Saad B. Qaisar

Compressive sensing: From theory to applications, a survey

Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist sampling theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of CS. This article gives a brief background on the origins of this idea, reviews the basic mathematical foundation of the theory and then goes on to highlight different areas of its application with a major emphasis on communications and network domain. Finally, the survey concludes by identifying new areas of research where CS could be beneficial.

V-Cloud: vehicular cyber-physical systems and cloud computing

The widespread use of smart phone devices and their ubiquity has brought up new application domains. One such interesting domain is vehicular networks. Furthermore, cloud computing is a next-generation information and communication technology that is gaining popularity due to its pay-as-you-go service model. In this paper, we propose our novel V-Cloud architecture which includes vehicular cyber-physical system (VCPS), vehicle-to-vehicle network (V2V) and vehicle-to-infrastructure network (V2I) layers. Each of these layers is explained in further details. We discuss research challenges in V-Cloud domain and introduce new services to show wide potential of such intelligent transportation system to meet safety and comfort requirements for driver.

Underwater sensor network applications: a comprehensive survey

There is no escaping fact that a huge amount of unexploited resources lies underwater which covers almost 70% of the Earth. Yet, the aquatic world has mainly been unaffected by the recent advances in the area of wireless sensor networks (WSNs) and their pervasive penetration in modern day research and industrial development. The current pace of research in the area of underwater sensor networks (UWSNs) is slow due to the difficulties arising in transferring the state-of-the-art WSNs to their underwater equivalent. Maximum underwater deployments rely on acoustics for enabling communication combined with special sensors having the capacity to take on harsh environment of the oceans. However, sensing and subsequent transmission tend to vary as per different subsea environments; for example, deep sea exploration requires altogether a different approach for communication as compared to shallow water communication. This paper particularly focuses on comprehensively gathering most recent developments in UWSN applications and their deployments. We have classified the underwater applications into five main classes, namely, monitoring, disaster, military, navigation, and sports, to cover the large spectrum of UWSN. The applications are further divided into relevant subclasses. We have also shown the challenges and opportunities faced by recent deployments of UWSN.

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey

Wireless sensor networks (WSNs) have received considerable attention for multiple types of applications. In particular, outlier detection in WSNs has been an area of vast interest. Outlier detection becomes even more important for the applications involving harsh environments, however, it has not received extensive treatment in the literature. The identification of outliers in WSNs can be used for filtration of false data, find faulty nodes and discover events of interest. This paper presents a survey of the essential characteristics for the analysis of outlier detection techniques in harsh environments. These characteristics include, input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types(local and global), type of approach(distributed/centralized), outlier identification(event or error), outlier degree, outlier score, susceptibility to dynamic topology, non-stationarity and inhomogeneity. Moreover, the prioritization of various characteristics has been discussed for outlier detection techniques in harsh environments. The paper also gives a brief overview of the classification strategies for outlier detection techniques in WSNs and discusses the feasibility of various types of techniques for WSNs deployed in harsh environments.

Quarter-Sphere SVM: Attribute and Spatio-Temporal correlations based Outlier & Event Detection in wireless sensor networks

Support-Vector Machines (SVM) have received a great interest in the machine learning community since their introduction, especially in Outlier Detection in Wireless Sensor Networks (WSN). The Quarter-Sphere formulation of One-Class SVM (QS-SVM), extends the main SVM ideas from supervised to unsupervised learning algorithms. The QS-SVM formulation is based only on Spatio-Temporal correlations between the sensor nodes (hence the name Spatio-Temporal Quarter-Sphere SVM, ST-QS-SVM). Thus, it has a non-ideal performance. This work presents a new One-Class Quarter-Sphere SVM formulation based on the novel concept of Attribute Correlations between the sensor nodes, hence the name, Spatio-Temporal-Attribute Quarter-sphere SVM (STA-QS-SVM) formulation. Online and partially online approaches to Outlier Detection in WSNs have been presented using this formulation. The results indicate a significant increase in the Outlier Detection rates and a remarkable reduction in the False Positive rates over the previous formulation (ST-QS-SVM). The results of this novel technique also suggest that the partially online approach is as efficient as the online approach, thereby conserving significant computational and communication complexity. Moreover very high Event Detection rates have been reported for STA-QS-SVM, which have not been reported by ST-QS-SVM.

Multipath multi-stream distributed reliable video delivery in Wireless Sensor Networks

Reliably transporting captured video content over Wireless Sensor Networks (WSNs) poses several challenges due to the unique energy constraints presented by individual sensor nodes. The nature and amount of video content is another challenge that further complicates WSN data reliability problem. Motivated by the aforementioned factors, in this work, we propose an architecture that promises to enhance received video quality along-with sensor network lifetime maximization using a three prong strategy: splitting source coded video into prioritized streams, use path diversity to route video packets and partially and progressively decode the data packets as they traverse the multi-hop network. Further intrigued by quality enhancement for received video, we apply our proposed framework to an actual video sequence and compare the benefits that can be achieved using multipath multi-stream distributed reliability (MMDR) framework that we propose. We show that with little bit of processing within the network, significant enhancements in quality of video can be achieved for same level of energy spent to deliver video over the end-to-end path. We propose selective budgeting of network energy resources for processing within the network. We use low density parity check (LDPC) codes for channel coding the video sensor data. We show that the proposed architecture outperforms prevalent end-to-end channel coding schemes by considerable margins. Further, based on the results achieved, we discuss the associated rate-distortion tradeoffs in design of video sensor networks.

One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

Performance Analysis of LTE Smartphones-Based Vehicle-to-Infrastrcuture Communication

With an increase in number of vehicles on road, applications and services offered by Intelligent Transportation System are growing in demand. However, there are still coverage and capacity limitation of wireless links that support such services. In this work, we address a new and novel concept that leverages 4G network for vehicle-to-infrastructure communications. Instead of considering on-board units communication interface, we make a case for using smart-phones as economical alternative given that an interface exists between vehicle navigation system and smart phone. The applications considered in this paper are broadband internet access and entertainment applications (video streaming etc.) We provide performance analysis results using LTE-simulator. Our high-level conclusion is that smart phones enriched with LTE capabilities are feasible for vehicular communications given that fourth generation network penetrates market rapidly.

Joint Admission Control, Mode Selection, and Power Allocation in D2D Communication Systems

Device-to-device (D2D) communications can help in achieving the higher data rate targets in emerging wireless networks. The use of D2D communication imposes certain challenges such as interference with the cellular and D2D users. A well-designed joint admission control, network mode selection, and power allocation technique in a cellular network with D2D capability can improve overall throughput. The proposed technique jointly maximizes the total throughput and number of admitted users in cellular networks under quality-of-service (QoS) and interference constraints. The joint admission control, mode selection, and power allocation problem (JACMSPA) falls into a class of mixed-integer nonlinear constraint optimization problems that are generally NP-hard. Due to the combinatorial nature of the problem, its optimal solution needs exhaustive search of integer variables whose complexity increases exponentially with the number of user pairs. In this paper, we invoke outer approximation approach (OAA)-based linearization technique to solve the JACMSPA. The proposed method gives guaranteed ε-optimal solution with reasonable computational complexity. Simulation results verify the effectiveness of the proposed approach method.