Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

In Internet of Things (IoT), the massive connectivity of devices and enormous data on the air have made information susceptible to different type of attacks. Cryptographic algorithms are used to provide confidentiality and maintain the integrity of the information. But small size, limited computational capability, limited memory, and power resources of the devices make it difficult to use the resource intensive traditional cryptographic algorithms for information security. In this scenario it becomes impertinent to develop lightweight security schemes for IoT. A thorough study on the lightweight cryptography as a solution to the security problem of resource-constrained devices in IoT has been presented in this work. This paper is a comprehensive attempt to provide an in-depth and state of the art survey of available lightweight cryptographic primitives till 2019. In this paper 21 lightweight block ciphers, 19 lightweight stream ciphers, 9 lightweight hash functions and 5 variants of elliptic curve cryptography (ECC) has been discussed i.e. in total 54 LWC primitives are compared in their respective classes. The comparison of the ciphers has been carried out in terms of chip area, energy and power, hardware and software efficiency, throughput, latency and figure of merit (FoM). Based on the findings it can be observed that AES and ECC are the most suitable for used lightweight cryptographic primitives. Several open research problems in the field of lightweight cryptography have also been identified.

Lightweight Cryptography: A Solution to Secure IoT

Water pollution is the root cause for many diseases in the world It is necessary to measure water quality using sensors for prevention of water pollution However, the related works remain the problems of communication, mobility, scalability, and accuracy In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors) SCADA system is fine-tuned with additional sensors and reduced cost The results show that the proposed system outperforms the existing ones and produces better results SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication

Real - time water quality monitoring using Internet of Things in SCADA

A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary application-oriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training- and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset.

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Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network

With the wide applications of wireless sensor networks (WSNs) in various fields, such as environment monitoring, battlefield surveillance, healthcare, and intrusion detection, trust establishment among sensor nodes becomes a vital requirement to improve security, reliability, and successful cooperation. The existing trust management approaches for large-scale WSN are failed due to their low dependability (i.e., cooperation), higher communication, and memory overheads (i.e., resource inefficient). In this paper, we propose a novel and comprehensive trust estimation approach (LTS) for large-scale WSN that employs clustering to improve cooperation, trustworthiness, and security by detecting malicious (faulty or selfish) sensor nodes with reduced resource (memory and power) consumption. The proposed scheme (LTS) operates on two levels, namely, intra-cluster and inter-cluster along with distributed approach and centralized approach, respectively, to make accurate trust decision of sensor nodes with minimum overheads. LTS consists of unique features, such as robust trust estimation function, attack resistant, and efficient trust aggregation at the cluster, head to obtain the global feedback trust value. Data trust along with communication trust plays a significant role to cope with malicious nodes. In LTS, punishment and trust severity can be tuned according to the application requirement, which makes it an innovative LTS. Moreover, dishonest recommendations (outliers) are eliminated before aggregation at the base station by observing the statistical dispersion. The theoretical and mathematical validations along with simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes as well as communication overhead.

/pdf/a-novel-and-comprehensive-trust-estimation-clustering-based-7y7vd0468f.pdf

A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks

Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm

Effective algorithm for optimizing compressive sensing in IoT and periodic monitoring applications

Wireless sensor networks (WSNs) have captivated substantial attention from both industrial and academic research since last few years. The major factor behind the research efforts in the field of WSNs is their vast range of applications, such as surveillance systems, military operations, health care, environment event monitoring, and human safety. However, sensor nodes are low potential and energy constraint devices; therefore, energy efficient routing protocol is the foremost concern. In this paper, a new Cluster-Tree routing scheme for gathering data (CTRS-DG) is proposed that composed of two layers: routing and aggregation and reconstruction. In aggregation and reconstruction layer, a dynamic and a self-organizing entropy based clustering algorithm for cluster head (CH) selection and cluster formation is proposed. Data is aggregated and compressed at CHs based on compressive sensing technique. In routing layer, a new proposed algorithm to form the routing tree as backbone of the network is proposed. The routing tree is used to forward the compressed data by CHs to the base station (BS). Finally, as a phase of aggregation and reconstruction layer, an effective CS reconstruction algorithm called Bee based signal reconstruction (BEBR) is proposed to improve the recovery process at the BS. BEBR utilizes the advantages of the greedy algorithm and Bees algorithm to find the optimal solution of reconstruction process. Simulation results reveal that the proposed scheme outperforms existing baseline algorithms in terms of stability period, network lifetime, and average normalized mean squared error for compressive sensing data reconstruction.

Ahmed Aziz

Papers

Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm

Effective algorithm for optimizing compressive sensing in IoT and periodic monitoring applications

Cluster-Tree Routing Based Entropy Scheme for Data Gathering in Wireless Sensor Networks

Heat transfer characteristics in a sudden expansion pipe equipped with swirl generators

Successors of PEGASIS protocol: A comprehensive survey