Heterogeneous Internet of Things (HetIoT) is an emerging research field that has strong potential to transform both our understanding of fundamental computer science principles and our future living. HetIoT is being employed in increasing number of areas, such as smart home, smart city, intelligent transportation, environmental monitoring, security systems, and advanced manufacturing. Therefore, relaying on strong application fields, HetIoT will be filled in our life and provide a variety of convenient services for our future. The network architectures of IoT are intrinsically heterogeneous, including wireless sensor network, wireless fidelity network, wireless mesh network, mobile communication network, and vehicular network. In each network unit, smart devices utilize appropriate communication methods to integrate digital information and physical objects, which provide users with new exciting applications and services. However, the complexity of application requirements, the heterogeneity of network architectures and communication technologies impose many challenges in developing robust HetIoT applications. This paper proposes a four-layer HetIoT architecture consisting of sensing, networking, cloud computing, and applications. Then, the state of the art in HetIoT research and applications have been discussed. This paper also suggests several potential solutions to address the challenges facing future HetIoT, including self-organizing, big data transmission, privacy protection, data integration and processing in large-scale HetIoT.

Industrial mobile network is crucial for industrial production in Internet of Things. It guarantees the normal function of machines and the normalization of industrial production. However this characteristic might be utilized by spammers to attack others and influence industrial production. The users who only share spams such as links with virus and advertisement are called spammers. With the growth of mobile network members, spammers are organized in groups for the purpose of benefit maximization, which has caused confusion and heavy loss to industrial production. It is difficult to distinguish spammers from normal users due to characteristics of multidimensional data. To address this problem, this paper proposes a Spammer Identification scheme based on Gaussian Mixture Model (SIGMM) that utilizes machine learning for industrial mobile networks. It works on the intelligent identification of spammers without relying on the flexible and unreliable relationship. SIGMM combines the presentation of data. Each user node is classified to one class in the construction process of model. Finally, we validate SIGMM by comparing it with RMA, HFCM schemes using mobile network dataset from cloud server. Simulation results show that SIGMM outperforms these previous schemes in terms of recall, precision and time complexity.

The emerging deployment of geographically distributed data centers (DCs) incurs a significant amount of data transfers over the Internet. Such transfers are typically charged by Internet service providers with the widely adopted qth percentile charging model. In such a charging model, the time slots with top (100-q) percent of data transmission do not affect the total transmission cost and can be viewed as ``free.'' This brings the opportunity to optimize the scheduling of inter-DC transfers to minimize the entire transmission cost. However, a very little work has been done to exploit those ``free'' time slots for scheduling inter-DC transfers. The crux is that existing work either lacks a mechanism to accumulate traffic to ``free'' time slots, or inevitably relies on prior knowledge of future traffic arrival patterns. In this paper, we present TrafficShaper, a new scheduler that shapes the inter-DC traffic to exploit the ``free'' time slots involved in the qth percentile charging model, so as to reduce or even minimize the transmission cost. When shaping traffic, TrafficShaper advocates a simple principle: more traffic peaks should be scheduled in ``free'' time slots, while less traffic differentiation should be maintained among the remaining time slots. To this end, TrafficShaper designs a pricing-aware control framework, which makes online decisions for inter-DC transfers without requiring a prior knowledge of traffic arrivals. To verify the performance of TrafficShaper, we conduct rigorous theoretical analysis based on Lyapunov optimization techniques, large-scale trace-driven simulations, and small-scale testbed implementation. Results from rigorous mathematical analyses demonstrate that TrafficShaper can make the transmission cost arbitrarily close to the optimum value. Extensive trace-driven simulation results show that TrafficShaper can reduce the transmission cost by up to 40.23%, compared with the state-of-the-art solutions. The testbed experiments further verify that TrafficShaper can realistically reduce the transmission cost by up to 19.38%.

This paper studies the practically important problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the ranges specified by the user. The existing RFID protocols that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ), in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag range information from singleton and even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ. To optimize the number of queried ranges, we further propose the Partition&Mergence (PM) approach that consists of two steps, i.e., top-down partitioning and bottom-up merging. Sufficient theoretical analyses are proposed to optimize the involved parameters, thereby minimizing the time cost of RQ+PM or minimizing its energy cost. We can trade off between time cost and energy cost by adjusting the related parameters. The prominent advantages of the RQ+PM protocol over previous protocols are two-fold: (i) it is able to make use of the collision slots, which are treated as useless in previous protocols. Thus, frame utilization can be significantly improved; (ii) it is immune to the interference from unexpected tags, and does not suffer information corruption issue. We use USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ+PM. Extensive simulation results reveal that RQ+PM can ensure 100% query accuracy, and reduce the time cost as much as 40% when comparing with the state-of-the-art protocols.

Nowadays, service function chaining (SFC) becomes more and more widespread and profound to implement flexible and economical virtual network infrastructures for the Internet of Things (IoT). With the benefits of SFC, the IoT service providers can steer massive traffic through a sequence of heterogeneous virtual network function instances based on their business logic. SFC is viewed as an attractive solution for building virtualized IoT-dedicated network. However, the SFC orchestration in IoT is still a challenge problem. Existing work usually focuses on the performance guarantee while ignoring the issue of resource idleness. To meet the sharp increase in IoT traffic amounts and the diversification of IoT traffic requirements, it is necessary to implement the performance and resource aware SFC orchestration system. Motivated by this, we propose a novel linear programming model and an effective approximation optimization algorithm for SFC orchestration, in order to achieve performance guarantee while avoiding resource idleness. Based on the proposed model and algorithm, a new prototype system named performance and resource aware orchestration system of SFC for IoT (PRSFC-IoT) is built upon OpenStack for online SFC orchestration. A large number of simulation experiments show that the PRSFC-IoT outperforms existing solutions for SFC orchestration in IoT.

This paper focuses on the topology optimization problem to decrease transmission delay and prolong network lifetime on the premise of reliable transmission in Underwater Wireless Sensor Networks (UWSNs). This is extremely challenging owing to dynamic ocean current movement, harsh underwater communication channel and increasingly demanding application requirements. With the development of software defined network architectures, the centralized topology control (TC) strategy with a global perspective in UWSNs is expected to become a more effective way to tackle the above challenges compared with the existing distributed and heuristic TC strategies involving local network state information. Therefore, we first transform the topology optimization problem of UWSNs into an integer nonlinear programming (INLP) model and design a centralized TC framework to solve the INLP model. In this framework a TC center is built to periodically generate the network topology for UWSNs according to the current network state information. Further, an efficient topology generation algorithm based on deep reinforcement learning (TGA-DRL) is proposed in the TC center. In TGA-DRL, to reduce computing overhead and improve operational efficiency, we formulate an action-space narrowed Markov decision process suitable for network topology generation and solve it with the aid of the rainbow algorithm which is a deep reinforcement learning model. Finally, the performance of our centralized TC framework is verified in terms of the node out-degree, algorithm convergence and optimization effect.