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This research can contribute to design and develop wireless sensor network (WSN) routing algorithm based on the comparison data. The paper aims to discuss these issues.

This research is a tree-based routing algorithm in order to reduce routing cost on WSNs.

The approach is more efficient than other cluster-based approach.

Originality is high because there is no other paper about cost effect and energy saving both considering at the same time.

The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem. In mobile ad hoc network (MANET), multicast routing is a non-deterministic polynomial -complete problem that deals with the various objectives and constraints. Quality of service (QoS) in the multicast routing problem mainly depends on cost, delay, jitter and bandwidth. So the cost, delay, jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols. However, mobile node battery energy is finite and the network lifetime depends on node battery energy. If the battery power consumption is high in any one of the nodes, the chances of network’s life reduction due to path breaks are also more. On the other hand, node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time. Hence, the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem.

Recently, many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem, although it obviously is a multi-objective optimization problem. In the MOMR-DE, the network lifetime, cost, delay, jitter and bandwidth are considered as five objectives. Furthermore, three QoS constraints which are maximum allowed delay, maximum allowed jitter and minimum requested bandwidth are included. In addition, we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth, minimize cost, delay and jitter.

Two sets of experiments are conducted and compared with other algorithms for these problems. The simulation results show that our proposed method is capable of achieving faster convergence and is more preferable for multicast routing in MANET.

In MANET, most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem. However, this paper proposes a multi-objective differential evolution algorithm to resolve multicast routing problem, and the proposed algorithm is capable of achieving faster convergence and more preferable for multicast routing.

Group communication has become increasing important in mobile ad hoc networks (MANET). Current multicast routing protocols in MANET have been shown to incur large overheads due to dynamic network topology. To this end, this paper proposes an overlay multicast scheme in MANET that is more efficient.

The approach is to construct an overlay multicast tree based on the locations of the group nodes. The paper proposes several tree construction algorithms, namely, location‐guided k‐ary (LGK) tree, location‐guided directional (LGD) tree and location‐guided Steiner (LGS) tree. All of them rely on the geometric locations of the nodes as heuristics to construct the tree. The paper also introduces several mechanisms to enhance the proposed algorithms.

The simulation results show that the location‐guided heuristics is very effective in constructing low bandwidth cost overlay multicast trees in MANET. When location information is up‐to‐date, the bandwidth cost of a LGS tree is similar to that of an optimal router‐assisted Steiner multicast tree. When location information is out‐dated, LGD tree has the lowest cost.

This paper demonstrates the effectiveness of location‐guided heuristics in constructing overlay multicast trees in MANET. This study strongly suggests that location‐guided heuristics can be used to design many other algorithms in a mobile network.

A Bluetooth scatternet is a network topology that is formed by inter‐connecting piconets. A piconet is a starshaped ad‐hoc networking unit that can accommodate eight Bluetooth devices, a master and up to seven slaves. By designating certain piconet nodes as bridges, or gateways, we can interconnect piconets by forcing the bridge nodes to interleave their participation in multiple piconets. Bridge nodes form an auxiliary relay connection between adjacent piconet masters and are fundamental for establishing scatternets. In this paper we present a new fault‐tolerant approach to scatternet formation that is selfhealing and operates in a multi‐hop environment. Our Bluetooth Distributed Scatternet Formation Protocol (BTDSP) establishes a flat scatternet topology, allows incremental node arrival, and automatically heals scatternet partitions by re‐incorporating disconnected nodes. By maintaining neighbor associations in soft state, existing links can also be re‐established quickly upon disconnection due to intermittent wireless connectivity. By only using slave/slave bridges, the algorithm is resilient to both node failure and wireless interference. It also prevents time‐slot waste due to master/slave bridges being away from their piconets.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

Wireless sensor networks are expected to be an integral part of any pervasive computing environment. This implies an ever‐increasing need for efficient energy and resource management of both the sensor nodes, as well as the overall sensor network, in order to meet the expected quality of data and service requirements. There have been numerous studies that have looked at the routing of data in sensor networks with the sole intention of reducing communication power consumption. However, there has been comparatively little prior art in the area of multi‐criteria based routing that exploit both the semantics of queries and the state of sensor nodes to improve network service longevity. In this paper, we look at routing in sensor networks from this perspective and propose an adaptive multi‐criteria routing protocol. Our algorithm offers automated reconfiguration of the routing tree as demanded by variations in the network state to meet application service requirements. Our experimental results show that our approach consistently outperforms, in terms of Network Lifetime and Coverage, the leading semantic‐based routing algorithm which reconfigures the routing tree at fixed periods.

The decoupling and asynchrony properties of the content‐based publish‐subscribe paradigm makes it very appealing for dynamic wireless networks, like those that often occur in pervasive computing scenarios. Unfortunately, most of the currently available content‐based publish‐subscribe middleware do not fit the requirements of such extreme scenarios, in which the network is subject to very frequent topological reconfigurations due to mobility of nodes. In this paper we propose a protocol for content‐based message dissemination tailored to Mobile Ad Hoc Networks (MANETs) showing frequent topological changes. Message routing occurs without the support of any network‐wide dispatching infrastructure thus eliminating the need of maintaining such infrastructure on top of a physical network continuously changing its topology. The paper reports an extensive simulation study that confirms the suitability of the proposed approach along with a stochastic analysis of the central mechanism adopted by the protocol.

This paper aims to discuss problems, requirements and current trends for deploying group communication in real‐world scenarios from an integrated perspective.

The Hybrid Shared Tree is introduced – a new architecture and routing approach to combine network – and subnetwork‐layer multicast services in end‐system domains with transparent, structured overlays on the inter‐domain level.

The paper finds that The Hybrid Shared Tree solution is highly scalable and robust and offers provider‐oriented features to stimulate deployment.

A straightforward perspective is indicated in the paper for a mobility‐agnostic routing layer for future use.

Wireless sensor network (WSN) and mobile crowd sensing (MCS) technologies face some challenges, especially when deployed in a large environment such as a smart city environment. WSN faces network latency, packets delivery and limited lifetime due to the nature of the used constrained internet of things small devices and low power network. On the other hand, most of the current applications that adapt MCS technology use 3G or long term evalution network to collect data and send them directly to the server. This leads to higher battery and bandwidth consumption and higher data cost.

This paper proposes a hybrid routing protocol based on the routing protocol (RPL) protocol that combines the two wireless sensing technologies (WSN and MCS) and allows the integration between them. The aim is to use MCS nodes in an opportunistic way to support static WSN nodes to enhance the performance.

The evaluation of the proposed protocol was conducted in a static WSN to study the impact of the integration on the WSN performance. The results reveal a good enhancement on packet delivery ratio (17% more), end-to-end delay (50% less) and power consumption (25% less) compared with native RPL (without MCS integration).

The authors believe that the hybrid-RPL protocol can be useful for sensing and data collection purposes, especially in urban areas and smart city contexts.