http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper1.pdf

Security, privacy and trust in Internet of Things

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future.

Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.

Wireless sensor networks: a survey on recent developments and potential synergies

Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Trust management for the Semantic Web

Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology—remote patient monitoring—is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient’s life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.

https://www.mdpi.com/1424-8220/19/2/326/pdf

A Decentralized Privacy-Preserving Healthcare Blockchain for IoT

A survey of communication/networking in Smart Grids

Much of the existing work on wireless sensor networks (WSNs) has focused on addressing the power and computational resource constraints of WSNs by the design of specific routing, MAC, and cross-layer protocols. Recently, there have been heightened privacy concerns over the data collected by and transmitted through WSNs. The wireless transmission required by a WSN, and the self-organizing nature of its architecture, makes privacy protection for WSNs an especially challenging problem. This paper provides a state-of-the-art survey of privacy-preserving techniques for WSNs. In particular, we review two main categories of privacy-preserving techniques for protecting two types of private information, data-oriented and context-oriented privacy, respectively. We also discuss a number of important open challenges for future research. Our hope is that this paper sheds some light on a fruitful direction of future research for privacy preservation in WSNs.

Privacy preservation in wireless sensor networks: A state-of-the-art survey

Sink mobility has attracted much research interest in recent years because it can improve network performance such as energy efficiency and throughput. An energy-unconscious moving strategy is potentially harmful to the balance of the energy consumption among sensor nodes so as to aggravate the hotspot problem of sensor networks. In this paper, we propose an autonomous moving strategy for the mobile sinks in data-gathering applications. In our solution, a mobile sink approaches the nodes with high residual energy to force them to forward data for other nodes and tries to avoid passing by the nodes with low energy. We performed simulation experiments to compare our solution with other three data-gathering schemes. The simulation results show that our strategy cannot only extend network lifetime notably but also provides scalability and topology adaptability.

/pdf/hums-an-autonomous-moving-strategy-for-mobile-sinks-in-data-3se0b1jr7k.pdf

HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks

In general, the encounter probability is leveraged to evaluate whether a node is a qualified next-hop forwarder in Opportunistic Networks (OppNets); however, it does not truly present the node's competency of delivering data in a hostile wireless environment, because a malicious node can arbitrarily bloat that probability to intercept data from others. In this paper, we design a trust-based framework to more accurately evaluate an encounter's delivery competency, which can be flexibly integrated with a large family of existing data forwarding protocols designed for OppNets. As a case study, we integrate our proposed framework with PROPHET, and demonstrate its effectiveness against ''black hole'' attacks through experimental study.

A trust-based framework for data forwarding in opportunistic networks

In opportunistic networks, the probability of encountering a destination node is popularly used to select a qualified forwarder; however, it can not represent the competency of delivering data in a hostile wireless environment, because a malicious node can bloat its such probability to intercept data from others. In this paper, we design a reputation-based framework to more accurately evaluate an encounter's competency of delivering data, which can be integrated with a large family of existing data forwarding protocols in opportunistic networks. In particular, a special message, called Positive Feedback Message (PFM), is proposed to help monitor the forwarding behavior of a node. We also design a Reputation-Assisted Data forwarding protocol for Opportunistic Networks (RADON), which integrates our reputation framework with a bare-bone data forwarding protocol using the number of times of previous encounters as the metric to select the next qualified forwarder. Through simulation experiments, we demonstrate that RADON effectively improves the network performance (e.g., data delivery ratio) against "black hole" attacks.

RADON: reputation-assisted data forwarding in opportunistic networks

Online social networks routinely publish data of interest to third parties, but in so doing often reveal relationships, such as a friendship or contractual association, that an attacker can exploit. This systematic look at existing privacy-preservation techniques highlights the vulnerabilities of users even in networks that completely anonymize identities. Through a taxonomy that categorizes techniques according to the degree of user identity exposure, the authors examine the ways that existing approaches compromise relation privacy and offer more secure alternatives.

Na Li

Papers

Privacy preservation in wireless sensor networks: A state-of-the-art survey

HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks

A trust-based framework for data forwarding in opportunistic networks

RADON: reputation-assisted data forwarding in opportunistic networks

Preserving Relation Privacy in Online Social Network Data