Mohammad Abdur Razzaque

Middleware for Internet of Things: A Survey

The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoTs characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.

Energy-efficient sensing in wireless sensor networks using compressed sensing

Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

VGDRA: A Virtual Grid-Based Dynamic Routes Adjustment Scheme for Mobile Sink-Based Wireless Sensor Networks

In wireless sensor networks, exploiting the sink mobility has been considered as a good strategy to balance the nodes energy dissipation. Despite its numerous advantages, the data dissemination to the mobile sink is a challenging task for the resource constrained sensor nodes due to the dynamic network topology caused by the sink mobility. For efficient data delivery, nodes need to reconstruct their routes toward the latest location of the mobile sink, which undermines the energy conservation goal. In this paper, we present a virtual grid-based dynamic routes adjustment (VGDRA) scheme that aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sink. We propose a set of communication rules that governs the routes reconstruction process thereby requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sink. Simulation results demonstrate reduced routes reconstruction cost and improved network lifetime of the VGDRA scheme when compared with existing work.

Compression in wireless sensor networks: A survey and comparative evaluation

Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply, memory capacity, communication bandwidth, and processor performance. Compression of sampling, sensor data, and communications can significantly improve the efficiency of utilization of three of these resources, namely, power supply, memory and bandwidth. Recently, there have been a large number of proposals describing compression algorithms for WSNs. These proposals are diverse and involve different compression approaches. It is high time that these individual efforts are put into perspective and a more holistic view taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of compression and compression frameworks in WSNs. A comparative study of the various approaches is also provided. In addition, open research issues, challenges and future research directions are highlighted.

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

Security and Privacy in Wireless Body Area Networks for Health Care Applications

Wireless Body Area Sensor Networks (WBANs) are becoming more and more popular and have shown great potential in real-time monitoring of the human body. With the promise of cost effective, unobtrusive, and unsupervised continuous monitoring, WBANs have attracted a wide range of monitoring applications such as healthcare, sport activity and rehabilitation systems. However, in using the advantage of WBANs, a number of challenging issues should be resolved. Besides open issues in WBANs such as standardization, energy efficiency and Quality of Service (QoS), security and privacy issues are one of the major concerns. Since these wearable systems control life-critical data, they must be secure. Nevertheless, addressing security in these systems faces some difficulties. WBANs inherit most of the well known security challenges from Wireless Sensor Networks (WSN). However, typical characteristics of WBANs, such as severe resource constraints and harsh environmental conditions, pose additional unique challenges for security and privacy support. In this chapter, we will survey major security and privacy issues and potential attacks in WBANs. In addition, we will explain an unsolved quality of service problem which has great potential to pose a serious security issues in WBANs, and then we discuss a potential future direction.

A comprehensive review on privacy preserving data mining

Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.

Reliability aware routing for intra-wireless body sensor networks

With the promise of cost effective, unobtrusive, and unsupervised continuous monitoring, wireless body sensor networks (WBSNs) have attracted a wide range of monitoring applications such as medical and healthcare, sport activity, and rehabilitation systems. Most WBSNs medical and healthcare applications are real-time and life-critical, which require strict guarantee of quality of service (QoS), in terms of latency, and reliability. Reliability in routing plays key role in providing the overall reliability in WBSNs. This paper presents reliability aware routing (RAR) for intra-WBSNs that aims to provide high reliability for reliability constraint data packets. It considers the high and dynamic path loss due to body postural movements and temperature rise of the implanted biomedical sensor nodes. We have used two network models in this paper: RAR without Relays (RAR) and RAR with Relays (RARR). The simulation results reveal that RARR outperforms the other state-of-the-art schemes while RAR has slightly low reliability at low data rates as compared to RARR but significantly higher than other state-of-the-art schemes.

QOS-aware error recovery in wireless body sensor networks using adaptive network coding.

Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding networks QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

VGDD: A virtual grid based data dissemination scheme for wireless sensor networks with mobile sink

Wireless sensor network (WSN) is considered as the enabling technology to bridge the gap between the physical and digital world. Some of the applications environments of WSN require a mobile sink to operate in the sensor field where delayed and/or partial data delivery might lead to inappropriate conclusions and thus require high quality of service in terms of latency and packets delivery ratio. Majority of existing mobile sink based data dissemination schemes aim to prolong network lifetime whereas few schemes improve the data delivery performance by employing multiple mobile sinks which add to the hardware and operating cost. In this paper we propose a virtual grid based data dissemination (VGDD) scheme that aims to optimize the tradeoff between network lifetime and data delivery performance while adhering to the low cost theme of WSN. Using the virtual structure, the proposed VGDD scheme follows a set of rules to disseminate sinks mobility updates in an energy efficient manner thereby maintaining nearly optimal routes. Furthermore, to cope with speeds variation of mobile sink, VGDD makes use of appropriate forwarder nodes for guaranteed data delivery. Simulation results reveal improved data delivery performance in terms of latency and data delivery ratio compared to existing work.