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’.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Abstract: Performance of wireless sensor network are highly prone to network anomalies particularly to misdirection attacks and blackhole attacks. Therefor intrusion detection system has a key role in WSN and it’s essential in security application. However the identification of active attacks is cumbersome in many cases particularly for remote sensing applications. This paper proposes hybrid anomaly detection method for misdirection and blackhole attacks by employing K-medoid customized clustering technique. A synthetic dataset was established by defining network parameters and threshold values were obtained to detect the anomalies. Experimental work was performed on network simulator (NS-2) and R studio. The proposed algorithm successfully detect the hybrid anomalies with high accuracy. This work is suitable for hybrid anomaly detection including misdirection and blackhole attacks in wireless environment.

Abstract: According to world health statistics 285 million out of 7.6 billion population suffers visual impairment; hence 4 out of 100 people are blind. Absence of vision restricts the mobility of a person to pronounced extent and hence there is a need to build an explicit device to conquer guiding aid to the prospect. This paper proposes to build a prototype that performs real time object detection using image segmentation and deep neural network. Further the object, its position with respect to the person and accuracy of detection is prompted through speech stimulus to the blind person. The accuracy of detection is also prompted to the device holder. This work uses a combination of single-shot multibox detection framework with mobileNet architecture to build rapid real time multi object detection for a compact, portable and minimal response time device construction.

Abstract: Today, Wireless Sensor Networks are widely employed in various applications including military, environment, medical and urban applications. Thus, security establishment in such networks is of great importance. One of the dangerous attacks against these networks is Sybil attack. In this attack, malicious node propagates multiple fake identities simultaneously which affects routing protocols and many other operations like voting, reputation evaluation, and data aggregation. In this paper, first, a novel model of Sybil attack in cluster-based sensor networks is proposed. In the proposed attack model, a malicious node uses each of its Sybil identity to join each cluster in the network. Thus, the malicious node joins many clusters of the network simultaneously. In this paper, also a distributed algorithm based on Received Signal Strength Indicator and positioning using three points to defend against the novel attack model is proposed. The proposed algorithm is implemented and its efficiency in terms of true detection rate, false detection rate, and communication overhead is evaluated through a series of experiments. Experiment results show that the proposed algorithm is able to detect 99.8% of Sybil nodes with 0.008% false detection rate (in average). Additionally, the proposed algorithm is compared with other algorithms in terms of true detection rate and false detection rate which shows that the proposed algorithm performs desirably.

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.

Abstract: Mobile Adhoc Networks (MANETS) are gaining popularity because of interconnected networks. Routing is a key issue which needs to be addressed for efficient forwarding of packets from source to destination. Optimized Link State Routing (OLSR) is a proactive or table driven routing protocol in MANETS which works on the principal of link sensing. Multi Point Relay (MPR) node is mainly responsible for forwarding of topology control messages in OLSR. In this work MPR node is selected on the basis of node’s willingness to be selected as MPR. Wireless links are generally much inferior and prone to link losses. Frequent link failures result in lesser QoS parameters such as lesser throughput, higher end to end delay, higher latency and less utilization of link bandwidth. Link quality is an important metric to be taken as a research topic while deciding routing protocol. Routing protocols suggested by many authors have proffered minimum hop routing which contains lossy links in wireless medium resulting in reduction of throughput. In this work, Link Defined OLSR (OLSR-LD) is proposed which considers quality of link while making routing decision. Extensive Simulations were performed using NS-2 Simulator by varying pause time of nodes, simulation time of nodes and speed of nodes.

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.

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.

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.

Abstract: Wireless sensor networks (WSNs) consist of spatially distributed low power sensor nodes and gateways along with base station to monitor physical or environmental conditions. In cluster-based WSNs, the cluster head is treated as the gateway. The gateways perform the multiple activities, such as data gathering, aggregation, and transmission etc. The collected data is transmitted from gateways to the base station using routing information. Routing is a key challenge in WSNs design as gateways are constrained by energy, processing power, and memory. Moreover, heavily loaded gateways die in early stages and cause changes in network topology. It is necessary to conserve gateways energy for prolonging the WSNs lifetime. To address this problem, particle swarm optimization (PSO)-based routing is proposed in this paper. Also, a novel fitness function is designed by considering the number of relay nodes, the distance between the gateway to base station and relay load factor of the network. The proposed algorithm is validated under two different scenarios. The experimental results show that the proposed PSO-based routing algorithm prolonged WSNs lifetime when compared to other bio-inspired approaches.

Abstract: A new era of ubiquitous indoor location awareness is on the horizon especially for context sensing, ambient assisted living and many other smart city applications. Although indoor localization plays a pivotal role in making the environment smarter, it is still very difficult to compare state-of-the-art localization algorithms due to the scarcity of standard databases. Publicly available databases are neither fine-grained nor contain data for different conditions. Received Signal Strength Indicator (RSSI) of Wi-Fi signals vary with indoor environment (open/closed room, presence/absence of user, temperature etc.) and scanning smart hand-held devices. Thus, localization accuracy varies with various environmental conditions and also granularity of location points (cell). Consequently, in this paper, our contribution is two-fold. First, we present a comprehensive indoor localization dataset, subject to different domains-spatial, temporal, context and device. RSSI data has been collected with cell sizes as small as $$1\,{\mathrm{m}}\times 1\,{\mathrm{m}}$$ from three floors of a building of our University using an Android application built for this purpose. This multi-floor dataset is available online at https://drive.google.com/open?id=1_z1qhoRIcpineP9AHkfVGCfB2Fd_e-fD. Our experimental results show that maximum of $$71.78\%$$ classification accuracy can be achieved for state-of-the-art classifiers when training and testing samples are taken in different environmental conditions and from smartphones having different configurations. Single classifier cannot easily be modified to suit these variations without loosing its generality. So, to overcome these conditional dependencies, our second contribution is to propose a framework for indoor localization, JUIndoorLoc and design an ensemble of condition specific classifiers as part of the framework to take care of context and device heterogeneity. Consequently, this ensemble of condition specific classifiers is implemented and found to predict a location with $$91.74\%$$ accuracy (1.87 m) for our dataset.

Abstract: Internet of Things (IoT) is increasingly deployed in different domains and environments including smart homes, smart cities, healthcare, industry 4.0, and smart agriculture, by connecting a large number of physical objects to deliver a new class of applications. The rising number of these connected objects and their heterogeneity have raised new research directions and challenges regarding their communications, scalability and the large amount of data that generate. As a result, new communication technologies have been proposed to be applied in these environments and applications, mainly to consider their main inherent properties which are the information that generate and handle, and the content that disseminate. Among the adopted techniques and recently integrated into the IoT is the ICN (Information Centric Networking) paradigm. The choice of integration of ICN in the context of IoT is mainly motivated by all the advantages it represents, in particular content caching and decoupling senders and receivers. In this paper, we provide a detailed analysis of the motivations behind using ICN in IoT environments and a survey of existing research work that have already applied ICN as a communication support for IoT applications.

Abstract: Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime.

Abstract: In mobile ad hoc network (MANET), optimal path identification is the main problem for implementing the Multipath routing technique MANET desires an efficient algorithm for improving the performance of the network by improving the connectivity of network organization MANET routing protocol will consider so many parameters like extended power, the superiority of wireless associations, path failures, desertion, obstruction, and topological adjusts are generated for the discovery of optimal path for increasing the original routing algorithms Further advancement in multipath routing algorithm proposal will be based on local rerouting called particle swarm optimization-based bandwidth and link availability prediction algorithm for multipath routing and to ensure forwarding continuity with compound link failures In the route discovery phase, each node establishes a link between their neighboring nodes If there is any route failure resulting in data loss and overhead will occur Hence routing in MANET is developed by the movement of a node (mobility) In this paper, the particle swarm optimization based on available bandwidth and link quality based on mobility prediction algorithm is used to provide the multipath routing in MANET In this prediction phase, the available bandwidth, link quality, and mobility parameters are used to select the node based on their fuzzy logic The selected node will broadcast information among all the nodes and details are verified before transmission In the case of link failure, the nodes are stored into a blacklisted link Furthermore, the routes are diverted and backward to find a good link as a forwarder or intermediate node The proposed scheme is able to attain a significant progress in the packet delivery ratio, path optimality, and end-to-end delay

Abstract: Wireless sensor networks (WSNs) consist of a large number of nodes, communicating sensor readings to the base stations through other nodes. Due to their energy limitations and positioning in hostile environments, WSNs are vulnerable to various routing attacks. From a security point of view in WSN, data authenticity, confidentiality, Integrity, and availability are the important security goals. It is in common practice that a security protocol used to be created by focusing a particular attack in WSN. Most renowned attacks in WSN are Sybil attack, Denial of Service attack, wormhole attack, selective attack, HELLO Flooding attack, Sinkhole attack etc. This survey focuses on one of the most challenging routing attacks, called Sinkhole attack. A Sinkhole attack is one of the sternest routing attacks because it attracts surrounding nodes with misleading routing path information and performs data forging or selective forwarding of data passing through it. It can cause an energy drain on surrounding nodes resulting in energy holes in WSNs and it can cause inappropriate and potentially dangerous responses based on false measurements. Researchers had presented several ways to detect and identify sinkhole attacks. This survey reviews related work on Sinkhole attack detection, prevention strategies, and attack techniques and also highlights open challenges in dealing with such attacks. Among many discussed techniques, fuzzy logic-based systems are considered to be good in performance in intruder detection system (IDS).

Abstract: Optimizing the energy consumption of sensor nodes have been a big design issue in wireless sensor networks (WSNs). Energy efficient WSN usually compromise with network stability which is a crucial factor in ensuring full, lasting and reliable coverage of the network. Connected dominating set (CDS) based virtual backbone and traditional cluster based approach are two most commonly used data delivery protocols in a WSN. The paper proposes a distance based stable connected dominating set methodology using a meta-heuristic algorithm grey wolf optimization (DBSCDS-GWO) for achieving a stable, balanced and energy efficient CDS based WSN. We also propose a distance based stable clustering algorithm using GWO (DBSC-GWO) for improving the performance of cluster based WSN. DBSCDS-GWO performs better than RMCDS-GA and SAECDS-GA by 70.5% and 67.7% respectively and DBSC-GWO performs better than LEACH and DRESEP by 74.7% and 50.6% respectively in terms of both network stability and energy efficiency. Performance of the proposed algorithm is validated using Matlab simulation and Netsim Emulator.

Abstract: The smart grid is the next-generation electrical power system that combines operations technology (OT) and information technology (IT) for the efficient generation, delivery, and consumption of electrical energy. We aim to provide a brief overview of machine to machine (M2M) communication and its history, its application in the smart grid, security issues affecting M2M data on the smart grid, and some available solutions to detect and prevent cyber threats. With the emergence of 5G networks, we also provide an introduction to this evolving technology, how the smart grid will benefit from its deployment, and some security concerns.

Abstract: As the electroencephalography (EEG) biomedical signals are affected under the presence of the muscular motion artifacts. Presence of these artifacts leads to error in visual analysis of EEG signal, thus results in wrong diagnosis of human diseases. The variants of blind source separation (BSS) methods are available. This paper aims to design the efficient BSS based method for effectively eradicating the EEG motion artifacts. This is accomplished by evaluating the six different methods, which are combination of independent component analysis (ICA) and canonical correlation analysis (CCA) along with the discrete wavelet transform and stationary wavelet transform methods. Each of above combination methods are applied on the ensemble empirical mode decomposed, Intrinsic Mode Functions for EEG motion artifact suppression. This research paper tests the performance over pure EEG signal and also on the simulated EEG sinusoids to mimic the effect of motion artifacts. The performance of six BSS artifact removal algorithms are evaluated using efficiency matrices such as del signal to noise ratio, lambda (λ), spectral distortion (Pdis) and root mean square error. The execution time is also calculated to evaluate the computation efficiency of the algorithms. The results suggest that CCA algorithm outperforms over ICA in the case of the high noisy condition of EEG signal.

Abstract: Orthogonal frequency division multiplexing (OFDM) based free space optics (FSO) link is a promising technology for future wireless data transmission networks. In this paper, we report designing and performance analysis of hybrid OFDM-FSO link for the transmission of 4 independent channels each having a data rate of 20 Gb/s incorporating Mode division multiplexing of distinct Hermite Gaussian modes (HG00, HG01, HG02, and HG03) over a link distance of 10 km to 50 km under clear weather conditions. The performance of the proposed link is also evaluated under the effect of atmospheric turbulence and beam divergence.

Abstract: Data collection is one of the most important task in wireless sensor networks where a set of sensor nodes measure properties of a phenomenon of interest and send their data over a routing tree to the sink. In this paper, we propose a new simulated annealing based tree construction algorithm (SATC) to aggregate data with a collision-free schedule for node transmissions. SATC minimizes the time duration of delivering aggregated data to the sink. In the proposed algorithm, the average time latency is considered as the fitness function for the SA algorithm and it is evaluated based on Routing aware MAC scheduling methods. The efficiency of the proposed algorithm is studied through simulation and compared with existing state-of-the-art approaches in terms of average latency and average normalized latency.