International Journal of Sensor Networks (57 papers in press)
Recent Advances in Wireless Sensor Networks with Environmental Energy Harvesting
by Lei Shu, Wanjiun Liao, Jaime Lloret, Lei Wang
Low-Delay Fair-Reliability Scheduling in Multi-hop IEEE802.15.4e Time Synchronized Channel Hopping Networks
by Junhua Zhang, Yuanyi Wang, Zhenqian Wu
Abstract: IEEE802.15.4e is the latest generation of high-reliability, low-power medium access control protocols. As part of IEEE802.15.4e, Time Synchronized Channel Hopping (TSCH) provides support for multi-hop and multi-channel communications. IEEE802.15.4e only defines when a schedule is executed; it does not specify a concrete resource schedule strategy. In this study, an IEEE802.15.4e TSCH scheduling algorithm called Fair Reliability Scheduling Algorithm (FRESA) is proposed to focus on both less delay and fair reliability. Through leaf node prior and multiple nodes sharing the same resource, there is less delay to transmit messages. Balancing the assignment of different qualities of channel offset guarantees fair reliability in message transmission. We design two strategies to ensure fair reliability, one based on per Link and the other based on per Path, and then accordingly divide our algorithm into FRESA-L and FRESA-P. Simulation results demonstrate our algorithms performance. We also observe that FRESA-P can achieve higher reliability than FRESA-L.
Keywords: IEEE802.15.4e; time synchronized channel hopping; wireless sensor network; industrial Internet of Things; fair reliability.
Heuristically Accelerated Reinforcement Learning for Channel Assignment in Wireless Sensor Networks
by Mohamed Sahraoui, Azeddine Bilami, Abdelmalik Taleb-Ahmed
Abstract: In Wireless Sensor Networks (WSNs), multi-channel communication represents an attractive field due to its multiple advantages in improving performance that has become necessary in many applications in recent fields such as the Internet of Things (IoT). The main axes that determine this performance are throughput and delivery rate, while the major challenge that faces WSNs is the drain of energy. For this reason, various approaches have been proposed for the design of distributed resource allocation schemes in an energy-efficient way. Thus, to overcome the problem of both channel assignment and scheduling in the schedule-based distributed WSNs, Reinforcement Learning (RL) approach for channel assignment is used. However, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Heuristically Accelerated Reinforcement Learning approach for Channel Assignment (HARL CA) in schedule-based distributed WSNs is proposed to reduce the number of learning iterations in an energy-efficient way. The proposal considers the channel chosen by the other neighboring sender nodes as external information and uses it to accelerate the learning process and avoid collisions. Also, it takes into account the bandwidth of the used channel as an important factor in the scheduling process to increase the delivery rate. The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance.rn
Keywords: Wireless sensor networks; Multi-channel; Reinforcement learning; IoT; Energy efficiency.
Design and analysis of telemedicine authentication protocol
by Lijuan Zheng, Chunlei Song, Rui Zhang, Baoqing Lv, Yujin Liu, Meng Cui, Lili Meng
Abstract: In recent years, in order to protect the security of medical privacy data, a large number of telemedicine authentication protocols have been proposed. Some protocols use digital signature methods to achieve two-way authentication among patients, doctors and medical servers, so the execution efficiency of protocol is low. Some protocols fail to achieve patient identity anonymity, the true identity of patient can be analysed by the doctor, so it cant resist security attacks. In order to solve the above problems, a more secure telemedicine authentication protocol is proposed in this paper, which implements two-way identity authentication in the telemedicine authentication model using SHA-1 hash function, time stamp and random number operations. In addition, the introduction of temporary identity can achieve patient identity anonymity and personal medical data security. The protocol has better performance in terms of security, storage and computing overhead, it is more suitable for telemedicine authentication system.
Keywords: Telemedicine; privacy protection; authentication protocol; digital signature; identity anonymity; hash function; time stamp; random number; temporary identity; performance analysis.
Design and Analysis of Network Adaptive Coding Protocol Based on Dynamic Feedback
by Wei Zhang, Yangyang Bai, Xiarui Li, Renjie Zhou
Abstract: Traditional wireless network coding methods often fail to achieve ideal collection efficiency under single-point-to-multipoint multi-layer networks, and the Coupon Collectors Problem aggravates the collection delay. This paper divides the network hierarchy into the source node, relay node and sink node, and proves the collection delay effect in the multi-layer network through Markov chain analysis. Then we propose to use a small amount of feedback information to reduce the collection delay and the number of invalid codewords and derive a new degree distribution formula. We propose a dynamic feedback adaptive coding (DFAC) model and propose an adaptive coding protocol (DFACP) based on DFAC. It shows that the DFAC model is superior to the traditional LT Codes and Growth codes protocol in terms of collection efficiency and other indicators, and the scheme has strong adaptive robustness in dynamic node changes.
Keywords: network coding; dynamic feedback; degree distribution; Coupon Collector’s Problem; multi-layer network; Markov chain of stochastic process theory; real-time collection;.
Optimal Deployment of a Distributed IoT Acoustic Surveillance System
by Manal Omer Bin Hamza, Leena Alghamdi, Mohamed K. Watfa
Abstract: Sensing using sound waves is extensively used in many medical, industrial and surveillance applications and they are especially advantageous in conditions where radio frequency communications are inoperative. In this study, we propose a setup for a high-efficiency surveillance system where we study the deployment of distributed IoT Acoustic Sensors using a real-time scenario. Simulation results indicate that in an ideal situation, with no obstacles, the proposed sensor count remained constant with a 99% efficiency, even with an increase in the count of dangerous events. We observed a rise in the count of sensors with the increase in obstacles and the total area under study. Our study also confirms that any increment beyond the obtained optimal count of the sensors will not necessarily affect efficiency. Moreover, the Total Detection Rate verified that the optimal placement of the acoustic sensors requires fewer sensors than any randomly placed sensors, to achieve a 99% efficiency. To our knowledge, the proposed research work in this paper specific to acoustic surveillance using a non-traditional IoT cloud architecture has not been previously investigated in the literature.
Keywords: surveillance; acoustic; simulation; IoT; internet of things; sensors; sound detection; regression; obstacles; dangerous events; optimal sensors; optimal deployment.
A survey on state-of-the-art road surface monitoring techniques for intelligent transportation systems
by Rishu Chhabra, C. Rama Krishna, Seema Verma
Abstract: Intelligent Transportation Systems (ITS) aims at maximizing the convenience, safety, and efficiency of the transportation system with the help of advanced technologies while reducing the investment in transportation infrastructure. It refers to the embedment of technology in the standard conventional transportation framework. With the advancement of the road network in all countries across the globe, road surface condition information has become a significant factor in preventing road accidents. Road condition monitoring is a critical aspect of road infrastructure management and affects the safety and control of the entire transportation system. In this paper, we present a survey of the road surface monitoring techniques for ITS. The techniques presented in this paper; focus on detecting the presence of road anomalies like potholes, manholes, speed bumps, and cracks, etc. that can lead to road accidents. Due to the shortcomings of the manual inspection method, automatic and semi-automatic road surface monitoring techniques have been proposed in the literature. In this paper, we have categorized the road surface monitoring techniques into four categories based on the equipment used for data acquisition. The four categories are vision-based, optical technology-based, smartphone-based, and specially designed hardware-based techniques. The vision-based techniques are less expensive but are constrained by illumination conditions. The optical technology-based techniques are highly accurate but exhibit high cost. However, the smartphone-based techniques are ubiquitous and offer a low-cost solution for road surface monitoring. Finally, we conclude by presenting future research directions to improve the process of road surface monitoring.
Keywords: crack; ITS; manhole; pothole; road surface; speed bump.
BSAD: Bus-based services advertisement protocol for vehicular ad hoc network (VANET)
by Kifayat Ullah, Abdul Haseeb
Abstract: The deployment of emerging Vehicular Ad hoc Networks (VANETs) will unfold the possibilities for a large number of safety and non-safety applications. An attractive no-safety application is the advertisement of roadside services to the drivers and passengers. The provision of roadside services would enable the business owners to reach out to a large number of customers while, on the other side, it will also help the drivers and passengers to discover these services. Current solutions make use of private cars for advertisement dissemination. However, the main problem with these solutions is the provision of incentives. It would not be possible for the business owners to give incentives to all participating car owners. To address this problem, we proposed a novel protocol for roadside services advertisement using public buses. Our proposed solution would enable the business owners to register their services with a near-by Road Side Unit (RSU) through a broker. The RSU would then broadcast these services to the buses. These buses will store the advertisements for a predefined time and will offer them to the drivers and passengers, upon request. To implement, test, and evaluate the performance of our proposed solution, we used a multi-lane highway scenario with high traffic density by using state-of-the-art simulators. We carried out several experiments and analyzed the results to get important insights. Besides, we also compared the performance of our protocol with existing solution under various traffic conditions. The results of our experiments revealed that: (a) broadcast frequency and speed of the buses influenced the advertisement messages received by the buses; (b) by using a broadcast frequency of 5\s, the buses were able to receive all the advertised services.
Keywords: VANET; Services Advertisement; ITS.
Dairy cows localization and feeding behavior monitoring using a combination of IMU and RFID network
by Samir Aoughlis, Rafik Saddaoui, Brahim Achour, Mourad Laghrouche
Abstract: Localization systems have become recently important for the monitoring of cow's health. However, the main limitations of these systems lie in the lack of information about cows activities such as feeding and drinking in the monitored zone. To overcome these limits, we have developed a non-invasive ear-attached sensor based on a combination of a low-cost radio frequency identification system with an inertial measurement unit. With this procedure, the cows are localized and their feeding behaviors are classified. The localization is realized by means of an accelerometer and a gyroscope permitting the trajectory estimation thanks to the dead reckoning and the sensor fusion method. However, problems related to instability emerge which are solved with the particle filter algorithm. In order to monitor the standing and the feeding behaviors, the decision tree technique is adopted to filter them. The obtained results proved that the algorithm has achieved high classification and localization rates.
Keywords: wireless sensor networks; dairy cow; radio frequency identification; FRID; inertial measurement unit; IMU; particle filter; dead reckoning; decision tree; feeding behavior.
Optimization of Wireless Sensor Networks Using Supervision Information
by Seyed Hossein Khasteh, Hamidreza Rokhsati
Abstract: The Wireless Sensor Network (WSN) is a rapidly growing technology that is used to meet the different requirements of a wide range of applications. However, these sensors have some limitations, including energy and bandwidth constraints and Energy saving in wireless sensor networks (WSNs) is a critical problem for diversity of applications. As a result, WSNs have inspired a resurgence in research on machine learning methodologies with the objective of overcoming the physical constraints of sensors. In many scenarios using WSNs, we have incomplete and imperfect prior knowledge about the problem; however, if this knowledge can be incorporated into the problem-solving process, our performance can be improved. Furthermore, this knowledge could be useful in WSN learning. The main goal of this paper is to demonstrate the positive effect of supervision information, i.e., information such as our prior knowledge about the problem domain, on the performance of machine learning in WSNs. The novelty of this paper is the use of supervision information to improve the performance of machine learning methods in WSNs. To achieve this goal, the routing problem in WSNs is solved with and without supervision information. First, the problem is solved using a very simple routing method, Gossiping. Next, a reinforcement learning-based machine learning technique is used to find the most energy-efficient routes from the sensor nodes to the sink, and the positive effect of supervision information on the performance of routing in WSNs is shown in terms of energy consumption. Our methods are analyzed theoretically and tested using a simulation. The results are highly promising and show that the utilization of supervision information can reduce energy consumption by nearly 60%.
Keywords: Wireless Sensor Networks; Machine Learning; Supervision Information; Energy Conservation; Routing.
Pedestrian Characterization in Urban Environments Combining WiFi and AI
by Antonio Guillen-Perez, Maria-Dolores Cano
Abstract: Knowing how many people there are in a given scenario offers new possibilities for the development of intelligent services. With this goal in mind, the use of sensors and Radio Frequency (RF) signals is becoming an interesting alternative to other classic methods such as image processing for counting people. In this paper we present a novel method for counting, characterizing, and localizing pedestrians in outdoor environments, called iPCW (intelligent Pedestrian Characterization using WiFi). iPCW is a passive, device-based sensor system that incorporates artificial intelligence techniques, more specifically, machine learning techniques. Performance evaluation using intensive computer simulations shows that iPCW achieves excellent results, with moving and static pedestrian detection accuracy above 98% and positioning accuracy above 92%.
Keywords: artificial intelligence; intelligent transportation systems; machine learning; people counting; pedestrians; sensor systems; smart cities; WiFi.
Protection of Surveillance Recordings via Blockchain-Assisted Multimedia Security
by Zhaowei Ma, Li Zhu, F. Richard Yu, Jeremy James Jeremy James
Abstract: Surveillance is being pervasively used, and its recording is extensively applied in practice. Thereby, protection of surveillance recordings, which is related to multimedia security, increasingly attracts research interests. Traditional techniques, such as watermarking, cryptography and steganography, focus on analyzing multimedia content, resulting in high complexity and long latency, which makes them not suitable for protecting real-time surveillance applications. In this paper, we propose a novel blockchain-assisted framework to protect the recordings in real-time surveillance applications. In the proposed framework, we design an algorithm that generates frame fingerprints, which involves real-time extraction of packets from surveillance multimedia streams, SHA3-512 functions, and the verification of frame consistency. We use a blockchain for preserving the frame fingerprints generated by our proposed algorithm. Different from existing works that use simulations to show the performance, we develop a real system using a service-oriented blockchain, virtualization for distributed ledger technology (vDLT), and the effectiveness of this system is demonstrated.
Keywords: Blockchain; surveillance; DPoS; RTP; multimedia; SHA3; cloud Computing.
A Lighting Control System of Art Museum Based on Image Recognition and STM32
by Cong Zhang, Zhisheng Wang, Dan Zhu, Haiwen Gao, Nianyu Zou
Abstract: Intelligent lighting control system plays a vital role in the lighting regulation of art gallery. It is necessary to design an automatic control system of light regulation. This paper proposes a layered design of lighting control system, which can be divided into three major parts. Each part communicates with each other using wireless sensors to realise intelligent lighting regulation. Before the lighting control system works, it also needs to have the initial values of the lighting and lamp parameters. We also design an experiment to simulate. The initial value of the luminaire indicates that the luminaire adopts 30
Keywords: lighting control; image processing; wireless sensor; feedback adjustment; simulation; initial value.
Recurrent Neural Network NARX for Distributed Fault Detection in Wireless Sensor Networks
by Jamila Atiga, Monia Hamdi, Ridha Ejbali, Mourad Zaied
Abstract: \r\nWireless sensor networks (WSNs) comprise a collection of sensors used to collect data, such as temperature, displacement, and acceleration. \r\nThese data allow knowing the state of a zone or a monitored system. Sensor nodes are usually deployed in harsh environments, where node failures are common. The network should be able to distinguish these defective nodes. In this work, we propose a Distributed Fault Detection (DFD) algorithm based on the nonlinear automatic regression recurrent neural network with eXogen input (NARX). The NARX network is used to predict a time series of data based on the previous outputs and the current input. Defective sensors are identified by comparisons between series of actually measured values and their predicted values. The proposed method, in the first step, divides the network into a set of cliques, forming different areas. In the next step, the damaged cliques are identified using the Gaussian distribution theorem. Finally, the NARX approach is applied only to the damaged cliques to determine the defective nodes. The comparisons of simulation results to other existing algorithms show that the proposed method reaches the best results.
Keywords: WSN; fault detection ; DFD algorithm; NARX; Gaussian distribution theorem.
A Prediction Model of Death Probability for Guiding Wireless Recharging in Sensor Networks
by Ping Zhong, Aikun Xu, Shu Lin, Xiaoyan Kui
Abstract: Wireless power transmission (WPT) technology is usually used to maintain the continuous operation of sensor nodes. However, when large amounts of data need to be processed, the node may enter an abnormal death state because it cannot be charged in time. Therefore, a prediction of node death probability is crucial to guide the charging path planning for charging vehicles. In this paper, we build an analysis model based on a Markov fluid queue model with the aim of creating harvest-store-use (HSU) and harvest-then-use (HTU) models of the node. Specifically, the proposed models involve a Markov process, a queuing model, and a successive fluid process. The result shows that the abnormal death probability calculated by the model is approximately 0.1% different from the probability of death obtained by simulation. Meanwhile, by comparing the two modes of energy usage, we find that HTU is better than HSU.
Keywords: abnormal death; MFQ; markov fluid queue; WSN; wireless rechargeable sensor network; WRSN; wireless rechargeable sensor network; WPT; wireless power transmission; WET; wireless energy transmission.
An Efficient Resource Sharing Scheme based on the Linear Feedback Shift Register
by Tianqi Zhou, Jian Shen, Huaqun Wang
Abstract: With the invention of various kinds of electrical equipment, electricity has become an indispensable necessity in human society. The concept of smart grid is proposed to overcome the problems of power waste and low power utilization rate. Resource sharing is regarded as one of the important goals in smart grid. However, how to improve communication efficiency has become the bottleneck of resource sharing in smart grid. In this paper, we focus on optimization of the symmetric balanced incomplete block design (SBIBD) to support efficient communication for resource sharing in smart grid. Note that the linear feedback shift register (LFSR) is employed in the generation of the SBIBD, which is far more efficient than the algorithm implementation. In order to improve efficiency, the characteristic polynomial of the LFSR for generating the SBIBD is defined. Moreover, the one-step state transition matrix of the LFSR is presented and the $s$-step state transition matrix is further deduced. Compared with the one-step state transition matrix, the $s$-step state transition matrix can well support multi-step parallel shift operation thereby increasing efficiency by $s$ times.
Keywords: Smart grid; SBIBD; LFSR; characteristic polynomial; state transition matrix.
LIA-EN: Enhancing the performance of multipath congestion control over lossy networks
by Songyang Zhang, Weimin Lei
Abstract: For a transmission protocol, the most important component is the congestion control module. It provides the endpoint the ability to probe available bandwidth to get channel resource fully utilized and prevents the network falling into severe congestion. But these coupled congestion control algorithms designed for multipath transmission protocol take packet loss event to infer link congestion and they achieve inferior performance if the multipath session traverses route suffering from random loss. Especially, the random packet loss event is not rare in wireless network. For possible performance improvement, we present LIA-EN in this work. The main idea is based on the fact that the congested loss only happens when the buffer is fully occupied and the round trip delay is in its peak in a normal route. By counting the packet loss rate in safe area, LIA-EN can determine whether a path contains link that induces random loss. If the route induces random loss, the congestion window of LIA-EN session will be set according to estimated bandwidth delay product instead of recklessly cutting it by half. Further, a framework is implemented and interested readers can test the performance of other congestion control algorithms for multipath transmission. Extensive experiments are conducted on ns3 to verify the effectiveness of the proposed solution.
Keywords: congestion control; multipath congestion control; coupled congestion control; bandwidth estimation; lossy connection.
An IoT Based Efficient Multi-Objective Real-Time Smart Parking System
by Hitesh Mohapatra, Amiya Kumar Rath
Abstract: Nowadays, finding a parking place is the most frustrating job for drivers in many of the major cities. The improper parking invites many negative side effects on urban roads. In this work, we propose a unique smart parking analyst (SPA) model for adequate parking. The SPA broadly focuses on two types of parking spaces such as public and private space-based smart parking. Unlike existing models, our proposed plan does not give any additional burden on drivers, like carrying a smartphone or driver has to be a techie. Here, we broadcast all the sensor-based real-time parking information through LED displays at the appropriate point for the convenience of all types of people. Additionally, we propose an efficient clear crowd (CC) method which helps to make available parking space most of the time and to generate more revenue from parking areas in case of excessive use. Though this work does not support smart phone-based parking solutions, still we have provided a few additional services through smartphone users as it is a demand of time which is not a core part of the SPA. The result of our execution shows that it achieves high efficiency of 94.23% in judging the appropriate parking slots and high accuracy of 91.76% in making free the occupied parking area. On the other hand the proposed model generates revenue of 1.18 times than the existing conventional parking model.
Keywords: Wireless Sensor Network; Smart Parking; Revenue Generation; Internet of Things.
Performances Analysis of UAV-assisted Wireless Powered Sensor Network
by Jing Yan, Liming Wang
Abstract: In a wireless sensor network (WSN), the key restrictive factor for network service performance is the energy replenishment of energy-constrained sensor nodes. Moreover, utilizing radio frequency signals for wireless energy transmission is regarded as an effective method for energy-constrained sensor nodes to charge. Furthermore, UAV (Unmanned Aerial Vehicle) has attracted increased attention to wireless sensor networks because of their high flexibility and low-cost by improving energy efficiency through trajectory planning while data collection. In this paper, we propose a novel UAV-assisted wireless rechargeable sensor network (WRSN), where the energy-constrained sensor nodes firstly harvest energy from the RF (Radio Frequency) signals of UAV in the downlink and then forward the information to the sink node with harvesting energy. The closed-form expressions of system throughput and outage probability in AF (Amplify and Forward) and DF (Decode and Forward) modes are both derived. Additionally, the theoretical derivation model is validated with simulation results. In particular, the results show that the DF protocol is superior to the AF protocol with lower probability and higher throughput. Besides, the influences of energy harvest time, the transmission power of UAV, and target signal-to-noise ratio on the throughput and outage probability are systematically analyzed.
Keywords: Wireless Sensor Network; UAVs; Relay; Outage Probability; Throughput.
Multi-Armed bandit Algorithms Over DASH for Multihomed Client
by Ali Hodroj, Marc Ibrahim, Yassine Hadjadj-Aoul
Abstract: There is an expanding request from the mobile clients of video traffic which has come to over the most recent two years the greater part of all the mobile data traffic. The challenge of providing the clients excellent video streaming is expected principally to the constrained transfer speed which makes it important to misuse the rising decent variety of mobile video spilling and get to systems. For enhancing the video quality received by \"multi-homed client\" (i.e., clients with multiple network interfaces), a network selection algorithm based on the Multi-Armed Bandit heuristic is proposed on top of the most broadly utilized standard for video streaming Dynamic Adaptive Streaming over HTTP (DASH).rnFor choosing the best network at each progression powerfully, DASH gives mobile video quality dependent on the apparent exhibition from the pre-owned system association through the Adaptive Bitrate Rules (ABR) without investigating the system conditions through the other network(s) which could give better quality. Subsequently, a few alterations for DASH ABR is required to enhance the video quality.rnTwo of the MAB algorithms (UCB and Epsilon Greedy) were embraced for improving MPEG-Dash. The investigations are performed through a proving ground execution to show that UCB surpasses Epsilon Greedy, in stable system conditions, regarding goodput received by the Dash customer. Additionally, UCB can discover the harmony between investigating new choices, and abusing the triumphant variation.
Keywords: DASH; Multi-homed; video streaming.
An Improved DV-Hop Algorithm Based on Differential Simulated Annealing Evolution
by Fei Tang, Witold Pedrycz
Abstract: To improve the localization accuracy of a DV-Hop localization algorithm in the wireless sensor networks with irregular network topologies, in this study, we propose an improved DV-Hop algorithm based on differential simulated annealing evolution (DSAE). The distance between the unknown node and anchor are main element in the phase of calculating the coordinate of the unknown node, but this is calculated by average hop distance (HD). So, the key behind improving the localization accuracy is to calculate the hop distance between the anchor nodes. In this paper, we innovatively divide the hop distance into the global single-hop average HD between beacon nodes, the corrected the average HD between anchor nodes and the local single-hop average HD between anchor nodes. The defect of computation method of HD, only considering the average HD, draws forth this originality. Combining three kinds of HDs, DSAE based on the HC threshold is used to estimate the average HD. The simulation results show that the improved DV-Hop algorithm can decrease the error of localization and significantly outperforms state of the art localization algorithm by 37.38% in terms of the localization error.
Keywords: wireless sensor networks; the global single-hop average HD; the corrects the average HD; the local single-hop average HD; differential simulated annealing evolution algorithm.
Analysis and Modeling of iBeacon Wireless Signal Propagation in Multiple Environments
by Luchuan Zeng, Zhifei He, ZheRui Tan, Renliang Geng, Menghua Liu, Linglin Xia, Deyu Lin
Abstract: IBeacon positioning accuracy is usually influenced by its signal quality in practical environments. The purpose of this study is to provide users with a reasonable iBeacon networking deployment scheme to improve the accuracy of positioning. We explore the characteristics of iBeacon signal propagation and existing propagation path loss in common environments (corridor, playground, classroom). Firstly, the changes of Received Signal Strength Indicat (RSSI) value of iBeacon wireless signal under three environments are collected, and then the signal samples are filtered numerically. Secondly, a simplized single-slope and a double-slope propagation models are developed to simulate iBeacon signal propagation. Finally, the developed models are used to carry out regression analysis on the sample data under different scenarios, and the best applicable scenario of the two models is determined according to the results. In conclusions, based on the simulation results of the propagation model, this study provides some suggestions for users to deploy ibeacons indoors or outdoors.
Keywords: iBeacon; propagation characteristics; logarithmic distance model; received signal strength indicat (RSSI); multi-environment.
An Inertial and Global Positioning System based Algorithm for Ownship Navigation
by Ihsan Ullah, Bizzat Hussain, Shafaq Nisar, Uzair Khan, Mohsin Ali, Sajjad Manzoor
Abstract: In this paper a fusion algorithm for Global Positioning Systems (GPS) and Inertial Navigation System (INS), using Inertial Measurement Unit (IMU), is proposed for ground vehicle trajectory estimation during a GPS outage. As a standalone system an INS has its limitations and an error growth as a result of bias and drift inherent to this sensor restricts its application. A GPS can be used for vehicle navigation flawlessly, however the navigation performance may suffer due to intentional or unintentional GPS signal outage. The proposed GPS-INS fusion algorithm is able to handle any GPS blackout, restrict the INS error development and estimate a precise ownship position while utilizing information from two route frameworks i.e. the GPS and the INS. In order to validate the proposed GPS-INS based Matched State Cascaded Fusion (MSCF) algorithm, it's root mean squared error (RMSE) of the developed algorithm is analyzed. It is proven using experiments and simulations that a decrease in critical measure of error has been accomplished with the proposed algorithm.
Keywords: Sensor Fusion; Global Positioning Systems (GPS); Inertial Navigation System (INS) and Inertial Measurement Unit (IMU).
The Flaws of Internet of Things (IoT) Intrusion Detection and Prevention Schemes
by Saher Ghayyad, Shengzhi Du, Anish Kurien
Abstract: For years, the Internet has been a major tool in our lives but is always a target for hackers who intend to take advantage of individuals, corporates, governments, banks and other institutions. As the Internet evolved to include the Internet of Things (IoT), each physical system with an IP address on this wide network can communicate without human intervention. These cyber-physical systems can vary from a smart grid to a smartphone and are attractive to hackers leading to a high risk of compromise of these systems on core infrastructure and network communication security. This paper addresses a Denial-of-Service attack (DoS), one of the major attacks on IoT Networks. The flaws are discovered in existing IoT intrusion detection and prevention schemes combating DoS attacks on Wireless Sensor Networks (WSN). Live DoS attacks on IoT Sensors are demonstrated using Kali Linux Platform. Moreover, the flaws in existing solutions are revealed using a demo Cisco Meraki MR33 and a more effective intrusion detection scheme is proposed by an active in-depth-defence strategy against DoS attacks.
Keywords: Internet of Things (IoT); Hackers; Denial-of-Service (DoS) Attack; Wireless Sensor Networks; Intrusion Detection and Prevention Schemes; Cisco Meraki MR33; Air Marshal.
Performance optimization of multichannel MAC in large-scale wireless sensor network
by Chuchu Dong
Abstract: Efficient multichannel medium access control (MAC) protocols can significantly prolong the lifetime of sensor networks by reducing the energy consumption of packet transmission among neighbor nodes. In this paper, an energy-efficient Prediction-based Multichannel MAC protocol (PM-MAC) is proposed for large-scale wireless sensor networks. PM-MAC minimizes sensor node's idle listening time without introducing any multihop time synchronization and channel assignment overhead by introducing a prediction mechanism of the target node's wakeup channel and wakeup time. PM-MAC introduces a dynamic channel allocation to achieve precise and quick resynchronization when a prediction error in the wakeup time occurs. In addition, in PM-MAC, the load can be well balanced on all channels. Finally, its prediction-based retransmission mechanism can achieve more energy efficiency than traditional strategies such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in resolving severe wireless collisions caused by concurrent traffic in large-scale sensor network. The PM-MAC not only reduces multihop packet latency and energy consumption, but also increases network capacity and throughput. Experimental results confirm the outstanding performance of PM-MAC in all testbeds.
Keywords: wireless sensor network; MAC protocol; channel assignment; energy efficiency.
Hybrid Visible Light Communication Power Optimization in Indoor Environment
by Choong Florence, Tan Ching Han, Yandan Lin
Abstract: Traditional way of handling more traffic is by having higher cell density, higher spectral efficiency and carrier aggregation. Many network optimizations have been put in place to overcome this high growth in data traffic demand. However, the network electricity consumption also increases tremendously. Hybrid visible light communication (HVLC) rides on solid-state light emitting diode lighting infrastructure with higher energy efficiency. HVLC has the advantages of high data rates, higher bandwidth, un-licence requirement, lower cost and more energy efficient. This paper focuses on recent breakthrough and advancement in HVLC especially for indoor applications. Two indoor lighting topologies were studied in detail in order to determine their power optimization performances. The power optimization through linear programming methods were used to simulate and compare the performance of square and ring topology design. The square topology showed better performance as compared to ring topology. Also, the number of users versus received power and channel assignments performance is also better for square topology. The findings demonstrated the feasibility of HVLC for indoor application.
Keywords: HVLC; radio frequency; visible light communication; square topology; ring topology; bandwidth; power optimization; energy efficiency; lighting; LED.
An IoT Smart Clothing System for the Visually Impaired using NFC Technology
by Reham Alabduljabbar
Abstract: The selection of appropriate clothing to match ones daily life is an important ritual for individuals across the globe. But this can be a Herculean task, especially for the visually impaired. Together with the rise of the Internet of Things (IoT) paradigm and enabling technologies, solutions to improve the quality of life for visually impaired people have arisen. This paper proposes an IoT-based smart clothing system (EZwear) to enable visually impaired individuals to select and find appropriate clothing within their closets. The main underlying technical components in the solution are the use of an NFC (Near Field Communication) and a smartphone and its applications. By combining these technologies, we can transform a smartphone into an NFC reader that can provide sound information to the visually impaired user. The system enhances the ability of the visually impaired person to independently and comfortably manage their closets. The results show that the system performed reasonably and achieved the usability requirements.
Keywords: IoT; Internet of Things; visually impaired; smart clothing; mobile application; NFC; assistive technology; sensors; network.
Optimal fusion rule for distributed detection with channel errors taking into account sensors unreliability probability when protecting coastlines
by Parfenov Vladimir Ivanovich, Le Van Dong
Abstract: In this paper, algorithms that make decisions on the presence or absence of penetration on a protected object with both ideal and non-ideal communication channels are synthesized and analyzed. Local sensor decision rules based on an energy detector and its performance characteristics are presented. We determine the dependencies of the total error probability on the energy parameter taking into account the energy emitted from the target at the level of local sensors and the channels signal-to-noise ratio. The gain of the optimal fusion rule in efficiency compared to known fusion rules such as the LRT-BER and Chair-Varshney fusion rule is shown. The detection algorithm under possible sensor failure and its detection performance is also presented. With the unknown target location on a coastline, the detection rules are given and the detection performance is analyzed
Keywords: wireless sensor networks (WSN); sensor; coastline; Bézier curve; decision fusion; likelihood ratio test; total error probability; unreliability probability; location estimation; generalized maximum likelihood method.
Grid-based Lane Identification with Roadside LiDAR Data
by Jianqing Wu
Abstract: Lane identification is important for many different applications, especially for connected-vehicle technologies. This paper presents a new method for lane identification with the roadside Light Detection and Ranging (LiDAR) serving connected-vehicles. The proposed lane identification method is a revised grid-based clustering method (RGBC). The whole procedure includes background filtering, object clustering, object classification, and RGBC. A location matrix (LM) can be generated to store the location of each lane. The performance of the proposed method was evaluated with the data collected from the real world. The testing results showed that the RGBC can locate 96.73% of vehicles to the correct lane. The RGBC was also compared to the state of the art, showing that the computational load for RGBC is lowest compared to other algorithms, with a cost of slightly reduced accuracy. The time delay for real-time data processing is less than 0.1 ms, which can provide the High-Resolution Micro Traffic Data (HRMTD) for connected-vehicles.
Keywords: Lane identification; Roadside LiDAR; Grid-based Classification.
Trustworthy Collaborative Trajectory Scheme for Continuous LBS
by Yuan Tian, Biao Song, Miada Murad, Najla Al-Nabhan
Abstract: With the high demand for using location-based services (LBSs) in our daily lives, the privacy protection of users trajectories has become a major concern. When users utilize LBSs, their location and trajectory information may expose their identities in continuous LBSs. Using the spatial and temporal correspondences on users trajectories, adversaries can easily gather their private information. Using collaboration between users instead of location service providers (LSPs) reduces the chance of revealing private information to adversaries. However, there is an assumption of a trusting relationship between peers. In this paper, we propose a trustworthy collaborative trajectory privacy (TCTP) scheme, which anonymizes users trajectories and resolves the untrustworthy relationship between users based on peer-to-region LBSs. Moreover, the TCTP scheme provides query content preservation based on a fake query concept in which we conceal the users actual query among a set of queries. The results of several experiments with different conditions confirm that our proposed scheme can protect users trajectory privacy successfully in a trustworthy and efficient manner.
Keywords: Continuous Location-based Services; Anonymity; Trajectory Privacy Protection; Fake Queries; K-anonymization.
Object detection algorithm combined with dynamic and static for air target intrusion
by Yi Xiao, Faming Shao, Fanjie Meng, Jiqing Luo
Abstract: The infrared detection of target intrusion usually faces the problems of complex background and insufficient brightness. This paper proposed a method combining dynamic detection and static detection, which is mainly composed of adaptive local contrast module (ALCM), interval frame semantic layered module (IFSL) and enhancement network module. ALCM, as a static method, uses adaptive sliding box to traverse the original image to obtain relatively simple background targets. As a dynamic method, IFSL can effectively separate moving targets from the background based on spatial semantic information under dynamic conditions. The function of the enhanced network module is to suppress the background clutter and highlight the target. Compared with the traditional detection method, the SNR of the proposed method is improved by 8.65%, and the computing speed is improved by 7.14%. This strongly proves that the method has high detection rate and detection efficiency in this case.
Keywords: infrared target; static detection; dynamic detection; feature fusion; enhanced network.
Energy-efficient Time Synchronization Based on Odd-Even Periodic Data Transmission and Acknowledgement in Wireless Sensor Networks
by Chuang Wang, Zhili Zhang
Abstract: In the working process of wireless sensor networks (WSNs), it is challenging to realize precise and energy-efficient time synchronization on resource-constrained WSNs nodes. In this paper, a fusion method of time synchronization with periodical data transmission and acknowledgement is proposed for WSNs. Moreover, different response intervals are generated according to the parity characteristic of the measurement data's serial number. The maximum likelihood estimators (MLE) of clock skew and fixed delay, and the simplified MLE of clock skew under the Gaussian random delay are derived. Meanwhile, the corresponding Cramer-Rao Lower Bound is calculated. The two proposed MLEs of clock skew show that both of them can work independently of the fixed delay. Simulation results verify the efficiency of these two clock skew estimators and fixed delay estimator, especially the performance and effectiveness of the second simple and practical MLE.
Keywords: Time Synchronization; Clock Skew Estimation; Energy Efficiency; Timed Response; Wireless Sensor Networks.
A Collaborative Charge Scheduling Scheme for Electric Vehicles (EV) Based on Charging Time and Resource Cost-awareness
by Mahfuzulhoq Chowdhury
Abstract: To reduce the greenhouse gas emissions problems of traditional fuel vehicles, Electric vehicles (EVs) are regarded as a promising transportation solution by industry peoples. Although EVs' performances are limited due to their limited battery power, large travelling distance during their journeys, charging delay for service, and resource price. To improve the EV's performance, this paper presents a charging time and resource cost- aware collaborative EV charging policy that selects not only a suitable charging station but also assigns timeslot for charging-discharging operation. In this paper, a decentralized EV charging/discharging network architecture is proposed in which the EV does not only communicate with the local cloud server by using the traditional vehicular network but also utilizes the EV-to-charging station and EV-to-EV interaction. The simulation results indicate that the proposed charging scheduling policy results in a better charging requirement satisfaction ratio, charging delay, and resource costs than the compared traditional schemes.
Keywords: Electric Vehicles (EV); Charging delay; Charge scheduling; Satisfaction ratio,
Utility; Waiting delay; Resource allocation; Revenue-to-required time ratio; Elapsed time,
IoT Networks for Monitoring and Detection of Leakage in Pipelines
by Seham Bakheder, Ghadah Aldabbagh, Samar Alkhuraiji, Nikos Dimitriou, Mai Fadel, Helen Bakhsh
Abstract: Pipelines have been an important means of oil transport in recent years, especially in Saudi Arabia. Since oil pipelines have extensive length, tracking them is a critical task. Therefore, monitoring pipeline leaks has become a significant field of intelligent sensing systems. There are many challenges when monitoring long-distance pipelines, requiring energy efficient. Wireless sensor networks (WSNs) deployed in oil pipelines provide solutions to address these challenges, but various issues must be considered, such as costs, scalability, and delay issues for monitoring systems. The recent advances in Internet of things (IoTs) and the new LPWAN capabilities have promising benefits over monitoring systems. This paper investigates WSN systems in oil pipelines with IoTs technologies. Also, it presents a LoRa as an LPWAN single-hop model for monitoring aboveground oil pipelines. The LoRa model is evaluated by using two scenarios and analyzing the scalability and capacity of the LoRa and discussing the result.
Keywords: Internet of Things IoT; WSN; LPWAN; LoRa; Long range; Low power.
Outlier node localization in sensor networks based on double layer modified unscented Kalman filter
by Zhonghua Ni, Xinhua Wang
Abstract: Traditional sensor network abnormal node localization has some problems, such as low positioning accuracy, high positioning time cost and so on. The range of motion of abnormal nodes is determined by controlling the distance of abnormal nodes; According to the fading degree of abnormal nodes, the unstructured feature extraction model is constructed, and the minimum mean square error of abnormal nodes is calculated. According to the median change of neighbor nodes, the movement situation of abnormal nodes is analyzed. With the help of the upper unscented Kalman to determine the nonlinear state space of abnormal nodes, the abnormal nodes in sensor networks are located, and then the positioning error is corrected by the lower unscented Kalman to realize the abnormal node location. The results show that the highest accuracy of the proposed method is about 96%, and the shortest positioning time is about 1.1s.
Keywords: Double layer correction; unscented Kalman; abnormal node; control distance; fading degree; average hop distance.
Research on Abnormal Node Detection in a Wireless Sensor Network Based on Random Matrix Theory
by Jibao Hu
Abstract: Because the traditional detection methods have the problems of low recall and precision and long detection time, this paper studies a method of abnormal node detection in a wireless sensor network(WSN) based on random matrix theory. This method uses particle swarm optimization to improve DV-Hop, and uses the improved DV-Hop method to locate WSN nodes. According to the spatiotemporal characteristics of WSN data, a data matrix is built, and the dimensionality of the data matrix is reduced by using a random matrix. The node attributes are judged according to the element correlation between multiple matrices to realize abnormal node detection in a WSN. The test results show that the average recall rate and recall rate of this method are 97.0% and 97.2% respectively, and the detection time is always less than 0.5s, so the practical application effect is good.
Keywords: Random matrix theory; WSN; Abnormal nodes detection; DV-Hop; Particle swarm optimization.
A Self-Organizing Organizational Paradigm for Using Multi-Agent Systems in Traffic Control Application of VANETs
by Mir Bagher Hosseini, Amin Rahmanzadeh, Eslam Nazemi
Abstract: The growth of small systems towards ultra-large-scale ones gradually leads us to leave the control of those systems to themselves. Autonomic computing and the self-adaptation concept have come to help. One of the sub-categories of autonomic computing is self-organization, which is suitable for distributed systems. By controlling a distributed system using agents and turning it into a multi-agent system, we can use self-organization mechanisms to enable the system to control itself. Vehicular Ad-Hoc Networks (VANETs), which can be considered as multi-agent systems, have many applications in the areas of safety and comfort of passenger vehicles. Due to the characteristics of VANETs, it is not easy to manage and maintain them manually. The dynamic nature of these networks makes automatic management necessary. VANETs characteristics, such as including vehicles with computing and energy resources, bring us to the idea of assuming them as multi-agent systems. Having this in mind, we can provide the required automatic management by adding self-organization mechanisms to VANETs, as they are multi-agent systems. In this paper, we present Team-Coalition Traffic Control (TCTC), a self-organizing organizational paradigm to add self-organization features to VANETs for traffic control. The presented organizational paradigm works without any need for roadside traffic control infrastructure, and it is fully distributed. The goal of this paradigm is to reduce the waiting time for vehicles to pass an intersection.
Keywords: Multi-Agent Systems; VANETs; Autonomic Computing; Self-adaptation; Self-organization; Traffic Control.
Real-Time monitoring of Social Distancing with Person Marking and Tracking System using YOLO V3 model
by Pandiyan P, Rajasekaran Thangaraj, Subramanian M, Rahul R, Nishanth M, Indupriya Palanisamy
Abstract: The global economy has affected enormously due to the spread of coronavirus (COVID-19). Even though, there is the availability of vaccines, social distancing in public places is one of the viable solutions to reduce the spreading of COVID-19 suggested by the World Health Organization (WHO) for fighting against the pandemic. This paper presents a YOLO v3 object detection model to automate monitoring of social distancing among persons through CCTV surveillance camera. Furthermore, this research work used to detect and track the person, measure the inter-person distance in the crowd under a challenging environment which includes partial visibility, lighting variations and person occlusion. Moreover, the YOLO V3 model is experimented with Darknet53 and ShuffleNet V2 backbone architecture. Compared with Darknet53 architecture, ShuffleNet V2 achieves better detection accuracy tested on Custom Video Footage Dataset (CVFD), Oxford Town Centre Dataset (OTCD) and Custom Personal Image Dataset (CPID) datasets.
Keywords: Deep Learning; COVID-19; Social Distancing; Surveillance Camera; Crowd Counting; Computer Vision.
Dynamic Bandwidth Estimation and Congestion Avoidance based on Network Traffic in Mobile Cloud
by TAMIZHSELVI S.P, Vijayalakshmi M
Abstract: Mobile cloud computing (MCC) plays a vital role in digital communication for delivering the network and media services such as live streaming, social network, search engine, GPS navigation, and email. While providing services, the smartphone faces many QoS challenges due to network traffic, bandwidth, congestion, delay, and packet loss. To solve the issues, we propose a novel framework, namely, Network traffic-aware dynamic bandwidth estimation and congestion avoidance in the mobile cloud. To handle the mobile network traffic, delay, and congestion, we propose three algorithms in the cloud, namely, Network Bandwidth Cloud Estimation (NBCE) algorithm, Cloud Estimated Queuing Delay (CEQD) algorithm, Cloud Bandwidth Congestion Avoidance (CBCA) algorithm. Firstly, NBCE utilizes the actual bandwidth based on different mobile network traffic. The second algorithm CEQD determines the delay to minimize the packet loss. Finally, CBCA reduces the congestion with the help of two parameters estimated bandwidth and queue length. The above algorithms were implemented in the public cloud Amazon Web Service (AWS) and evaluated. The experiments conducted in this work observed that NBCE utilizes the actual bandwidth of 88%, 80%, 67% for different network status such as low, medium, and high compared with existing TCP variants to provide improved network performance. CEQD minimizes the average delay to 20 ms when compared with other TCP algorithms. Finally, CBCA improves 5%, 4%, 4% goodput in different network traffic.
Keywords: Network traffic; bandwidth estimation; queueing delay; congestion avoidance;rnmobile cloud; Quality of service; packet loss rate; throughput; goodput; congestion window;.
Improved Bees Algorithm for the Deployment of Homogeneous and Heterogeneous Wireless Sensor Networks
by Hicham Deghbouch, Fatima Debbat
Abstract: Nowadays, countless applications benefit from the services provided by Wireless Sensor Networks (WSNs) in remote surveillance and data gathering. Coverage is considered an important Quality of Service (QoS) criterion for many of these applications. To ensure that the required QoS is achieved, the sensors must be placed in locations that optimise the area coverage and eliminate the coverage holes, especially when deploying sensors with heterogeneous sensing ranges. In this paper, a novel sensor deployment scheme based on the Improved Bees Algorithm (IBA) for optimising the area coverage in both homogeneous and heterogeneous WSNs is proposed. The IBA includes a neighbourhood shrinking procedure that aims to improve the local search efficiency and a site abandonment procedure for preventing the algorithm from the entrapment in a local optimum. Experiments show that the IBA delivers high-quality solutions and optimises the area coverage effectively compared with four state-of-the-art methods in homogeneous and heterogeneous WSNs.
Keywords: wireless sensor networks; bio-inspired algorithms; metaheuristics; coverage redundancy; area coverage; simulation; optimisation.
A quantum key injection scheme for mobile terminals based on commercial quantum key distribution
by Xiaohui Li, Dexin Zhu, Huan Wang, Lifeng Yang, Jianan Wu, Lijun Song
Abstract: In order to satisfy the demand of mobile secure communication for quantum keys, this study proposes a commercial quantum key distribution (QKD) based quantum key injection scheme for mobile terminals. It integrates a quantum network with a classic network, employs a quantum key injection device, and applies an encryption strategy to securely transmit the quantum key from the quantum network to the classic network for injection into a mobile terminal.Experimental results in a real quantum key distribution environment show that the fusion network runs stably, and the injection of the quantum key at the mobile terminal is realized effectively, which can meet the demands of mobile secure communication for quantum keys.
Keywords: quantum key; mobile key injecting; commercial QKD.
Motion Mode Recognition in Multi-storey Buildings Based on the Naive Bayes Method
by Litao Han, Lei Gu, Cheng Gong, Tianfa Wang, Aiguo Zhang
Abstract: When walking within a multi-storey building, pedestrians use a variety of modes of motion, such as going upstairs or downstairs, or walking on a plane. In each of these modes, the step size will be different, and this has a strong impact on the accuracy of pedestrian dead reckoning. In order to identify the patterns of movement in multi-storey buildings, we propose a new method of movement recognition based on smart phones. Firstly, the relationships between the patterns of indoor movement and the changes in the air pressure, acceleration and angular velocity data obtained from the built-in sensors of mobile phones are analysed. The naive Bayes method is then used to identify four different modes of motion: walking up and down stairs, moving on a landing and moving at a constant speed along a corridor. Our experimental results show that the recognition accuracy of our scheme reaches 95.82%.
Keywords: accelerometer; barometer; gyroscope; motion mode recognition; naive Bayes; smart phone.
Online Drone-based Data Gathering Strategies for Ground Sensor Networks
by Celia Yasmine TAZIBT, Nadjib ACHIR, Tounsia DJAMAH
Abstract: This paper proposes two path-planning schemes for data collection in WSN using a drone flying over the sensor nodes to collect their data. We assign a weight to each sensor node corresponding to its priority in the collection process. When the drone selects its destination node, it will choose the one having the highest weight. We have defined utility functions based on the sensor nodes' information disseminated in the Wireless Sensor Network (WSN) using the Optimized Link State Routing protocol (OLSR). The information required to compute the nodes' weight is added to the exchanged packets during the execution of OLSR. The first proposed strategy is Data-driven Data Gathering Strategy (DDG) which uses the amount of stored data in each sensor node buffer. A priority is given to the nodes having the most significant data amount to collect. The second strategy is called Time-driven Data Gathering Strategy (TDG) where the age of the data is considered.
Keywords: Wireless sensor networks; unmanned aerial vehicles; data gathering; path planning.
An IoT-based Scalable River Level Monitoring Platform
by Juan Acosta, Diego Mendez, Daniel Moreno, German Montanez, Luis Trujillo, Mauricio Escobar, Ignacio Gonzalez
Abstract: Due to extreme natural conditions, there is a clear necessity of cost-effective and scalable solutions to continuously monitor the environmental conditions in order to take precautionary actions. In this paper we present the design and implementation of an IoT-based solution for river level monitoring. The implemented system integrates 31 stations with communication capabilities to monitor the status of the Negro and Nare rivers. An embedded system has been designed and implemented from scratch into a single PCB, integrating ultrasonic sensors and power management. In order to consolidate and visualize the data, a cloud platform has been implemented, which is also in charge of generating alerts to local authorities and the population in general. The stations have an autonomy of 22 days in case of complete solar panel damage. The experimental results show that the river dynamics are perfectly captured in real-time, allowing the authorities to timely warn the population.
Keywords: Embedded system; early warning system; floods; river level; IoT.
Iot-based indoor access control and monitoring: a case study from the containment measures of the Covid-19 pandemic
by Sérgio Andrade, Rogério Negrão, Micael Fernandes, Fernando Costa, Marcos Seruffo
Abstract: The Covid-19 pandemic has led many serving environments to seek solutions to control people\'s access and avoid crowding in order to contain its spread, and to ensure the health and safety of users. Given the various current solutions, this paper presents a monitoring system that shows, in real time, via web, the status of people in closed environments. It uses Internet of Things (IoT) techniques for data interconnections and electronic components NodeMCU board and proximity sensors to monitor the entrance and exit of people in an enclosed environment, providing the statistics through an IoT platform (application) that can be installed in a mobile device (smartphone). This study highlights a low-budget system, shows the implementation of IoT platforms in the development of prototypes and the tests carried out in the academic service office.
Keywords: Internet of Things; Covid-19; IoT Monitoring; IoT Platform; Prototypes.
Random Binary Sequences Generation using Heartbeats for Cryptographic Keys in WBSNs
by Aditya Sinha, Judhistir Mahapatro, Bidisha Bhabani
Abstract: Heartbeats as a cryptographic key is an emerging biometric based authentication in Wireless Body Sensor Networks (WBSNs). Specifically, Random Binary Sequences (RBSs) are generated using heartbeats which serve as the key for encryption and decryption algorithms. Modern trend is to monitor the patients health in real-time (anytime and anywhere) and provide telemedicine remotely. Monitoring the patients physiological parameters is done using sensor devices attached to the patients body. The sensor devices then relay the information to the medical servers for diagnosis. These devices are very small and are resource constrained, so, implementing computationally heavy cryptographic algorithms inside these devices for secure transmission of data is a challenging task. Recent security measure is that of producing a 128 bit RBS using the heartbeats which is used as a cryptographic key to aid in secure communication. This takes around 30 seconds for computation which is quite time consuming. In this paper, a novel approach is discussed which helps in decreasing this 30 seconds latency. To generate a RBS, first cyclic block encoding is applied and then convolutional encoding on a single heartbeat to obtain 32 bit binary encoded value. Four consecutive heartbeats 32 bit encoded value is concatenated to obtain 128 bit RBS. The generated 128 bit RBS is subjected to various tests to verify the cryptographic key strength and is analyzed for optimal resource consumption.
Keywords: Inter Pulse Intervals (IPIs); RR-intervals ; Cyclic Block Encoding ; Convolutional Encoding ; Random Binary Sequences (RBSs).
Two-stage pricing strategy for personal cloud storage: Free trial and the cloud security risk
by Mengdi Yao, Donglin Chen
Abstract: A common approach to attract potential cloud users is to offer a two-stage pricing strategy to enhance the perceived value. This value is based on the willingness of diverse cloud users to pay and the market stages of cloud storage providers (CSPs). However, the risk of breaching cloud security in the free trial stage due to the sharing of personal cloud storage products (PCSPs) may decrease the number of cloud users. Consequently, the study proposes a two-stage pricing strategy to make a trade-off between the cloud security risk and the perceived value. Furthermore, it aims to uncover the conditions under different security risk coefficients. It selects the optimal pricing strategy to help CSPs determine the free trial time and optimal price. Our findings can provide helpful insight in devising an optimal pricing strategy by CSPs considering security risks, which assures the safety of PCSPs and benefits CSPs by increasing profits.
Keywords: Personal cloud storage products; Two-stage pricing strategy; Free trial; Cloud security risk.
A false deletion data tracking method based on Fisher information distance in Wireless Sensor Networks
by Wei Jing, Peng Wang, Ningchao Zhang
Abstract: To solve the shortcomings of traditional data tracking methods, such as low tracking efficiency, high error rates, high consumption, and so on, we propose a method based on Fisher information distance to track the deleted data in wireless sensor networks. The proposed method adopts a multilateral measurement technology to measure the geometric data and mutual position information among wireless sensors. It also adopts a Kalman filter to reduce noise interference during data processing. It selects the most suitable and pre-selected member component tracking cluster to realize data tracking of wireless sensor networks based on Fisher information distance. Experimental results show that the proposed method has high accuracy, noise elimination ability, and network energy consumption saving capability.
Keywords: Fisher information distance; Wireless sensors; Deleted data by mistakes; Data tracking;.
Message-Ferrying Delay-Tolerant Routing in Linear Wireless Sensor Networks
by Imad Jawhar, Sheng Zhang, Jie Wu, Nader Mohamed
Abstract: Due to linearity of a monitored structure, wireless sensor network (WSN) topology exhibits a linear form and the resulting network is named a linear sensor network (LSN). In order to communicate data from sensor nodes (SNs) to the sink, a multi-hop approach can be used. However, this would result in significant transmission energy loss, as the SNs would be involved in the transmission of their data as well as the data from other sensor nodes. In this paper, we introduce a ferry-based LSN (FLSN) model, where a ferry node such as a robot or other types of moving vehicles goes along the LSN, collects the data from the SNs and delivers it to the sink at the end of the network. Overall, this approach saves SN energy, increases network lifetime, and reduces transmission interference. Four ferry movement algorithms are presented, simulated and analyzed under various network conditions.
Keywords: Routing; delay-tolerant networks (DTNs); mobile ad hoc networks (MANETs); wireless sensor networks (WSNs); ferry; monitoring.
A Novel Skin-Inspired Model for Intelligent Object Recognition in Sensor Networks
by Aaron Rababaah
Abstract: Extensive research work has been conducted in the area of wireless sensor networks with high concentration on target detection or/and tracking. Although, there is some work dedicated to target classification, but the considered sensor modalities require inexpensive hardware and complex algorithms such as video, audio, radar, infrared, etc. Our solution to this problem is to propose a novel concept inspired by the human skin. It is well-known that skin possesses simple sensing receptors, compared to sophisticated ones such as vision, through which humans can not only detect a stimulus but can identify its type such as a needle, glass, table, ball, etc. analogues to this biological behavior, we propose a stimulus data modeling, characterization and classification in a simulated sensor network. The technical development of the proposed model is presented and validated via training a convolution neural network and was found to be effective and promising for future extensions.
Keywords: Skin-inspired model; object recognition; object classification; intrusion detection; sensor networks; deep neural networks; convolution neural networks.
A Practical Solution for Blockchain-Secured Sharing of Trustworthy Traffic Information in Vehicular Ad Hoc Networks
by Zhaowei Ma, Li Zhu, Xiantao Jiang, F. Richard Yu, M. Omair Shafiq, Jeremy James
Abstract: In vehicular ad hoc networks, data transmission and storage are unreliable due to various constraints such as limited physical resource and unsteady topology. As Blockchain has become a reliable data protection approach using consensus algorithm and distributed ledger, we propose a system in this paper, which employs Blockchain technology to secure the sharing of traffic information and holds profound significance for intelligent applications. Our system focuses on the real-time visual traffic information which is uploaded into Blockchain in frames via smart contracts. The sequence of frames is considered for integrity verification prior to the consensus and persistence in Blockchain. The system can dynamically adjust the transaction volume in terms of the frame types, and supports transcoding frames in Blockchain. Along with the fault tolerance and immutability of Blockchain, the traffic information can be solidly protected and thus traffic safety can be improved.
Keywords: VANET; Distributed Ledger Technology; Blockchain; Smart Contract; Consensus.
Simulation-based Usability Evaluation of Visually Impaired Outdoor Navigation Using Sensor Fusion
by Chathurika Silva, Prasad Wimalaratne
Abstract: Conducting evaluation experiments with assistive navigation in real-life environments with visually impaired subjects is challenging for several reasons. Hence simulation-based usability evaluation experiments are a pragmatic and cost-effective approach in such studies. This study presents sensor fusion in a virtual simulation of assistive navigation, including obstacle detection, localization, and motion planning. The obstacle detection involves a complementary sensor fusion approach to detect static and dynamic obstacles via vision and obstacle sensing. Localization is carried out using Kalman filter-based sensor fusion of measurement data acquired by inertial measurements using an accelerometer and gyroscope as well as Global Positioning System (GPS) data of the visually impaired navigator. Precise motion planning for local and global navigation is based on the fused signals through obstacle detection and localization modules. The evaluation showed that experiments effectively estimate the trajectory of a visually impaired navigator by fusing the heterogeneous sensor measurements in a virtual environment.
Keywords: blind navigation; sensor fusion; perception; localization; assistive technology; motion planning; simulation.
Virtual Resource Mapping in Wireless Sensor Networks Based on the Maximum Independent Link Set
by Danjun Deng
Abstract: To solve the problems of low mapping success rate, high mapping energy consumption, and low conversion rate of high-dimensional data existing in traditional methods, propose a virtual resource mapping method based on the maximum independent link set in this paper. We adopt a vector space model to reduce the dimensionality of the virtual wireless sensor networks(WSNs) resource data. We also adopt a support vector machine to input the dimensionality reduction results into the resource feature space for resource classification to obtain the independent link set and label in virtual links of the WSNs. According to the maximum independent link set, we design the network virtual resource mapping process to complete the resource mapping. Experimental results show that, compared with other methods, our method has a higher success rate of resource mapping, lower energy consumption, and higher conversion rate of high-dimensional data.
Keywords: Maximum independent link set; WSNs; Virtual resource mapping; Resource classification; Label the semantic.
A COMPARATIVE STUDY OF MOBILITY MODELS FOR FLYING AD-HOC NETWORKS
by Juhi Agrawal, Monit Kapoor
Abstract: Flying Ad-hoc Network (FANET) consists of cooperative Unmanned Aerial Vehicle (UAVs) that form an ad-hoc network to accomplish an assigned task. The mobility of the UAVs plays a dominant role as the performance of FANETs is directly connected to the mobility of UAVs. To this end, this paper presents an extensive review of mobility management models of FANETs. There is limited work presented in previous survey as most of the survey papers are more focused on routing protocols. The novelty of this work is that we perform taxonomy and comparative analysis of various mobility models of FANETs. The comparison study of all FANET mobility models has been performed based on various network characteristics. Later, we also discuss the strengths, and weaknesses, of each mobility model. Furthermore, open issues, challenges, and some future directions for research in the field of FANETs have been discussed which shall be useful for further research.
Keywords: UAV; FANET; Mobility models; Connectivity; Area coverage.
Leak Detection and Localization on a Gas Pipeline using the Gradient of Squared Pressure
by Ghassan Alnwaimi, Hatem BOUJEMAA, Feras Alfosail, Nebras M. Sobhani
Abstract: In this paper, we suggest a new algorithm for leak detection and localization on gas pipeline. We decompose the pipeline in different sections of length L. We put two pressure sensors at the beginning of the studied section at location x=iL and two other pressure sensors at the end located at x=(i+1) L where i=0,,N-1 is the number of the studied section of pipeline. We also use two mass flow rate sensors at the beginning and end of each section. Our algorithm uses the Gradient of squared Pressure to locate the leak. We show that our new algorithm allows to locate the leak with 1.2% error in distance. We tested our algorithm on a pipeline of length 15km and the average error was around 180m. Therefore, the location error is around 180/15000=1.2%.
Keywords: Wireless Sensors; Leak localization; Gas pipeline; Pressure sensors; Mass flow rate sensors.
A Nonlinear Outlier Detection Method in Sensor Networks Based on the Coordinate Mapping
by Wei Jing, Peng Wang, Ningchao Zhang
Abstract: This paper designs a nonlinear outliers detection method based on the coordinate mapping. Because data in different coordinate systems have specific attributes, the data coordinates in different coordinate systems can be transformed by coordinate mapping. Then the stream data features in a sensor network can be extracted accurately by principal component analysis to improve the detection accuracy of abnormal data points. Clustering of convection data features is implemented to shorten the time of subsequent detection by rapidly classifying data. Finally, the difference point factor is used to detect the nonlinear outliers in the sensor network. Experimental results show that the maximum detection accuracy of this method can reach 97%, the maximum detection time required is only 15s, and the maximum miss rate of this method is 1.32%, indicating that this method can effectively detect nonlinear anomaly points.
Keywords: Log polar coordinate mapping; Cartesian coordinate system; Principal component analysis; Sensor network; Nonlinear outlier detection.
A novel design of the multi-wire based solution for water leak detection and localization in buried pipes
by Raouia Khelif, Mohamed Kharrat, Mohamed Abid
Abstract: With the increase of the need for freshwater, concerns about leaks in water distribution systems have been increasing. These leaks may cause not only water losses but also significant damages to public property. Proposing an appropriate solution to identify them at an early stage is required to avoid extra problems. This paper presents a novel design of the multi-wire solution for water leak detection and localization in underground pipelines. The enhanced technique aims to overcome the limitations of the old design while preserving the same performance. The novel design uses a reduced number of sensing wires which facilitate the implementation phase, reduce energy consumption and lower the system cost. Several experimental trials were made in the laboratory to test the efficiency of the system. The obtained results confirm that the proposed methodology is highly sensitive to small leaks and accurate in locating them without triggering false alarms.
Keywords: water conservation; leak detection; leak localization; sensors; multi-wire cable sensor; underground pipelines; water distribution system; soil moisture; system testing; sensitivity; accuracy.