with sensors are becoming increasingly popular for various commercial, industrial, and public-safety applications. However, drones with uncontrolled deployment pose challenges for highly security-sensitive areas such as a President's house, nuclear plants, and commercial areas because they can be used unlawfully. In this article, to cope with security-sensitive challenges, we propose point-to-point and FANET architectures to assist the efficient deployment of MDrs. To capture an ADr, an MDr must have the capability to efficiently and quickly detect, track, jam, and hunt the ADr. We discuss the capabilities of the existing detection, tracking, localization, and routing schemes, and also present the limitations of these schemes as further research challenges. Moreover, the future challenges related to co-channel interference, channel model design, and cooperative schemes are discussed. Our findings indicate that MDr deployment is necessary for the care of ADrs, and intensive research and development is required to fill the gaps in the existing technologies.

Skip-connection convolutional neural network for still image crowd counting

In recent years, crowd counting in still images has attracted many research interests due to its applications in public safety. However, it remains a challenging task for reasons of perspective and scale variations. In this paper, we propose an effective Skip-connection Convolutional Neural Network (SCNN) for crowd counting to overcome the issue of scale variations. The proposed SCNN architecture consists of several multi-scale units to extract multi-scale features. Each multi-scale unit including three convolutional layers builds connections between the input and each convolutional layer. In addition, we propose a scale-related training method to improve the accuracy and robustness of crowd counting. We evaluate our method on three crowd counting benchmarks. Experimental results verify the efficiency of the proposed method, and it achieves superior performance compared with other methods.

Integration of GPS with Digital devices and Interactive objects for public safety.

Abstract: 
 The paper demonstrated the role of GPS technology in law enforcement and public safety application areas. It presents the design, implementation, testing and verification of a simple, cheap, full proof energy source, user friendly and portable GPS-technology based safety system. The system, called “CARE system” was based on the open data sources and very cheap electronic chips available off the shelf at affordable prices. This proposed system is capable of providing a push button emergency help from different bodies assumed responsible for responding simultaneously by sending an SMS message containing the full information of the emergency location. Typical contents of this message are, the emergency geographic location, the system number, time and the link for displaying the location on an internet interactive map. The system can be fixed on the wall or a fixed stand or table near a patient or disable person. It will be portable to provide service to the moving system user. This is useful for emergencies in general and abduction for ransom and abuse emergencies in particular and demonstrates the system excellent role in law enforcement and public safety areas. The global positioning unit (GPS) integrated in the system provides the location of the user in the form of geographic coordinates, with exact UTC time. All the information delivered to the contacted bodies will be automatically updated whenever the user presses the master button or the contacted body dials the system number. This is facilitated by the GPS unit which receives the coordinates from the satellites for each and every second with time and date. This also, facilitates the moving victim tracking. The system is an excellent contribution to the quality of life improvement for humans in general and old men, women and children in particular.

SDNs in the Sky: Robust End-to-End Connectivity for Aerial Vehicular Networks

Unmanned aerial vehicles allow rapidly deploying a multihop communication backbone in challenging environments with applications in public safety, search and rescue missions, crowd surveillance, and disaster area monitoring. Due to environmental obstructions in the above scenarios or intentional jamming, the communication links between peer unmanned aerial vehicless are susceptible to outages. This necessitates resiliency measures to be closely integrated into the network design. To address the needs of efficient and robust end-to-end data relaying, we propose an aerial network management protocol built on top of an SDN architecture. Unique to our design, each unmanned aerial vehicle becomes an SDN switch that performs under directives sent by a centralized controller. Using a novel 3D spatial coverage-related metric, the controller calculates diverse multiple paths among unmanned aerial vehicles so that isolated and localized failures do not interrupt the overall network performance. The controller issues directives to the unmanned aerial vehicle switches through flow entries in Openflow v1.5 protocol for immediate and effective switching to the best available path. Results reveal that the proposed multi-path routing algorithm reduces the average end-to-end outage rate by 18 percent while increasing the average endto- end delay by 12 percent when compared to the traditional multi-path routing algorithms.