Abstract:Due to the dynamics of spectrum and ad-hoc networking, traditional clustering protocols have the issue of high clustering frequency and large communication overhead. To address the issue, this paper for the first time proposes a network stability-aware clustering (NSAC) protocol for cognitive radio sensor networks. In NSAC protocol, each node searches the maximum edge biclique based on its neighbors and available channels. The weight of one node is calculated based on the remaining energy, the numbers of nodes and available common control channels in its maximum edge biclique. The node with the largest weight in the neighborhood will be selected as the cluster head, and the rest of nodes in the neighborhood join the cluster as members. As energy consumption and spectrum dynamics are sufficiently considered in the clustering process, network lifetime and network stability are significantly enhanced. Extensive simulations show that the proposed NSAC protocol markedly outperforms existing works in the aspects of network lifetime and network stability.

Abstract:This paper proposes a rotational direction algorithm based on time of arrival (TOA) ranging with UWB transmission equipment. During rotating UWB receiver around the human body, the maximum and minimum ranging can confirm the relative direction of the transmitter and receiver. But in the indoor complex environment, multipath effect, human body will cause large TOA ranging errors and error distribution is non-Gaussian. So, the relative orientation is indeed a template matching problem. This paper implements the secondary matching scheme, and analyzes the key parameters in the secondary matching for optimal value analysis and realizes high-precision horizontal direction detection. 4 degrees deviation is achieved, and the accuracy is improved by 9% relative to the single match method.

Abstract:Recommender system can solve the information overload problem effectively, and collaborative filtering (CF) is one of the techniques that is widely used in recommendation system. However, the traditional CF technology has problems such as poor scalability, sparse data, and low accuracy of recommendation results. In order to improve the quality of recommendations, this article integrates the trust relationship into the recommendation system in which the trust relationship is clustered by using the clustering (FCM) method. Using the trust cluster to predict implicit trust between users, the trust relationship is finally combined with the user-item relationship to give recommendations. The experimental results on the data set of Douban and Epinions show that compared with traditional CF algorithm, trust based recommendation algorithm and recommendation algorithm for user item clustering, the presented algorithm can greatly improve the recommendation quality and time efficiency.

Abstract:Connectivity is an important metric of network performance. In opportunistic sensor networks (OSNs), the frequent topology change caused by node mobility leads to the challenges of representation of connectivity. Building connectivity model for OSNs is helpful for its optimization and maintenance. After analyzing the topological characteristics of OSNs, this paper constructs connectivity model based on temporal-spatial graph theory, which is employed to describe the topological evolution law. According to the message reachability, temporal distance and topological distance are defined based on message transmission temporal and spatial characteristics. The correlation between them is also analyzed by statistical product and service solutions (SPSS), and the results show that there is no obvious correlation. Thus temporal distance and topological distance of each snapshots are employed to construct overall network connectivity. Experimental results show that the proposed model can better depict the network connectivity of OSNs, and comparing with the connectivity model based on Katz centrality, it can represent the connectivity changes better.

Abstract:It is crucial to appropriately deploy Roadside Units (RSUs) to guarantee the quality of communication service. The Delta Network metric △_ρ2^ρ1 is an important metric to evaluate the QoS of VANET. Based on this metric, Sarubbi, et al. proposed a deployment method Delta-r by using relative trip duration. However, the "false high" relative trip duration of vehicles at urban cells might play a negative effect on decision-making. In order to solve this problem, an improved algorithm Delta-uc, which is based on Useful Contribution, is presented. It avoids the negative effect of "extra" trip duration by retaining only the useful relative trip duration of a vehicle at an urban cell. The experimental data are coming from realistic mobility trace of Cologne, Germany. Experimental results indicate that in many cases of service requirement metrics, the Delta-uc algorithm can obtain fewer roadside units than the Delta-r algorithm, and is more practical in realistic applications.

Abstract:Target monitoring and data acquisition is one of the most widely used applications in the Internet of Things system. Monitoring the mobile target in IoT will cause the IoT nodes that can capture the target resource, i.e., the access location of the resource, to change constantly. Therefore, with the increasing mobility of devices, dynamic resource linking and data access methods have become a key technology that affects the performance of IoT applications. This paper proposes an application-layer protocol, Sealink, for dynamic resources, which aims at addressing the issue of the increase of access delay caused by resource dynamics and the decrease of response rate. The protocol adopts RESTful design style, uses URI to describe dynamic resources, and uses LSTM algorithm to predict and model the accessible location of dynamic resources, thereby reducing access latency and improving request response rate. Experimental results show that Sealink can not only reduce the number of requests for accessing dynamic resources between IoT nodes, but also reduce the access latency by 75.5% on average compared with the HTTP protocol.

Abstract:With the popularity of wireless networks and smart devices, indoor positioning has been rapidly developed. In indoor positioning, the fingerprint-based positioning method has gradually become a research hotspot because it does not require external facilities and strong anti-interference. The development of deep learning in recent years has brought new opportunities for improving the accuracy of fingerprint positioning algorithms. This paper proposes a convolutional neural network (CNN)-based fingerprint location algorithm to improve the construction of the fingerprint database. First, the collected CSI and magnetic field data is processed through CNN, and the CNN model parameter values are used at each reference point as fingerprint. Then a probabilistic method is utilized for the final fingerprint matching. Experimental results show that the proposed positioning algorithm has better robustness and higher positioning accuracy than the traditional fingerprint positioning algorithm.

Abstract:RF energy harvesting wireless sensor network (RFEH-WSN) is a recent active research field in the domain of wireless sensor network. The use of dedicated energy transmitter (ET) has further enhanced the controllability and practicability of wireless sensor networks. One issue worth researching in this kind of WSNs is how to configure a given number of ET to optimize transmission power and reduce energy consumption under the premise of meeting node energy requirements. Firstly, the ET placement and power optimization problem are formulated to make clear the optimization objective and constraints. Then two optimization schemes are proposed. One is a two-step optimization scheme by which the transmission power of ET will be adjusted after placing ET. Another scheme is a joint optimization of placement and transmission power based on particle swarm optimization (PSO). Simulation results show that the energy saving effect of the joint optimization scheme is more obvious than that of the other.

Abstract:Due to the extensive use of code reuse and third-party SDKs, open source components are ubiquitous in IoT device firmware. The security vulnerabilities which cause threat to the firmware usually exist in some specific versions of the components. The version information identification of open source binary components in the IoT firmware is of great significance for the safety assessment and emergency response of IoT devices. The existing version string based version extraction method is not applicable to the cases with missing version strings. This paper designs and implements a version extraction method (termed as Protues) for open source components that does not depend on version strings. The core idea of this method is to construct a version difference chain by using the differences between the open source components' neighboring versions of the source code to convert the version identification problem into a query on the version difference chain. Furthermore, in order to improve the recognition accuracy, the conditional judgment expressions are used in this paper to represent the nodes on the version difference chain. To verify the practicability of this method, version identification experiments are performed on a total number of 428 binary files from 4 kinds of open source components Samba, Msmtp, Nginx and Libgcrupt. The experimental results show that the number of versions that can be accurately identified by this method reaches 418, and the recognition accuracy rate is 98%.

Abstract:Pocket switched networks (PSN) is a kind of delay tolerant network (DTN) which transmits messages based on node movement and opportunity encounter. The traditional similarity index based prediction methods are not suitable for the link prediction of PSN due to the frequent topology change and time-various link in PSN. According to the characteristics of PSN node behavior, this paper analyzes connection time, connection duration, and the law of node accessing areas. Node similarity is categorized into sociality behavior similarity and movement behavior similarity. After comparing AUC and Precision with different weight for sociality behavior similarity and movement behavior similarity, similarity index based on node behavior patterns (SNBP) is proposed. The experiment results on MIT Reality and Dartmouth Campus datasets show that comparison with CN, AA, and Katz similarity index, the proposed similarity index has better precision.

Abstract:Most of the existing state-of-the-art studies assume that there is only one task requester for the crowdsourcing systems, which is hard to satisfy the scenario of multiple task requesters. Thus, this work focuses on the crowdsourcing systems consisting of multiple task requesters, where all the requesters and workers can bid or complete multiple tasks in each round. On the basis of that, this paper proposes a truthful double auction mechanism for crowdsourcing systems to incentivize workers and allow efficient task completion. First of all, this method abstracts the workers that interested in multiple tasks into multiple virtual workers, and further designs an efficient task assignment mechanism based on the principle of low bids first matching. A truthful pricing mechanism is developed based on the principle of uniform pricing. In addition, a proof is given to show that the proposed mechanism satisfies three essential economic characteristics:truthfulness, individual rationality, and budget balance through substantial theoretical analysis. Finally, extensive simulations are conducted to study the performance of the proposed auction mechanism, and the simulation results corroborate the theoretical analysis.

Abstract:LoRa network uses wideband linear frequency modulation techniques and employs different spreading factor (SF) settings to achieve different transmission rates. When there are many nodes in the network and collision occurs, nodes with higher spreading factor have lower transmission speed and longer transmission time, and are more prone to collision. The transmission success rate is also relatively low. Therefore, when each spreading factor in the network allocates the same number of nodes, it is not fair for low-rate transmission, because its channel takes longer and collision probability is greater. This paper proposes an allocation mechanism based on the spreading factor (FSA). The high spreading factor is more prone to collision and its transmission success rate is also low. Therefore, fewer nodes are assigned to the high spreading factor and low spreading factor allocates more nodes, so that the transmission success rate of different spreading factors is the same. Consequently the problem of fairness caused by the unreasonable allocation of the spreading factor of the nodes in the collision network is effectively solved, the probability of retransmission of high spreading factor nodes is reduced, and the stability of the network is greatly improved.