QUALITY-DRIVEN CROSS LAYER DESIGN FOR MULTIMEDIA SECURITY OVER RESOURCE CONSTRAINED WIRELESS SENSOR NETWORKS

The strong need for security guarantee, e.g., integrity and authenticity, as well as privacy and confidentiality in wireless multimedia services has driven the development of an emerging research area in low cost Wireless Multimedia Sensor Networks (WMSNs). Unfortunately, those conventional encryption and authentication techniques cannot be applied directly to WMSNs due to inborn challenges such as extremely limited energy, computing and bandwidth resources. This dissertation provides a quality-driven security design and resource allocation framework for WMSNs. The contribution of this dissertation bridges the inter-disciplinary research gap between high layer multimedia signal processing and low layer computer networking. It formulates the generic problem of quality-driven multimedia resource allocation in WMSNs and proposes a cross layer solution. The fundamental methodologies of multimedia selective encryption and stream authentication, and their application to digital image or video compression standards are presented. New multimedia selective encryption and stream authentication schemes are proposed at application layer, which significantly reduces encryption/authentication complexity. In addition, network resource allocation methodologies at low layers are extensively studied. An unequal error protection-based network resource allocation scheme is proposed to achieve the best effort media quality with integrity and energy efficiency guarantee. Performance evaluation results show that this cross layer framework achieves considerable energy-quality-security gain by jointly designing multimedia selective encryption/multimedia stream authentication and communication resource allocation

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3D Indoor Monitoring

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt and zoom (ptz), the sensing range of a video sensor can be further regarded as a fan-shape in 2d and pyramid-shape in 3d. Such uniqueness attributed to wireless video sensors and the challenges associated with deployment restrictions of indoor monitoring make the traditional sensor coverage, deployment and networked solutions in 2d sensing model environments for wsns ineffective and inapplicable in solving the wireless video sensor network (wvsn) issues for 3d indoor space, thus calling for novel solutions. In this dissertation, we propose optimization techniques and develop solutions that will address the coverage, deployment and network issues associated within wireless video sensor networks for a 3d indoor environment. We first model the general problem in a continuous 3d space to minimize the total number of required video sensors to monitor a given 3d indoor region. We then convert it into a discrete version problem by incorporating 3d grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. Due in part to the uniqueness of the visual sensor directional sensing range, we propose to exploit the directional feature to determine the optimal angular-coverage of each deployed visual sensor. Thus, we propose to deploy the visual sensors from divergent directional angles and further extend k-coverage to ``k-angular-coverage\u27\u27, while ensuring connectivity within the network. We then propose a series of mechanisms to handle obstacles in the 3d environment. We develop efficient greedy heuristic solutions that integrate all these aforementioned considerations one by one and can yield high quality results. Based on this, we also propose enhanced depth first search (dfs) algorithms that can not only further improve the solution quality, but also return optimal results if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced dfs solutions. Finally, this dissertation discusses some future research directions such as in-network traffic routing and scheduling issues

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Low-complexity, low-area computer architectures for cryptographic application in resource constrained environments

RCE (Resource Constrained Environment) is known for its stringent hardware design requirements. With the rise of Internet of Things (IoT), low-complexity and low-area designs are becoming prominent in the face of complex security threats. Two low-complexity, low-area cryptographic processors based on the ultimate reduced instruction set computer (URISC) are created to provide security features for wireless visual sensor networks (WVSN) by using field-programmable gate array (FPGA) based visual processors typically used in RCEs. The first processor is the Two Instruction Set Computer (TISC) running the Skipjack cipher. To improve security, a Compact Instruction Set Architecture (CISA) processor running the full AES with modified S-Box was created. The modified S-Box achieved a gate count reduction of 23% with no functional compromise compared to Boyar’s. Using the Spartan-3L XC3S1500L-4-FG320 FPGA, the implementation of the TISC occupies 71 slices and 1 block RAM. The TISC achieved a throughput of 46.38 kbps at a stable 24MHz clock. The CISA which occupies 157 slices and 1 block RAM, achieved a throughput of 119.3 kbps at a stable 24MHz clock. The CISA processor is demonstrated in two main applications, the first in a multilevel, multi cipher architecture (MMA) with two modes of operation, (1) by selecting cipher programs (primitives) and sharing crypto-blocks, (2) by using simple authentication, key renewal schemes, and showing perceptual improvements over direct AES on images. The second application demonstrates the use of the CISA processor as part of a selective encryption architecture (SEA) in combination with the millions instructions per second set partitioning in hierarchical trees (MIPS SPIHT) visual processor. The SEA is implemented on a Celoxica RC203 Vertex XC2V3000 FPGA occupying 6251 slices and a visual sensor is used to capture real world images. Four images frames were captured from a camera sensor, compressed, selectively encrypted, and sent over to a PC environment for decryption. The final design emulates a working visual sensor, from on node processing and encryption to back-end data processing on a server computer

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al

Low-complexity, low-area computer architectures for cryptographic application in resource constrained environments

RCE (Resource Constrained Environment) is known for its stringent hardware design requirements. With the rise of Internet of Things (IoT), low-complexity and low-area designs are becoming prominent in the face of complex security threats. Two low-complexity, low-area cryptographic processors based on the ultimate reduced instruction set computer (URISC) are created to provide security features for wireless visual sensor networks (WVSN) by using field-programmable gate array (FPGA) based visual processors typically used in RCEs. The first processor is the Two Instruction Set Computer (TISC) running the Skipjack cipher. To improve security, a Compact Instruction Set Architecture (CISA) processor running the full AES with modified S-Box was created. The modified S-Box achieved a gate count reduction of 23% with no functional compromise compared to Boyar’s. Using the Spartan-3L XC3S1500L-4-FG320 FPGA, the implementation of the TISC occupies 71 slices and 1 block RAM. The TISC achieved a throughput of 46.38 kbps at a stable 24MHz clock. The CISA which occupies 157 slices and 1 block RAM, achieved a throughput of 119.3 kbps at a stable 24MHz clock. The CISA processor is demonstrated in two main applications, the first in a multilevel, multi cipher architecture (MMA) with two modes of operation, (1) by selecting cipher programs (primitives) and sharing crypto-blocks, (2) by using simple authentication, key renewal schemes, and showing perceptual improvements over direct AES on images. The second application demonstrates the use of the CISA processor as part of a selective encryption architecture (SEA) in combination with the millions instructions per second set partitioning in hierarchical trees (MIPS SPIHT) visual processor. The SEA is implemented on a Celoxica RC203 Vertex XC2V3000 FPGA occupying 6251 slices and a visual sensor is used to capture real world images. Four images frames were captured from a camera sensor, compressed, selectively encrypted, and sent over to a PC environment for decryption. The final design emulates a working visual sensor, from on node processing and encryption to back-end data processing on a server computer