Tie Qiu

A greedy model with small world for improving the robustness of heterogeneous Internet of Things

Robustness is an important and challenging issue in the Internet of Things (IoT), which contains multiple types of heterogeneous networks. Improving the robustness of topological structure, i.e., withstanding a certain amount of node failures, is of great significance especially for the energy-limited lightweight networks. Meanwhile, a high-performance topology is also necessary. The small world model has been proven to be a feasible way to optimize the network topology. In this paper, we propose a Greedy Model with Small World properties (GMSW) for heterogeneous sensor networks in IoT. We first present the two greedy criteria used in GMSW to distinguish the importance of different network nodes, based on which we define the concept of local importance of nodes. Then, we present our algorithm that transforms a network to possess small world properties by adding shortcuts between certain nodes according to their local importance. Our performance evaluations demonstrate that, by only adding a small number of shortcuts, GMSW can quickly enable a network to exhibit the small world properties. We also compare GMSW with a latest related work, the Directed Angulation toward the Sink Node Model (DASM), showing that GMSW outperforms DASM in terms of small world characteristics and network latency.

Evaluating IEEE 802.15.4 for Cyber-Physical Systems

With rapid advancements in sensing, networking, and computing technologies, recent years have witnessed the emergence of cyber-physical systems (CPS) in a broad range of application domains. CPS is a new class of engineered systems that features the integration of computation, communications, and control. In contrast to general-purpose computing systems, many cyber-physical applications are safety critical. These applications impose considerable requirements on quality of service (QoS) of the employed networking infrastruture. Since IEEE 802.15.4 has been widely considered as a suitable protocol for CPS over wireless sensor and actuator networks, it is of vital importance to evaluate its performance extensively. Serving for this purpose, this paper will analyze the performance of IEEE 802.15.4 standard operating in different modes respectively. Extensive simulations have been conducted to examine how network QoS will be impacted by some critical parameters. The results are presented and analyzed, which provide some useful insights for network parameter configuration and optimization for CPS design.

EABS: An Event-Aware Backpressure Scheduling Scheme for Emergency Internet of Things

The backpressure scheduling scheme has been applied in Internet of Things, which can control the network congestion effectively and increase the network throughput. However, in large-scale Emergency Internet of Things (EIoT), emergency packets may exist because of the urgent events or situations. The traditional backpressure scheduling scheme will explore all the possible routes between the source and destination nodes that cause a superfluous long path for packets. Therefore, the end-to-end delay increases and the real-time performance of emergency packets cannot be guaranteed. To address this shortcoming, this paper proposes EABS, an event-aware backpressure scheduling scheme for EIoT. A backpressure queue model with emergency packets is first devised based on the analysis of the arrival process of different packets. Meanwhile, EABS combines the shortest path with backpressure scheme in the process of next-hop node selecting. The emergency packets are forwarded in the shortest path and avoid the network congestion according to the queue backlog difference. The extensive experiment results verify that EABS can reduce the average end-to-end delay and increase the average forwarding percentage. For the emergency packets, the real-time performance is guaranteed. Moreover, we compare EABS with two existing backpressure scheduling schemes, showing that EABS outperforms both of them.

ROSE: Robustness Strategy for Scale-Free Wireless Sensor Networks

Due to the recent proliferation of cyber-attacks, improving the robustness of wireless sensor networks (WSNs), so that they can withstand node failures has become a critical issue. Scale-free WSNs are important, because they tolerate random attacks very well; however, they can be vulnerable to malicious attacks, which particularly target certain important nodes. To address this shortcoming, this paper first presents a new modeling strategy to generate scale-free network topologies, which considers the constraints in WSNs, such as the communication range and the threshold on the maximum node degree. Then, ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed. Given a scale-free topology, ROSE exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks. Meanwhile, ROSE keeps the degree of each node in the topology unchanged such that the resulting topology remains scale-free. The extensive experimental results verify that our new modeling strategy indeed generates scale-free network topologies for WSNs, and ROSE can significantly improve the robustness of the network topologies generated by our modeling strategy. Moreover, we compare ROSE with two existing robustness enhancing algorithms, showing that ROSE outperforms both.

Heterogeneous ad hoc networks

The Heterogeneous Ad Hoc Networks (HANETs) are important components of the Internet of things, which become an inevitable trend in the future researches and applications. In recent years, the ad hoc networks have been widely employed in many fields, especially in environment monitoring, weapon control, intelligent transportation, smart city and other domains. HANETs consist of wireless sensor networks, smart ad hoc networks, wireless fidelity networks, telecommunication networks, vehicular ad hoc networks, etc. The digital information and physical objects are integrated through appropriate communication methods, thus new applications and services are created. Different applications use the independent network structures, which form a heterogeneous network platform and increase operational complexity of communication between each other. This paper presents a typical architecture of the large-scale HANETs, and investigates research advances of the current key technologies. To address existing issues, we suggest some potential solutions to deal with the current challenges, such as self-organization, big data transmission, privacy protection, data fusion and processing for large-scale HANETs.

A Robust Time Synchronization Scheme for Industrial Internet of Things

Energy-efficient and robust-time synchronization is crucial for industrial Internet of things (IIoT). Some energy-efficient time synchronization schemes that achieve high accuracy have been proposed recently. However, some unsynchronized nodes namely isolated nodes exist in the schemes. To deal with the problem, this paper presents R-Sync, a robust time synchronization scheme for IIoT. We use a pulling timer to pull isolated nodes into synchronized networks whose initial value is set according to level of spanning tree. Then, another timer is set up to select backbone node and its initial value is related to the distance to parent node. Moreover, we do experiments based on simulation tool NS-2 and testbed based on wireless hardware nodes. The experimental results show that our approach makes all the nodes get synchronized and gets the better performance in terms of accuracy and energy consumption, compared with three existing time synchronization algorithms TPSN, GPA, STETS.

A task-efficient sink node based on embedded multi-core SoC for Internet of Things

Abstract With the increase of collected information, the computing performance of single-core sink node for Internet of Things (IoTs) cannot satisfy with the demand of large data processing any more. Therefore, the sink node which based on embedded multi-core SoC for IoTs and maximizing its computing performance has brought into focus in recent years. In this paper, we design a multi-core Task-Efficient Sink Node (TESN) based on heterogeneous architecture and the Weighted-Least Connection (WLC) task schedule strategy has been proposed to improve its efficiency. There are two types of cores in the sink node, master core and slave cores. The master core deals with tasks allocation and the seven slave cores deal with data processing. All of the cores are communicating with each other through mailbox. By considering each core’s real-time processing information and computing performance, the proposed WLC can balance each core’s load and reduce network congestion. The X i l i n x V 5 platform is used to evaluate the performance of WLC and Round-Robin (RR) algorithms for multi-core sink node. The experiment results show that the WLC strategy improves the processing speed obviously, achieves load balance and avoids large scale congestion of sink node in the sensor networks of IoTs.

ERGID: An efficient routing protocol for emergency response Internet of Things

Abstract In recent years, Internet of Things (IoT) has been applied to many different fields such as smart home, environmental monitoring and industrial control system, etc. Under the pressure of the continuous expansion of network scale, how to ensure the real-time emergency response ability during data transmission has become a challenging problem for researchers. In this paper, we propose a routing protocol for Emergency Response IoT based on Global Information Decision (ERGID) to improve the performances of reliable data transmission and efficient emergency response in IoT. Specifically, we design and realize a mechanism called Delay Iterative Method (DIM), which is based on delay estimation, to solve the problem of ignoring valid paths. Moreover, a forwarding strategy called Residual Energy Probability Choice (REPC) is proposed to balance the load of network by focusing on the residual energy of node. Simulation results and analysis show that ERGID outperforms EA-SPEED and SPEED in terms of end to end (E2E) delay, packet loss and energy consumption. Additionally, we also carry out some practical experiments with STM32W108 sensor nodes, and observe that ERGID can improve the real-time response ability of network.

Fog Computing Based Face Identification and Resolution Scheme in Internet of Things

The identification and resolution technology are the prerequisite for realizing identity consistency of physical–cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individuals identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.

A Lifetime-Enhanced Data Collecting Scheme for the Internet of Things

A backpressure-based data collecting scheme has been applied in the Internet of Things, which can control the network congestion effectively and increase the network throughput. However, there is an obvious shortcoming in the traditional backpressure data collecting scheme for the network service chain. It attempts to search all possible paths between source node and destination node in the networks, causing an unnecessary long path for data collection, which results in large end-toend delay and redundant energy consumption. To address this shortcoming of backpressure data collecting scheme in the Internet of Things, this article proposes an energy-aware and distance-aware data collecting scheme to enhance the lifetime of backpressure-based data collecting schemes. We propose an energy- and distance-based model that combines the factors of queue backlog, hop counts, and residual energy for making routing decisions. It not only reduces the unnecessary energy consumption, but also balances the residual energy. The experiment results show that the proposed scheme can reduce unnecessary energy consumption and end-to-end delay compared to the traditional and LIFO-based schemes. Meanwhile, it balances the energy of nodes and extends the lifetime of an Internet of Things.