Internet of Things and Blockchain technologies have been dominating their respective research domains for some time. IoT offers automation at the finest level in different fields, while Blockchain provides secure transaction processing for asset exchanges. The capability of IoT devices to generate transactions prompts their integration with Blockchain as the next logical step. The biggest challenges in this integration are the scalability of ledger and rate of transaction execution in Blockchain. On one hand, due to their large numbers, IoT devices will generate transactions at a rate which current block chain solutions cannot handle. On the other hand, implementing Blockchain peers onto IoT devices is impossible due to resource constraints. This prohibits direct integration of both technologies in their current state. In this article, we propose a solution to address these challenges by using a local peer network to bridge the gap. It restricts the number of transactions which enters the global Blockchain by implementing a scalable local ledger, without compromising on the peer validation of transactions at local and global level. The testbed evaluations show significant reduction in the block weight and ledger size on global peers. The solution also indirectly improves the transaction processing rate of all peers due to load distribution.

Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.

Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for the next-generation Internet. The deployment and applications of IoT range from smart cities to urban computing and from ubiquitous healthcare to tactile Internet. For this reason, the physical infrastructure of heterogeneous network systems has become more complicated and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks and Network Function Virtualization has been extensively researched for IoT in the recent past. In this article, we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software-defined networks designed for IoT, function virtualization for IoT networks, and software-defined IoT networks. These categories are further divided into works that present architectural, security, and management solutions. Besides, the article highlights several short-term and long-term research challenges and open issues related to the adoption of software-defined Internet of Things.

Software-defined networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The controller in the control plane is the fundamental element used to manage all operations of the data plane. Hence, the performance and capabilities of the controller itself are essential in achieving optimal performance. Furthermore, the tools used to benchmark their performance must be accurate and useful in measuring different evaluation parameters. There are dozens of controller proposals for general and specialized networks in the literature. However, there is a very limited comprehensive quantitative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers, along with a quantitative analysis of their performance in different network scenarios. We categorize and classify 34 controllers and present a qualitative comparison. We also present a comparative analysis of controllers for specialized networks such as the Internet of Things, blockchain networks, vehicular networks, and wireless sensor networks. We also discuss in-depth capabilities of benchmarking tools and provide a comparative analysis of their capabilities. This work uses three benchmarking tools to compare 9 controllers and presents a detailed analysis of their performance, along with discussion on performance of specialized network controllers.

Truth discovery has received considerable attention in mobile crowdsensing systems. In real practice, it is vital to resolve conflicts among a large amount of sensory data and estimate the truthful information. Although truth discovery has been widely explored to improve aggregation accuracy, numerous security and privacy issues still need to be addressed. Existing schemes either do not guarantee the privacy of each participating user, or fail to consider practical needs in crowdsensing systems. In this paper, we present two reliable and privacy-preserving truth discovery schemes for different scenarios. Our first design is fit for applications where users are relatively stable. By employing the homomorphic Paillier encryption, one-way hash chain, and super-increasing sequence techniques, this approach not only guarantees strong privacy, but also is highly efficient and practical. Our second design suits applications where users are frequently moving. In such an application, we explore data perturbation and homomorphic Paillier encryption to shift all user workloads to the server side, without compromising users' privacy. Through detailed security analysis, we demonstrate that both schemes are secure, practical, and privacy-preserving. Moreover, extensive experiments based on real world and simulated mobile crowdsensing systems, we demonstrate the efficiency of our proposed schemes.

The integration of wearable wireless devices and cloud computing in e-health systems has significantly improved their effectiveness and availability. Patients can upload their personal health information (PHI) files to the cloud, from where the health service providers (HSPs) can obtain appropriate information to determine the health state. This system not only reduces the costs associated to healthcare, but also provides timely diagnosis to save lives. However, a number of privacy concerns arise while sharing sensitive information. In this work, we propose a novel privacy-preserving patient health information sharing scheme, which allows HSPs to access and search PHI files in a secure yet efficient manner. We make use of the searchable encryption technique with keyword range search and multi-keyword search. The proposed privacy-preserving equality test protocol allows different types of numeric comparison searches on encrypted data. We also use a variant of Bloom Filter and message authentication code to classify PHI files, filter false data, and check integrity of search results. Simulations on real world and synthetic data show the feasibility and efficiency of the system, and security analysis proves the privacy-preservation properties.