With the advance of the Internet of Underwater Things, smart things are deployed under the water and form the underwater wireless sensor networks (UWSNs), to facilitate the discovery of vast unexplored ocean volume. A routing protocol, which is not expensive in packets forwarding and energy consumption, is fundamental for sensory data gathering and transmitting in UWSNs. To address this challenge, this paper proposes E-CARP, which is an enhanced version of the C hannel-Aware Routing Protocol (CARP) developed by S. Basagni et al., to achieve the location-free and greedy hop-by-hop packet forwarding strategy. Generally, CARP does not consider the reusability of previously collected sensory data to support certain domain applications afterwards, which induces data packets forwarding which may not be beneficial to applications. Besides, the PING-PONG strategy in CARP can be simplified for selecting the most appropriate relay node at each time point, when the network topology is relatively steady. These two research problems have been addressed by our E-CARP. Simulation results validate that our technique can decrease the communication cost significantly and increase the network capability to a certain extent.

Video sensors are used in wireless multimedia sensor networks (WMSNs) to enhance the capability for event description. Due to the limited transmission capacity of sensor nodes, a single path often cannot meet the requirement of video transmission. Consequently, multipath transmission is needed. However, not every path found by multipath routing algorithms may be suitable for transmitting video, because a long routing path with a long end to end transmission delay may not satisfy the time constraint of the video. Furthermore, each video stream includes two kinds of information: image and audio streams. In different applications, image and audio streams play different roles, and the importance levels are different. Higher priority should be given to the more important stream (either the image stream or the audio stream) to guarantee the using of limited bandwidth and energy in WMSNs. In this paper, we propose a Multipriority Multipath Selection (MPMPS) scheme in transport layer to choose the maximum number of paths from all found nodedisjoint routing paths for maximizing the throughput of streaming data transmission. Simulation results show that MPMPS can effectively choose the maximum number of paths for video transmission.

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Abstract-Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energyefficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geo-graphical size. These grid cells are assigned to clusters through an algorithm inspired by the k-dimensional tree algorithm, such that the energy consumption of each clus-ter is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.

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An exciting paradise of data is emerging into our daily life along with the development of the Web of Things. Nowadays, volumes of heterogeneous raw data are continuously generated and captured by trillions of smart devices like sensors, smart controls, readers and other monitoring devices, while various events occur in the physical world. It is hard for users including people and smart things to master valuable information hidden in the massive data, which is more useful and understandable than raw data for users to get the crucial points for problems-solving. Thus, how to automatically and actively extract the knowledge of events and their internal links from the big data is one key challenge for the future Web of Things. This paper proposes an effective approach to extract events and their internal links from large scale data leveraging predefined event schemas in the Web of Things, which starts with grasping the critical data for useful events by filtering data with well-defined event types in the schema. A case study in the context of smart campus is presented to show the application of proposed approach for the extraction of events and their internal semantic links.

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Using multimedia sensor nodes in wireless sensor networks (WSNs) can significantly enhance the capability of WSNs for event description. Different kinds of holes can easily appear in WSNs. How to efficiently transmit multimedia streaming data and bypass all kinds of holes is a challenging issue. Moreover, some applications do not need WSNs to work for a long lifetime, e.g. monitoring an erupting volcano. These applications generally expect that WSNs can provide continuous streaming data during a relatively short expected network lifetime. Two basic problems are: (1) gathering as much data as possible within an expected network lifetime; (2) minimizing transmission delay within an expected network lifetime. In this paper, we proposed a cross-layer approach to facilitate the continuous one shot event recording in WSNs. We first propose the maximum streaming data gathering (MSDG) algorithm and the minimum transmission delay (MTD) algorithm to adjust the transmission radius of sensor nodes in the physical layer. Following that the two-phase geographical greedy forwarding (TPGF) routing algorithm is proposed in the network layer for exploring one/multiple optimized holebypassing paths. Simulation results show that our algorithms can effectively solve the identified problems.