A Comprehensive Survey on Exiting Solution Approaches towards Security and Privacy Requirements of IoT

‘Internet of Things (IoT)’emerged as an intelligent collaborative computation and communication between a set of objects capable of providing on-demand services to other objects anytime anywhere. A large-scale deployment of data-driven cloud applications as well as automated physical things such as embed electronics, software, sensors and network connectivity enables a joint ubiquitous and pervasive internet-based computing systems well capable of interacting with each other in an IoT. IoT, a well-known term and a growing trend in IT arena certainly bring a highly connected global network structure providing a lot of beneficial aspects to a user regarding business productivity, lifestyle improvement, government efficiency, etc. It also generates enormous heterogeneous and homogeneous data needed to be analyzed properly to get insight into valuable information. However, adoption of this new reality (i.e., IoT) by integrating it with the internet invites a certain challenges from security and privacy perspective. At present, a much effort has been put towards strengthening the security system in IoT still not yet found optimal solutions towards current security flaws. Therefore, the prime aim of this study is to investigate the qualitative aspects of the conventional security solution approaches in IoT. It also extracts some open research problems that could affect the future research track of IoT arena

Energy-efficient task-scheduling and networking protocols for secure wireless networks

The performance of wireless networks is dependent on a number of factors including the available energy, energy-efficiency, data processing delay, transmission delay, routing decisions, security overhead, etc. Traditionally, due to limited resources, nodes were tasked with only collecting measurements and sending them to a base station or central unit for processing. With increased capabilities of microprocessors the data processing is pushed more toward network and its more capable nodes. This thesis focuses to virtualize the processing resources of the entire network and dynamically distribute processing steps along the routing path while optimizing performance. Additionally, a new multi-key encryption (MKE) scheme is proposed to optimize efficiency while enhancing security. The main benefit of the MKE scheme is the improved resilience of the advanced encryption standard (AES) against correlation power analysis (CPA) attack by breaking the correlation between power consumption and the used secret key. The MKE security scheme is analyzed with network implementation and studied for its effects on network parameters such as network connectivity, resilience against node capture and energy efficiency of the scheme. Moreover, a new analysis methodology is proposed to quantify a resilience of a network against node capture such that the strength of the underlying security mechanisms is taken into account. Furthermore, the tradeoff between security and network performance is addressed by the proposed task-scheduling scheme. Also, the proposed methodology does not make assumption of homogenous [sic] network that is often used in literature to simplify analysis and scheme design. In contrast, the proposed formulation is generic, thus allowing heterogeneous nodes to be used while guaranteeing network performance. Consequently, the proposed scheme creates a wireless computing cloud where the processing tasks are dynamically assigned to the nodes using the Dynamic Programming (DP) methodology. The processing and transmission decisions are analytically derived from network models in order to optimize the utilization of network resources including: available energy, processing capacity, security overhead, bandwidth etc. As a result, the online optimization of network resources is achieved --Abstract, page iv

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment

Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)

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

Sense and Sensitivity: Spatial Structure of conspecific signals during social interaction

Organisms rely on sensory systems to gather information about their environment. Localizing the source of a signal is key in guiding the behavior of the animal successfully. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. In this dissertation, I investigate the spatial dynamics of natural stimuli and explore how the electrosensory system of weakly electric fish encodes these realistic spatial signals. To do so In Chapter 2, I develop a model that examines the strength of the signal as it reaches the sensory array and simulates the responses of the receptors. The results demonstrate that beyond distances of 20 cm, the signal strength is only a fraction of the self-generated signal, often measuring less than a few percent. Chapter 2 also focuses on modeling a heterogeneous population of receptors to gain insights into the encoding of the spatial signal perceived by the fish. The findings reveal a significant decrease in signal detection beyond 40 cm, with a corresponding decrease in localization accuracy at 30 cm. Additionally, I investigate the impact of receptor density differences between the front and back on both signal detection and resolution accuracy. In Chapter 3, I analyze distinct movement patterns observed during agonistic encounters and their correlation with the estimated range of receptor sensitivity. Furthermore, I uncover that these agonistic interactions follow a classical pattern of cumulative assessment of competitors\u27 abilities. The outcome of this research is a comprehensive understanding of the spatial dynamics of social interactions and how this information is captured by the sensory system. Moreover, the research contributed to the development of a range of tools and models that will play crucial roles in future investigations of sensory processing within this system

Survey of Deployment Algorithms in Wireless Sensor Networks: Coverage and Connectivity Issues and Challenges

International audienceWireless Sensor Networks (WSNs) have many fields of application, including industrial, environmental, military, health and home domains. Monitoring a given zone is one of the main goals of this technology. This consists in deploying sensor nodes in order to detect any event occurring in the zone of interest considered and report this event to the sink. The monitoring task can vary depending on the application domain concerned. In the industrial domain, the fast and easy deployment of wireless sensor nodes allows a better monitoring of the area of interest in temporary worksites. This deployment must be able to cope with obstacles and be energy efficient in order to maximize the network lifetime. If the deployment is made after a disaster, it will operate in an unfriendly environment that is discovered dynamically. We present a survey that focuses on two major issues in WSNs: coverage and connectivity. We motivate our study by giving different use cases corresponding to different coverage, connectivity, latency and robustness requirements of the applications considered. We present a general and detailed analysis of deployment problems, while highlighting the impacting factors, the common assumptions and models adopted in the literature, as well as performance criteria for evaluation purposes. Different deployment algorithms for area, barrier, and points of interest are studied and classified according to their characteristics and properties. Several recapitulative tables illustrate and summarize our study. The designer in charge of setting up such a network will find some useful recommendations, as well as some pitfalls to avoid. Before concluding, we look at current trends and discuss some open issues