The key management is the central element of network security. In fact, key distribution is necessary for securing applications in the context of Internet of Things (IoT). However, existing key management protocols are not directly applicable on IoT due, among other things, to severe and high resource constraints of some devices that make up the IoT network. Therefore, it is necessary that the proposed key management protocols takes in charge these features and constraints. Most existing solutions didn't focus on optimizing, at the same time, all performance criteria, like communication, computation and storage. Some of them put special emphasis on minimizing one criteria but ignore the others. In this paper, we propose a new lightweight matrix based key management protocol for Iot network, which is not only flexible, scalable and resilient to many types of attacks, but also can reduce the communication, computation and storage overheads at constrained nodes side. The security properties like authentication, integrity and secrecy have been checked by using the formal verification tool AVISPA. Moreover, security and performance analysis show that our scheme protects user's sensitive data from several types of attacks by achieving secure end-to-end communications, and optimizes the energy consumption, which is suitable for resource-limited networks.

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Offloading is one major type of collaborations between mobile devices and clouds to achieve less execution time and less energy consumption. Offloading decisions for mobile cloud collaboration involve many decision factors. One of important decision factors is the network unavailability that has not been well studied. This paper presents an offloading decision model that takes network unavailability into consideration. Network with some unavailability can be modeled as an alternating renewal process. Then, application execution time and energy consumption in both ideal network and network with some unavailability are analyzed. Based on the presented theoretical model, an application partition algorithm and a decision module are presented to produce an offloading decision that is resistant to network unavailability. Simulation results demonstrate good performance of proposed scheme, where the proposed partition algorithm is analyzed in different application and cloud scenarios.

The availability of NFC capabilities on smartphones has facilitated the development of a large number of related applications. Some of these applications may be resourceintensive tasks; and Cloudlets-based mobile computing are a good candidate to offload computation while being free of WAN delays, jitter, congestion, and failures. In this context, new use cases dedicated to NFC applications based on cloudlets are presented and a security protocol is proposed to authenticate the cloudlets by the mobile devices. The secure element of the mobile device is a trust environment used to store sensitive data and to perform cryptographic calculations.

The emergence of the Internet of Things (IoT) and the advantages of Mobile Cloud Computing (MCC) have drawn a big absorption from technological experts. This paper investigates the integration of IoT and MCC by introducing the service architecture towards the applications of mobile devices, sensors and Cloud computing. The proposed approach not only presents the cutting edge convergence between IoT and MCC but also focuses on the smart home scenario to answer the question what the benefits are, coming from the integration of IoT and MCC. A developmental process of DropLock architecture dedicated to Smart City is also presented to demonstrate this convergence.

Named Data Networking (NDN) is a new architecture which allows communications using data's natural names rather than hosts' logical addresses. In recent years, several research projects have demonstrated the ability of NDN to support emerging IoT applications like home automation, smart cities and smart farming applications. This paper aims to integrate NDN with ZigBee to give NDN a better support for IoT applications that are known to require wireless sensing/actuating abilities, mobility support and low power consumption. For this purpose, we present our NDN-over-ZigBee design and we show through experiments conducted with three different scenarios the suitablity and the ease of use of NDN in IoT context. The choice of ZigBee is motivated by the fact that it is a network specification for low-power wireless personal area networks (WPANs) and supports a large number of nodes.

Urban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or a Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs a higher computational cost. This paper proposes a novel framework that combines the descriptive strength of the Gaussian Mixture Model with the high-performance classification capabilities of the Support Vector Classifier. A new approach is presented that uses the mean vectors obtained from the GMM model as input to the SVC. Experimental results show that the approach compares very favorably with baseline statistical methods.