Showing papers in "Wireless Personal Communications in 2019"

Optimal Task Assignment in Mobile Cloud Computing by Queue Based Ant-Bee Algorithm

Abstract: Mobile cloud computing (MCC) broadens the mobile devices capability by offloading tasks to the ‘cloud’. Hence, offloading numerous tasks simultaneously increases the ‘cloudlets’ load and augments the average completion duration of the offloaded tasks. To withstand this issue, we propose a hybrid Queue Ant Colony-Artificial Bee Colony Optimization (Ant-Bee) algorithm for optimal assignment of tasks in MCC environment. The proposed algorithm works on a two-way MCC model with offloading technique, that considers of both the ‘cloudlets’ and the public ‘cloud’. The ‘cloud’ and the ‘cloudlets’ are designed on the basis of queue model for the estimation of clients waiting time in the limitation of resources. The major concern of the proposed algorithm is to offload the tasks by identifying the accurate place preferably in a ‘cloud/cloudlet’. The ‘cloud/cloudlet’ is encompassed by a queue model with the end goal to minimize the drop rate by permitting the tasks to wait in the queue. It also aims for the optimal assignment of tasks to manage the ‘cloudlets’ load and to minimize the entire tasks average completion time. The performance of the proposed algorithm is analyzed with few Queue based conventional algorithms such as, “Round Robin”, “Weighted Round Robin” and “Random”. From the simulation result, it is analyzed that our proposed algorithm outperforms in the power consumption of the mobile devices, the average completion time of tasks, and drop rate. Also, to ensure the efficiency of our proposed hybrid QAnt-Bee algorithm, it is contrasted with the “HACAS” application scheduling algorithm, which fails to consider queue in the ‘cloudlets’.

IoT Technology, Applications and Challenges: A Contemporary Survey

Abstract: Internet of things (IoT) is a very unique platform which is getting very popular day by day. The very reason for this to happen is the advancement in technology and its ability to get linked to everything. This feature of getting linked has in itself provided multiple opportunities and a vast scope of development. The fact that technology in various fields has evolved through the years, is the reason why we observe a rapid change in the shape, size and capacity of various instruments, components and the products used in daily life. And this benefit of simplified technology when accompanied by a platform like IoT eases the work as well as benefits both the manufacturer and the end user. The Internet of Things gives us an opportunity to construct effective administrations, applications for manufacturing, lifesaving solutions, proper cultivation and more. This paper proposes an extensive overview of the IoT technology and its varied applications in life saving, smart cities, agricultural, industrial etc. by reviewing the recent research works and its related technologies. It also accounts the comparison of IoT with M2M, points out some disadvantages of IoT. Furthermore, a detailed exploration of the existing protocols and security issues that would enable such applications is elaborated. Potential future research directions, open areas and challenges faced in the IoT framework are also summarized.

Applicability of Wireless Sensor Networks in Precision Agriculture: A Review

Abstract: Presently, wireless sensor network (WSN) plays important role in engineering, science, agriculture and many other field like surveillance, military applications, smart cars etc. Precision agriculture (PA) is one of the field in which WSN is widely adopted. The aim of the adoption of WSNs in PA is to measure the different environmental parameters such as humidity, temperature, soil moisture, PH value of soil etc., for enhancing the quantity and quality of crops. Further, the WSNs are also helped to reduce the consumptions of the natural resources used in farming. Hence, the aim of this review is to identify the various WSNs technologies adopted for precision agriculture and impact of these technologies to achieve smart agriculture. This review also focuses on the different environmental parameters like irrigation, monitoring, soil properties, temperature for achieving precision agriculture. Further, a detailed study is also carried out on different crops which are covered using WSNs technologies. This review also highlights on the different communication technologies and sensors available for PA. To analyze the impact of the WSNs in agriculture field, several research questions are designed and through this review, we are tried to find the solutions of these research questions.

Energy-Efficient Routing Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization Algorithm

Abstract: One of the most effective approaches to increase the lifetime of wireless sensor networks (WSNs), is the use of a mobile sink to collect data from sensor. In WSNs, mobile sinks implicitly help achieving uniform energy-consumption and provide load-balancing. In this approach, some certain points in the sensors field should be visited by the mobile sink. The optimal selection of these points which are also called rendezvous points is a NP-hard problem. Since hierarchical algorithms rely only on their local information to select these points, thus the probability of selecting an optimal node as rendezvous point will be very low. To address this problem, in this paper, a new method called particle swarm optimization based selection (PSOBS) is proposed to select the optimal rendezvous points. By applying PSO, the proposed method is capable of finding optimal or near-optimal rendezvous points to efficient management of network resources. In the proposed method, a weight value is also calculated for each sensor node based on the number of data packets that it receives from other sensor nodes. The proposed method was compared with weighted rendezvous planning based selection (WRPBS) algorithm based on some performance metrics such as throughput, energy consumption, number of rendezvous points and hop count. The simulation results show the superiority of PSOBS as compared with WRPBS, but it increases the packet loss rate in comparison with WRPBS.

Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture

Abstract: Precision agriculture is a suitable solution to these challenges such as shortage of food, deterioration of soil properties and water scarcity. The developments of modern information technologies and wireless communication technologies are the foundations for the realization of precision agriculture. This paper attempts to find suitable, feasible and practical wireless communication technologies for precision agriculture by analyzing the agricultural application scenarios and experimental tests. Three kinds of Wireless Sensor Networks (WSN) architecture, which is based on narrowband internet of things (NB-IoT), Long Range (LoRa) and ZigBee wireless communication technologies respectively, are presented for precision agriculture applications. The feasibility of three WSN architectures is verified by corresponding tests. By measuring the normal communication time, the power consumption of three wireless communication technologies is compared. Field tests and comprehensive analysis show that ZigBee is a better choice for monitoring facility agriculture, while LoRa and NB-IoT were identified as two suitable wireless communication technologies for field agriculture scenarios.

An Energy Aware Trust Based Secure Routing Algorithm for Effective Communication in Wireless Sensor Networks

Abstract: Security is an important phenomena for energy conservation in wireless sensor networks (WSN). Moreover, the management of trust in the WSN is a challenging task since trust is used when collaboration is critical to achieve reliable communication. In a military application using WSN, it is often necessary to communicate secret information such as military operation urgently. However, the existing routing algorithms do not consider security in the routing process. Moreover, since security is an important aspect in WSN, it is necessary to consider the security aspects in routing algorithms. Different approaches for providing security are trust management, intrusion detection, firewalls and key management are considered in the literature. Among them, trust management can provide enhanced security when it is compared with other security methods. Therefore, a new secure routing algorithm called energy aware trust based secure routing algorithm is proposed in this paper where the trust score evaluation is used to detect the malicious users effectively in WSN and spatio-temporal constraints are used with decision tree algorithm for selecting the best route. From the experiments conducted, it is proved that the proposed trust based routing algorithm achieves significant performance improvement over the existing schemes in terms of security, energy efficiency and packet delivery ratio.

Trust-Based Intrusion Detection and Clustering Approach for Wireless Body Area Networks

Abstract: For most of the tele-health applications body area networks (BANs) have become a favouring and significant technology. This application domain is exclusive so assuring security and obtaining the trustworthy details of the patients’ physiological signs is difficult. To rectify this issue, an attack-resilient malicious node detection scheme (BAN-Trust) is brought-in in the current system, which can identify the malignant attacks on BANs. In this BAN-Trust scheme, malignant nodes is identified according to the nature acquired through the nodes by their own and approvals shared by various nodes. Nevertheless, BAN-Trust conceives the common behaviour among the nodes and it doesn’t conceive the energy of the nodes and gather the information for measuring the trust. So, here, trust-based intrusion detection and clustering is proposed in order to identify the malignant nodes and broadcast the energy-effective data. In our work, trust-based intrusion detection model is brought-in for identifying the malignant nodes. Different varieties of trusts were conceives, namely energy, data and communication trust, which can be developed among two sensor nodes. Once after identifying the malignant nodes, the rest of the nodes in the network were gathered in order to create the cluster. Every cluster has one cluster head (CH) that is chosen by utilizing the multi objective firefly algorithm. The target function of this system is to reduce the delay, increase the broadcast energy and throughput. The multiple body sensor nodes were in-charge for gathering different varieties of data that were sent to the CH. The CH then forwards the gathered data to the sink and sends the details to the system via gateway. By utilizing a hybrid encryption algorithm, the system’s data is encrypted and forwarded to the hospital server. Decrypting is done on the server side to disclose the exact data. The proposed methodology is executed by utilizing an NS-2 simulator. The experimental output provides that the proposed system accomplishes good performance when distinguished with the current system in terms of precision, recall, throughput, packet delivery ratio and end to end delay.

An Optimal Information Hiding Approach Based on Pixel Value Differencing and Modulus Function

Abstract: This paper proposes an image steganography approach based on pixel value differencing and modulus function (PVDMF) to improve the peak signal-to-noise ratio (PSNR) and hiding capacity (HC). The proposed approach has two variants, (1) PVDMF 1 and (2) PVDMF 2. Both the variants use the difference between a pair of consecutive pixels to embed the secret data based on an adaptive range table. The modulus operations with pixel readjustment have been utilized to reduce the distortion in the stego-image. The experimental results prove that the PVDMF 1 offer higher PSNR and PVDMF 2 offers larger HC as compared to the existing approaches. In addition, the fall off boundary problem which exists in most of the pixel value differencing approaches has been avoided. Furthermore, it has been experimentally verified that the proposed approach is resistant against RS attack.

The Engagement of Hybrid Dispersion Compensation Schemes Performance Signature for Ultra Wide Bandwidth and Ultra Long Haul Optical Transmission Systems

Abstract: This paper presents the performance signature of the engagement of hybrid symmetrical hybrid compensation techniques for ultra wide bandwidth and ultra long haul optical transmission systems. These schemes that are namely optigrating, ideal dispersion compensation fiber Bragg Grating (IDCFBG), and dispersion compensation fiber (DCF). The combination of mixing these techniques together which is called hybrid symmetrical dispersion compensation techniques in that case. The employment of these mixing schemes is in symmetrical configuration with the presence of Erbium doped fiber amplifiers in order to upgrade optical fiber system capacity to reach transmission distance up to 432 km and transmission data rate up to 320 Gb/s. Maximum signal quality factor, minimum bit error rate (BER), output optical signal to noise ratio, electrical received power after APD photodetector, noise figure, and gain are the major interesting performance parameters for measuring the system operation efficiency. It is observed that the optimum case for maximum quality factor and minimum BER is achieved with 15 m EDFA amplifier length and 150 mW EDFA pump power.

Machine Learning for Wireless Communication Channel Modeling: An Overview

Abstract: Channel modeling is fundamental to design wireless communication systems. A common practice is to conduct tremendous amount of channel measurement data and then to derive appropriate channel models using statistical methods. For highly mobile communications, channel estimation on top of the channel modeling enables high bandwidth physical layer transmission in state-of-the-art mobile communications. For the coming 5G and diverse Internet of Things, many challenging application scenarios emerge and more efficient methodology for channel modeling and channel estimation is very much needed. In the mean time, machine learning has been successfully demonstrated efficient handling big data. In this paper, applying machine learning to assist channel modeling and channel estimation has been introduced with evidence of literature survey.

Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm

Abstract: Due to the compatibility of the designed classifiers with MLP Neural Networks (MLP NNs), in this article, MLP NNs have been used to identify and classify active and passive sonar targets. On the one hand, the great importance of precise and immediate classification of sonar targets, and on the other hand, being trapped in local minimums and the low convergence speed in classic MLP NNs have led the newly proposed Dragonfly Algorithm (DA) to be offered for training MLP NNs. In order to assess the performance of the designed classifier, this algorithm have been compared with BBO, GWO, ALO, ACO, GSA and MVO algorithms in terms of precision of classification, convergence speed and the ability to avoid local optimum. To have a comprehensive comparison, the three sets of active and passive data were used. Simulation results indicate that DA-based classification have better results in all three datasets compared to benchmark algorithms.

A Multi-objective Optimal Task Scheduling in Cloud Environment Using Cuckoo Particle Swarm Optimization

Abstract: In cloud computing, varied demands are placed on the constantly changing resources. The task scheduling place very vital role in cloud computing environments, this scheduling process needs to schedule the tasks to virtual machine while reducing the makespan and cost. The task scheduling problem comes under NP hard category. Efficient scheduling method makes cloud computing services better and faster. In general, optimization algorithms are used to solve the scheduling issues in cloud. So, in this paper we combined two optimization algorithms namely called as Cuckoo Search (CS) and Particle Swarm Optimization (PSO).The new proposed hybrid algorithm is called as, CS and particle swarm optimization (CPSO). Our main purpose of the proposed paper is to reduce the makespan, cost and deadline violation rate. The performance of the proposed CPSO algorithm is evaluated using cloudsim toolkit. From the simulation results our proposed works minimize the makespan, cost, deadline violation rate, when compared to PBACO, ACO, MIN–MIN, and FCFS.

Medical Image Encryption Based on Hybrid Chaotic DNA Diffusion

Abstract: We explore the use of two chaotic systems (Bernoulli shift map and Zizag map) coupled with deoxyribonucleic acid coding in an encryption scheme for medical images in this paper. The scheme consists of two main phases: Chaotic key generation and DNA diffusion. Firstly, the message digest algorithm 5 hash function is performed on the plain medical image and the hash value used in combination with the value of an input ASCII string to generate initial conditions and control parameters for two chaotic systems (Bernoulli shift map and Zigzag map). These chaotic systems are subsequently used to produce two separate key matrices. Secondly, a row-by-row diffusion operation between the plain image matrix and the two chaotic key matrices, using the DNA XOR algebraic operation is performed in an alternating pattern to produce the cipher image. The logistic map is used to select the DNA encoding and decoding rules for each row. Experimental results of statistical, differential and key analyses demonstrate that the proposed scheme is robust and provides resistance to various forms of attacks.

An Energy-Efficient Dynamic Resource Management Approach Based on Clustering and Meta-Heuristic Algorithms in Cloud Computing IaaS Platforms

Abstract: Cloud computing as an emerging technology, has revolutionized the information technology industry by elastic on-demand provisioning and De-provisioning of computing resources. Due to the huge amount of electrical energy consumption by large-scale Datacenters, it is essential to investigate various approaches in order to decrease simultaneously energy and its impacts on global economic crisis and ecological concerns. This study through virtualization technique applied a hybrid technique for resource management. This technique used k-means clustering for mapping task and dynamic consolidation method, which improved by micro-genetic algorithm. Experimental evaluation performed on CloudSim 3.0.3 and the results were analyzed with Expert-Choice software tools. We found that the proposed KMGA technique could provide a good trade-off between effectively reduce energy consumption of Datacenters and sustained quality of service. In addition, it minimized the number of virtual machine migrations and make-span, in comparison with particle swarm optimization and genetic algorithms in similar hybrid techniques.

Blockchain Network Based Topic Mining Process for Cognitive Manufacturing

Abstract: Cognitive manufacturing has brought about an innovative change to the 4th industrial revolution based technology in combination with blockchain distributed ledger, which guarantees reliability, safety, and security, and mining-based intelligence information technology. In addition, artificial intelligence, machine learning, and deep learning technologies are combined in processes for logistics, equipment, distribution, manufacturing, and quality management, so that an optimized intelligent manufacturing system is developed. This study proposes a topic mining process in blockchain-network-based cognitive manufacturing. The proposed method exploits the highly universal Fourier transform algorithm in order to analyze the context information of equipment and human body motion based on a variety of sensor input information in the cognitive manufacturing process. An accelerometer is used to analyze the movement of a worker in the manufacturing process and to measure the state energy of work, movement, rest, and others. Time is split in a certain unit and then a frequency domain is analyzed in real time. For the vulnerable security of smart devices, a side-chain-based distributed consensus blockchain network is utilized. If an event occurs, it is processed according to rules and the blocking of a transaction is saved in a distributed database. In the blockchain network, latent Dirichlet allocation (LDA) based topic encapsulation is used for the mining process. The improved blockchain distributed ledger is applied to the manufacturing process to distribute the ledger of information in a peer-to-peer blockchain network in order to jointly record and manage the information. Further, topic encapsulation, a formatted statistical inference method to analyze a semantic environment, is designed. Through data mining, the time-series-based sequential pattern continuously appearing in the manufacturing process and the correlations between items in the process are found. In the cognitive manufacturing, an equalization-based LDA method is used for associate-clustering the items with high frequency. In the blockchain network, a meaningful item in the manufacturing step is extracted as a representative topic. In a cognitive manufacturing process, through data mining, potential information is extracted and hidden rules are found. Accordingly, in the cognitive manufacturing process, the mining process makes decision-making possible, especially advanced decision-making, such as potential risk, quality prediction, trend prediction, production monitoring, fault diagnosis, and data distortion.

Moth Flame Optimization Based Energy Efficient Stable Clustered Routing Approach for Wireless Sensor Networks

Abstract: The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is an efficient data aggregation method that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, moth flame optimization (MFO) based threshold-sensitive energy-efficient clustering protocol (TECP) is proposed to extend the stability period of the network. Multi-hop communication between CHs and BS is utilized using MFO to achieve optimal link cost for load balancing of distant CHs and energy minimization. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

A Review of Security in Internet of Things

Abstract: Internet of Things (IoT) has drawn significant attention in recent years since it has made revolutionary changes in human life. The IoT enables the exchange of information in a wide variety of applications such as smart buildings, smart health, smart transport, and so on. These diverse application domains can be unified into a single entity referred as smart life. The rapid evolution of the IoT has pushed a race between cyber-criminals and security experts. As billions of connected things communicate with each other and can exchange sensitive information that may be leaked. Hence, strengthening IoT’s security and preserving users’ privacy is a major challenge. This paper aims to provide a comprehensive study of the IoT security. Several IoT security attacks are analyzed, and a taxonomy of the security requirements based on the attacks’ purposes is proposed. Moreover, recent security solutions are described and classified based on their application domains. Finally, open research directions and security challenges are discussed.

Wireless Brain Computer Interface for Smart Home and Medical System

Abstract: The number of aged and disabled people has been increasing worldwide. To look after these people is a big challenge in this era. However, scientists overcome the problems of handicapped people with the help of the latest communication technologies. The smart home and medical systems are a predominant concept in research and development, specially utilizing the brain-computer interface (BCI) technology to control the daily use appliances. BCI acquires the brain signals that transmit to a digital device for analyzing and interpreting into further command or action but this approach limits the communication range between the brain and the system and becomes bulky because of the wired interface of a brain with the system. Therefore, the main purpose of this research was to design and evaluate a system that empowered the immobilized, handicapped or elderly people to carry out their basic routine tasks wirelessly, for instance, operating home appliances and monitoring vital signs without any dependency. In addition, the subject should have a properly functioning brain and controlled with eye muscle movement. In this research work, wireless BCI (WBCI) technology that is a commercial electroencephalogram headset is used to control home and medical appliances such as a light bulb, a fan, a digital blood pressure monitor and an Infrared deep pain therapeutic belt for dependent people. An Android application is developed name “Smart Home Monitor” that monitors the data from the headset. The designed device is tested on younger (50-year-old) and older (> 50-year-old) individuals to achieve an attention level (0–100). The younger male reached attention level 74.78 within 26.20 s; quicker than younger female and older people. Overall, this research work is unique for the reason that it is suitable for all those people, whose brain and eye muscles are functional even if the rest of the body is paralyzed. This analysis evaluated WBCI device enables the system to be wireless, handy, portable and reliable. Thus, the whole system can be commercialized for immobilized or handicapped people to provide better care and facility at home. Especially, the disable people appreciated this system and want to see its implementation as soon as possible.

Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

Abstract: Energy consumption has become dominant issue for wireless internet of things (IoT) networks with battery-powered nodes. The prevailing mechanism allowing to reduce energy consumption is duty-cycling. In this technique the node sleeps most of the time and wakes up only at selected moments to extend the lifespan of nodes up to 5–10 years. Unfortunately, the scheduled duty-cycling technique is always a trade-off between energy consumption and delay in delivering data to the target node. The delay problem can be alleviated with an additional wake-up radio (WuR) channel. In the paper we present original power consumption models for various duty-cycling schemes. They are the basis for checking whether WuR approach is competitive with scheduled duty-cycling techniques. We determine the maximum energy level that an additional wake-up radio can consume to become a reasonable alternative of widely used duty-cycling techniques for typical IoT networks.

Data Aggregation in Wireless Sensor Networks Using Firefly Algorithm

Abstract: The challenging issue of data aggregation in wireless sensor networks (WSNs) is of high significance for reducing network overhead and traffic. The majority of transmitted data by sensor nodes is repetitious and doing processes on them in many cases leads to increased power consumption and reduced network lifetime. Hence, sensor nodes should use such a pattern for data transmission which minimizes duplicate data. However, in cluster based WSN, cluster heads (CHs) consume more energy due to aggregating the data from cluster member nodes and transmitting the aggregated data to the sink. Therefore, the proper selection of CHs plays vital role for prolonging the lifetime of WSNs. In WSNs, cluster head selection is an optimization problem which is NP-hard. In this paper, using firefly algorithm, we proposed a method for aggregating data in WSNs. In the proposed method, sensor nodes are divided into several areas by using clustering. In each cluster, nodes are periodically active and inactive. Criteria such as energy and distance are taken into consideration for selecting active nodes. In this way, nodes with more remaining energy and more distance will be selected as active nodes. Simulation results, conducted in MATLAB 2016a, revealed that the proposed method was able to enhance quality of service parameters more than low energy adaptive clustering hierarchy and shuffled frog algorithm methods.

Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment

Abstract: Task allocation within the cloud computing environment is a nondeterministic polynomial time class problem that is laborious to get the best solution. It is an important issue in the cloud computing setting. The usage of cloud based applications and cloud users are increasing tremendously. In order to handle the massive cloud user’s requests, effective multi-objective Hybrid Genetic Algorithm–Ant Colony Optimization (HGA–ACO) based task allocation technique is proposed in this paper. Utility based scheduler identifies the task order and suitable resources to be scheduled. The proposed HGA–ACO considers the utility based scheduler output and finds the best task allocation method based on response time, completion time and throughput. The HGA–ACO algorithm combines Genetic and Ant Colony Optimization algorithms together. Genetic algorithm (GA) initializes the effective pheromone for ant colony optimization (ACO). ACO is used to enhance the GA solutions for crossover operation of GA. The experimental results show that the proposed framework has better performance in task allocation and ensuring quality of service parameters.

Protocol Stack of Underwater Wireless Sensor Network: Classical Approaches and New Trends

Abstract: The oceans and rivers remain the least explored frontiers on earth but due to frequent occurrences of disasters or calamities, the researchers have shown keen interest towards underwater monitoring. Underwater Wireless Sensor Networks (UWSN) envisioned as an aquatic medium for variety of applications like oceanographic data collection, disaster management or prevention, assisted navigation, attack protection, and pollution monitoring. Like terrestrial Wireless Sensor Networks (WSN), UWSN consists of sensor nodes that collect the information and pass it to sink, however researchers have to face many challenges in executing the network in aquatic medium. Some of these challenges are mobile sensor nodes, large propagation delays, limited link capacity, and multiple message receptions. In this manuscript, broad survey of issues concerning underwater sensor networks is presented. We provide an overview of test beds, routing protocols, experimental projects, simulation platforms, tools and analysis that are available with research fraternity.

A Novel Image Encryption Technique Based on Generalized Advanced Encryption Standard Based on Field of Any Characteristic

Abstract: The privacy of digital contents is one of the most important issue of the digitally advanced world. The transmission of online information is increasing immensely from last one decade. As the technology evolving with the passage of time, the secrecy of digital information is one of the unavoidable problem. The secrecy of information can be achieved through different encryption algorithms. In this article, our aim is to introduce an innovative technique for an image encryption to extend the advanced encryption standard (AES) to the Galois field of any characteristic. With the new improvement, all four steps in basic algorithm with binary characteristic is modified accordingly. We have extended number of possibilities in our proposed substitution boxes which imply, we added more confusion capabilities and generalized the existing concepts. Moreover, we have applied the anticipated scheme to digital image encryption. We have utilized standard statistical to verify the robustness of our suggested technique for encrypted image.

Meta-heuristic Ant Colony Optimization Based Unequal Clustering for Wireless Sensor Network

Abstract: Sensor nodes are randomly deployed to perform specific area monitoring in geographical region and temporal space. The network connectivity maintenance is a major requirement for accurate event detection with minimum energy consumption. To minimize the energy consumption, various clustering algorithms have been evolved in research studies. But, they failed to consider the other performance parameters such as quality of service constraints and the performance level. The initialization of nodes nearer to the base station (BS) as relay nodes reduces the number of relay node participation and increases the performance. This paper proposes the novel ant colony meta-heuristic based unequal clustering for the novel cluster head (CH) selection. The data fusion from the CH node to the intermediate node called Rendezvous node reduces the message transmissions and hence the energy consumed by the nodes is minimum. The neighbor finding phase and the link maintenance through the Meta-Heuristic Ant Colony Optimization approach selects the optimal path between the nodes which increases the packets delivered to the destination. The population initialization requires more time at this stage. Hence, the Haversine distance is estimated among the nodes which also reduces the dimensionality of the message transmission among the nodes. The prediction of optimal path and the CH selection using Ant Colony Optimization Meta-Heuristic and unequal clustering reduces the energy consumption effectively. The comparative analysis of proposed Meta-Heuristic Ant Colony Optimization based Unequal Clustering with the existing unequal clustering approaches on the basis of various performance parameters such as Packet Delivery Ratio, number of packets sent to the BS, energy consumption, residual energy and the percentage of dead nodes shows the effectiveness of proposed work in WSN applications.

Secure and Trust-Aware Routing Scheme in Wireless Sensor Networks

Abstract: Secure and trustable routing is one of the remarkable challenges in wireless sensor networks (WSNs). In this paper, we proposed a secure, trustable and energy-efficient routing method for WSNs. The proposed scheme uses Fuzzy logic to obtain the trust values of the routes. Then, the shortest route from the source to the destination was selected by considering trust and security. The proposed method uses the multidimensional scaling-map (MDS-MAP) optimal routing approach and measures the trust model via fuzzy logic. The proposed method was compared with Trust and Centrality degree Based Access Control (TC-BAC) and Trust-Aware Routing Framework (TARF) protocols. Through simulation experiment result, we show that the proposed scheme performs better than TC-BAC and TARF methods in terms of average packet delivery rate, average end-to-end delay and consumption energy.

Environmental Monitoring System for Smart City Based on Secure Internet of Things (IoT) Architecture

Abstract: With increasing population, urbanization, energy, transportation, and agricultural developments, pollution is degrading the environment with ever-increasing pace. The degradation in the environment due to pollution can easily affect the quality of human life by increasing health issues. Therefore, in order to avoid health risks due to the polluted environment, it is essential to monitor its state. However, at present, monitoring of data on the state of the environment is not a well-researched field. Therefore, it is required to develop a system which can efficiently collect and analyze data on the environment in order to avoid any potential risks. The Internet is one of the necessary and important tools which can be used to develop a system capable of monitoring and sharing information on environmental pollution. This study proposes Internet of Things (IoT) based environment monitoring and alert system. The proposed system monitors the region specific environment for air quality, and sound pollution, while also facilitating secure data transmission over the network which solves the security issues in IoT system.

Evaluation of Network Intrusion Detection Systems for RPL Based 6LoWPAN Networks in IoT

Abstract: Over the past few years, Internet of Things security has attracted the attention of many researchers due to its challenging and constrained nature. Particularly in the development of Network Intrusion Detection Systems which act as first line of defence for the networks. Due to the lack of reliable Internet of Things based datasets, intrusion detection approaches are suffering from uniform and accurate performance advancements. Existing benchmark datasets like KDD99, NSL-KDD cup 99 are obsolete and unfit for the evaluation of Network Intrusion Detection Systems developed for RPL based 6LoWPAN networks. To address this issue, the RPL-NIDDS17 dataset has recently been generated. This dataset consists seven types of modern routing attack patterns along with normal traffic patterns. In the proposed dataset we consider twenty two attributes that comprise of flow, basic, time type of features and two additional labelling attributes. In this study, we have shown the effectiveness of RPL-NIDDS17 by statistically analysing the probability distribution of features, correlation between features. Complexity analysis of the developed dataset is done by evaluating five machine learning techniques on the dataset. Evaluation results are shown in terms of two prominent metrics accuracy and false alarm rate, and compared with the results of KDD99, UNSW-NB15, WSN-DS datasets. The experimental results are presented to show the suitability of our proposed RPL-NIDDS17 dataset for the evaluation of Network Intrusion Detection Systems in Internet of Things.

IoT Enabled RFID Authentication and Secure Object Tracking System for Smart Logistics

Abstract: Object tracking is a fundamental problem in Supply Chain Management (SCM). Recent innovations eliminate the difficulties in traditional approach such as manual counting, locating the object, and data management. Radio Frequency Identification (RFID) implementation in SCM improves the visibility of real-time object movement and provide solutions for anti-counterfeiting. RFID is a major prerequisite for the IoT, which connects physical objects to the Internet. Various research works have been carried out to perform object tracking using GPS, video cameras, and wifi technology. These methods just hope to see the actual object, but not the characteristic changes of the object due to environmental changes. After reviewing the implementation of latest technologies in object tracking system, it is expected that the security and privacy risks in large-scale IoT systems are to be eliminated and an efficient IoT services are provided to SCM. In this work, an architecture is proposed for a fine-grained IoT-enabled online object tracking system. Cloud storage used in this architecture enhances the scalability and data management. We propose a novel secure and efficient end to end authentication protocol that is based on a symmetric key cryptosystem and one-way hash function. A new scheme is also proposed to address object tracking communication flow which uses the secret key established in the authentication process. A formal security analysis method, GNY logic is used and proved that the proposed protocol achieves an end to end authentication. Tag/Reader impersonation attack and replay attack are prevented in the proposed scheme. It also preserves forward and backward secrecy. Performance analysis shows that the proposed protocol is not storage and computationally intensive.

Associative Feature Information Extraction Using Text Mining from Health Big Data

Abstract: With the development of big data computing technology, most documents in various areas, including politics, economics, society, culture, life, and public health, have been digitalized. The structure of conventional documents differs according to their authors or the organization that generated them. Therefore, policies and studies related to their efficient digitalization and use exist. Text mining is the technology used to classify, cluster, extract, search, and analyze data to find patterns or features in a set of unstructured or structured documents written in natural language. In this paper, a method for extracting associative feature information using text mining from health big data is proposed. Using health documents as raw data, health big data are created by means of the Web. The useful information contained in health documents is extracted through text mining. Health documents as raw data are collected through Web scraping and then saved in a file server. The collected raw data of health documents are sentence type, and thus morphological analysis is applied to create a corpus. The file server executes stop word removal, tagging, and the analysis of polysemous words in a preprocessing procedure to create a candidate corpus. TF-C-IDF is applied to the candidate corpus to evaluate the importance of words in a set of documents. The words classified as of high importance by TF-C-IDF are included in a set of keywords, and the transactions of each document are created. Using an Apriori mining algorithm, the association rules of keywords in the created transaction are analyzed and associative keywords are generated. TF-C-IDF weights and associative keywords are extracted from health big data as associative features. The proposed method is a base technology for creating added value in the healthcare industry in the era of the 4th industrial revolution. Its evaluation in terms of F-measure and efficiency showed its performance to be high. The method is expected to contribute to healthcare big data management and information search.

A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems

Abstract: Wireless Sensor Networks (WSN) consists of sensors used for sensing environmental conditions and many more applications in real world. Air pollution is a threat to the life of humans. To control the air pollution it is necessary to monitor the pollutant gases in periodically. Various air pollution monitoring systems using sensor network have been developed, deployed and tested in the literature. This paper presents a comparative study about the literature for air pollution monitoring systems based on the classification such as stationary air pollution monitoring systems, dynamic air pollution monitoring systems and pollution data analysis techniques. These pollution monitoring systems are compared based on the methodologies followed, microcontroller used, communication device used, pollutants analyzed using sensors, evaluation attributes, tested location and performance of the system. This paper also discusses the merits and demerits of the air pollution monitoring systems.