The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.

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Social-based routing approaches in delay-tolerant networks have attracted widespread attention in recent years, which attempt to import social behaviors and relations in real scene for node mobility. However, most social-based schemes resort to users' contact history and social relations that are dynamic, causing it so hard to establish stable relations between nodes. In this paper, we propose a utility-aware data transmission scheme which considers both internal property and external contact of nodes. Inspired by the concept of transfer station in real life, we set a central group and choose nodes for message forwarding, which have higher utility, i.e., enough energy, adequate cache, and more nodes encountered during the motivation. Two extensions are proposed also to further reduce the overhead. Simulation results demonstrate the increase in delivery ratio and decrease in overhead ratio, especially in large scale scenarios.

Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur's entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image's spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur's entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

In conventional directional sensor networks, coverage control for each sensor is based on a 2D directional sensing model. However, 2D directional sensing model failed to accurately characterize the actual application scene of image/video sensor networks. To remedy this deficiency, we propose a 3D directional sensor coverage-control model with tunable orientations. Besides, a novel criterion for judgment is proposed in view of the irrationality that traditional virtual potential field algorithms brought about on the criterion for the generation of virtual force. Furthermore, cross-set test is used to determine whether the sensory region has any overlap and coverage impact factor is introduced to reduce profitless rotation from coverage optimization, thereby the energy cost of nodes was restrained and the performance of the algorithm was improved. The extensive simulations results demonstrate the effectiveness of our proposed 3D sensing model and IPA3D (improved virtual potential field based algorithm in three-dimensional directional sensor networks).

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Coverage pattern and deployment strategy are directly related to the optimum allocation of limited resources for wireless sensor networks, such as energy of nodes, communication bandwidth, and computing power, and quality improvement is largely determined by these for wireless sensor networks. A three-dimensional coverage pattern and deployment scheme are proposed in this paper. Firstly, by analyzing the regular polyhedron models in three-dimensional scene, a coverage pattern based on cuboids is proposed, and then relationship between coverage and sensor nodes' radius is deduced; also the minimum number of sensor nodes to maintain network area's full coverage is calculated. At last, sensor nodes are deployed according to the coverage pattern after the monitor area is subdivided into finite 3D grid. Experimental results show that, compared with traditional random method, sensor nodes number is reduced effectively while coverage rate of monitor area is ensured using our coverage pattern and deterministic deployment scheme.