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This paper aims to balance the total energy consumption and the transmission delay for data gathering application in wireless sensor networks.

This paper adopts a hierarchical grid structure to reduce the total energy consumption, and utilizes a tree architecture to decrease the transmission delay.

In the results, the proposed method performs better, in terms of the number of rounds and the energy × delay cost, than other data gathering protocols with different network sizes and node densities. Moreover, the proposed method also provides good coverage preservation in different environments.

In this paper, sensor nodes are assumed to be uniformly distributed, homogenous, energy‐constrained. Each sensor node also has ability to adjust its transmission power. For practice, the proposed method needs location information of sensor nodes and the radio interference between sensor nodes during data transmissions should be considered.

The proposed method can significantly reduce the delay time and may be suitable for real‐time data gathering applications.

This paper combines hierarchical grid structure with tree architecture to minimize the energy × delay cost for data gathering application.

The accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms.

The original signal propagation‐based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately.

The NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN.

There is a need to have accurate location information of reference nodes.

This is an effective low‐cost solution for the localization of sensor nodes.

An adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.

This paper aims to propose an open service access (OSA) service capability server (SCS) architecture that supports the network capabilities to the Application Server (AS).

Based on this architecture, the service capability feature (SCF) provides the OSA Application programming interface functions by implementing the SCF service logic module and callback module. The SCF uses the XML communication module to interact with service capability (SC), which is the bearer to realize services by implementing the SC service logic module. The SC service logic interacts with mobile core network through the session initiation protocol (SIP)‐based session control module and the SIP Callback module.

The push to talk over cellular service is used to illustrate how the proposed OSA SCS interacts with the AS and the mobile core network elements.

In the design, when implementing a new service (i.e. to create a new SCS), one only needs to create the Callback module and the Service Logic modules, and other SC/SCF modules can be reused.

Through this modulized SCS design, the OSA service deployment can be speeded up.

In this paper, the design of multiple channels to achieve the goal of a high‐performance medium access control (MAC) protocol is to be proposed to solve the problem of wasting bandwidth resources due to waiting for the backoff time.

In the MAC design of this paper, a control channel and a data channel are used to improve bandwidth utilization. When the control channel waits for the backoff time, the data channel may transfer data. As a result, bandwidth utilization can be improved. In order to have better bandwidth utilization in multiple channels, the authors also propose a bandwidth allocation strategy for control channels and data channels. According to the strategy, the control and data signals can be smoothly transmitted without blocking or waiting, thereby not wasting bandwidth resources. Finally, the authors propose multiple control sub‐channels and data sub‐channels to further reduce the backoff time penalty and make more communication pairs work in a transmission range to increase the throughput.

The paper solves the following problems bandwidth waste that results from waiting for the backoff time in the single channel model and bandwidth allocation strategy for the control and data sub‐channels in the multiple channel model to achieve throughput enhancement in mobile ad‐hoc networks.

The proposed method needs the support of multiple channels.

From the result, the bandwidth allocation ratio of the proposed method performs better than other various allocation ratios. In addition, the proposed method with the bandwidth allocation strategy and multiple data and control sub‐channels results in a better throughput than IEEE 802.11 DCF by 22.3 per cent.

The proposed method using multiple control and data sub‐channels can improve the throughput and reduce bandwidth waste over IEEE 802.11 DCF.