Mobile sinks are extensively used for data gathering in wireless sensor networks (WSNs). This method avoids imbalances in energy consumption caused by multihop transmission but may cause an extended delay time. In this paper, we focus on how to shorten the length of the traveling path to reduce the delay time of data gathering. We propose that the mobile sink visits the overlapping areas of communication ranges of sensors instead of sensors one by one. Next, we determine the visiting point of each overlapping area and use the traveling salesperson problem (TSP) algorithm to plan a traveling path. Because the visiting point is a point within the overlapping area of communication ranges of sensors, it is possible that the length of the traveling path can be reduced further. Hence, we attempt to shorten the traveling path obtained by the TSP algorithm. The benefit of the proposed method is that the number of visiting points is reduced after integration of visiting points. This method not only shortens the length of the traveling path for the mobile sink but reduces the computational effort required for traveling-path planning by the TSP algorithm as well. Moreover, we also consider data transfer rate in traveling-path planning to obtain a path that satisfies the constraint of the data transfer rate. Our experimental results show that the proposed algorithm delivers good results in terms of the computational effort and length of the traveling path.

In this paper, we define a new type of coverage problem named target-barrier coverage problem in wireless sensor networks. A target-barrier is a continuous circular barrier formed around the target. The target-barrier has a d _bound constraint that is set depending on applications and needs, where d _bound is the minimum distance of the constructed barrier from the target. Target-barrier coverage is very suited for application in defense surveillance, including detection of intrusion from outside and prevention of barrier breaching from inside. For instance, in a jail scenario, sensors can be deployed to enclose a jail with the constraint of d _bound to detect the escape of prisoners or unauthorized entry into the jail for rescuing prisoners. We focus on how to minimize the number of members required to construct target-barriers in a distributed manner while satisfying the d _bound constraint and minimizing the amount of message exchange required. In performance evaluation, we compare our solution with the solution of related work and the Brute-Force algorithm which can find the minimum number of target-barrier members required. Our experimental results show that the proposed algorithm delivers satisfactory results in terms of the number of target-barrier members required and the amount of message exchange required.

High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

Due to the rapid development of the Internet of Things, cloud computing, big data, and software-defined networks technologies, vehicular ad hoc networks have evolved into software-defined Internet of Vehicles (IoV). In software-defined IoV, messages transmitted by vehicles are transferred using wireless communication technology, so attackers can easily attack specific vehicles and Road-Side Units (RSUs) within the wireless communication range. When any vehicle or RSU is under attack or control, the entire network may collapse and produce incorrect computing results, which in the worst case, can lead to car accidents. Therefore, building a highly reliable and fault-tolerant software-defined IoV is of high importance. In this paper, we visit the consensus problem in software-defined IoV. Software-defined IoV is built with infrastructures, that is, RSUs. RSUs have a backhaul capacity and a powerful computing capacity. Thus, in software-defined IoV, we will focus on how to use RSUs to assist vehicles in reaching a consensus.

The Internet of Things (IoT) has received significant attention from scholars, governments and various industries in recent years. This is because IoT enables physical devices (objects) in the real world to be connected to the Internet. The environmental data gathered by these objects can contribute to the development of more valuable applications. One of the most important issues in IoT research is how to deploy sensors in the target field effectively. Through the effective deployment of sensors, IoT-based applications can gather sufficient information to support decision making. In this paper, we propose a new carrier-based sensor deployment algorithm to solve the sensor deployment problem in the perception layer of the IoT. The proposed algorithm will first match redundant sensors with uncovered areas and then connect the matching results to form a traveling path. Finally, the algorithm will adjust the traveling path to further shorten its length. Our experimental results show that the proposed algorithm delivers good results in terms of the length of the traveling path under varied parameter settings.