Typical wireless sensor networks have restricted resources on memory capacity, computing/processing power, and energy supply. However, wireless sensor network has been increasingly applied to various applications and security in wireless sensor networks has become an essential and challenging issue. There is a wide range of security attacks in wireless sensor networks. A node replication attack is a type of attacks in wireless sensor networks. An attacker can be easily disguised in a wireless sensor network and acts as an intermediate node to intercept data packets. The attacker could be a clone node in the network by capturing nodes' ID, the pair-wise key etc. The base station may not be able to distinguish between good nodes and malicious nodes. In this paper, we present a new method, named Area-Based Clustering Detection (ABCD) method, for detecting node replication attacks. Our simulation results show that the proposed ABCD method can achieve high successful detection rate while decrease the communication overheads when compared with the Line-Selected Multicast (LSM) method previously proposed in the literature. The proposed ABCD method can also maintain the network lifetime and decrease the number of stored messages when compared with a centralized approach.

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Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT service providers (SPs) are emerging in the market and competing to offer their services. Many IoT applications utilize these services in an integrated manner with different Quality-of-Service (QoS) requirements. Thus, the provisioning of end-to-end QoS is getting more indispensable for IoT platforms. However, provisioning the system by using only QoS metrics without considering user experiences is not sufficient. Recently, Quality of Experience (QoE) model has become a promising approach to quantify actual user experiences of services. A holistic design approach that considers constraints of various QoS/QoE metrics together is needed to satisfy requirements of these applications and services. Besides, IoT services may operate in environments with limited resources. Therefore, effective management of services and system resources is essential for QoS/QoE support. This paper provides a comprehensive survey for the state-of-the-art studies on IoT services with QoS/QoE perspective. Our contributions are threefold: (1) QoEdriven architecture is demonstrated by classifying vital components according to QoE-related functions in prior studies, (2) QoE metrics and QoE optimization objectives are classified by corresponding system and resource control problems in the architecture, and (3) QoE-aware resource management e.g., QoE-aware offloading, placement and data caching policies with recent Machine Learning approaches are extensively reviewed. INDEX TERMSInternet of Things, Quality of Service, Quality of Experience, IoT services, IoT applications, QoS for IoT services, QoS metrics, QoE metrics, IoT architecture Recently, emerging IoT architectures with multi-layers, e.g., Mobile Edge Computing (MEC), Fog Computing and Cloud Computing, have been proposed to improve user experiences.